151
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Peng X, Chen L, Zhou JP. Identification of Carcinogenic Chemicals with Network Embedding and Deep Learning Methods. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200414084317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background:
Cancer is the second leading cause of human death in the world. To date,
many factors have been confirmed to be the cause of cancer. Among them, carcinogenic chemicals
have been widely accepted as the important ones. Traditional methods for detecting carcinogenic
chemicals are of low efficiency and high cost.
Objective:
The aim of this study was to design an efficient computational method for the
identification of carcinogenic chemicals.
Methods:
A new computational model was proposed for detecting carcinogenic chemicals. As a
data-driven model, carcinogenic and non-carcinogenic chemicals were obtained from Carcinogenic
Potency Database (CPDB). These chemicals were represented by features extracted from five
chemical networks, representing five types of chemical associations, via a network embedding
method, Mashup. Obtained features were fed into a powerful deep learning method, recurrent
neural network, to build the model.
Results:
The jackknife test on such model provided the F-measure of 0.971 and AUROC of 0.971.
Conclusion:
The proposed model was quite effective and was superior to the models with
traditional machine learning algorithms, classic chemical encoding schemes or direct usage of
chemical associations.
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Affiliation(s)
- Xuefei Peng
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Jian-Peng Zhou
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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152
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Xing L, Tian S, Mi W, Zhang Y, Zhang Y, Zhang Y, Xu F, Zhang C, Lou G. PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients. Front Cell Dev Biol 2021; 8:618341. [PMID: 33585454 PMCID: PMC7876278 DOI: 10.3389/fcell.2020.618341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 12/28/2020] [Indexed: 12/21/2022] Open
Abstract
Ovarian cancer is the most frequent cause of death among gynecologic malignancies. A total of 80% of patients who have completed platinum-based chemotherapy suffer from relapse and develop resistance within 2 years. In the present study, we obtained patients' complete platinum (cisplatin and carboplatin) medication information from The Cancer Genome Atlas database and then divided them into two categories: resistance and sensitivity. Difference analysis was performed to screen differentially expressed genes (DEgenes) related to platinum response. Subsequently, we annotated DEgenes into the protein–protein interaction network as seed nodes and analyzed them by random walk. Finally, second-ranking protease serine 1 gene (PRSS1) was selected as a candidate gene for verification analysis. PRSS1's expression pattern was continuously studied in Oncomine and cBio Cancer Genomic Portal databases, revealing the key roles of PRSS1 in ovarian cancer formation. Hereafter, we conducted in-depth explorations on PRSS1's platinum response to ovarian cancer through tissue and cytological experiments. Quantitative real-time polymerase chain reaction and Western blot assay results indicated that PRSS1 expression levels in platinum-resistant samples (tissue/cell) were significantly higher than in samples sensitive to platinum. By cell transfection assay, we observed that knockdown of PRSS1 reduced the resistance of ovarian cancer cells to cisplatin. Meanwhile, overexpression of PRSS1 increased the resistance to cisplatin. In conclusion, we identified a novel risk gene PRSS1 related to ovarian cancer platinum response and confirmed its key roles using multiple levels of low-throughput experiments, revealing a new treatment strategy based on a novel target factor for overcoming cisplatin resistance in ovarian cancer.
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Affiliation(s)
- Linan Xing
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Songyu Tian
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wanqi Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongjian Zhang
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yunyan Zhang
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yuxi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Fengye Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ge Lou
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
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153
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Bai J, Wang B, Wang T, Ren W. Identification of Functional lncRNAs Associated With Ovarian Endometriosis Based on a ceRNA Network. Front Genet 2021; 12:534054. [PMID: 33584822 PMCID: PMC7873467 DOI: 10.3389/fgene.2021.534054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 01/04/2021] [Indexed: 12/03/2022] Open
Abstract
Background Endometriosis is a common gynecological disease affecting women of reproductive age; however, the mechanisms underlying this condition are not fully clear. The aim of this study was to identify functional long non-coding RNAs (lncRNAs) associated with ovarian endometriosis for potential use as biomarkers and therapeutic targets. Methods RNA-seq profiles of paired ectopic (EC) and eutopic (EU) endometrial samples from patients with ovarian endometriosis were downloaded from the publicly available Gene Expression Omnibus (GEO) database. Bioinformatics algorithms were used to construct a network of ovarian endometriosis-related competing endogenous RNAs (ceRNAs) and to detect functional lncRNAs. Results A total of 4,213 mRNAs, 1,474 lncRNAs, and 221 miRNAs were identified as being differentially expressed between EC and EU samples, and an ovarian endometriosis-related ceRNA network was constructed through analysis of these differentially expressed RNAs. H19 and GS1-358P8.4 were identified as key ovarian endometriosis-related lncRNAs through topological feature analysis, and RP11-96D1.10 was identified using a random walk with restart algorithm. Conclusion Based on bioinformatics analysis of a ceRNA network, we identified the lncRNAs H19, GS1-358P8.4, and RP11-96D1.10 as being strongly associated with ovarian endometriosis. These three lncRNAs hold potential as targets for medical therapy and as diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these lncRNAs in the pathogenesis of endometriosis.
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Affiliation(s)
- Jian Bai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wu Ren
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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154
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Sheng Y, Jiang Y, Yang Y, Li X, Qiu J, Wu J, Cheng L, Han J. CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer. Brief Bioinform 2021; 22:6076935. [PMID: 33423051 DOI: 10.1093/bib/bbaa413] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/25/2020] [Accepted: 12/15/2020] [Indexed: 12/14/2022] Open
Abstract
Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).
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Affiliation(s)
- Yuqi Sheng
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Ying Jiang
- College of Basic Medical Science, Heilongjiang University of Chinese Medicine, China
| | - Yang Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Xiangmei Li
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Jiayue Qiu
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Jiashuo Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, China
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155
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Yang K, Lu K, Wu Y, Yu J, Liu B, Zhao Y, Chen J, Zhou X. A network-based machine-learning framework to identify both functional modules and disease genes. Hum Genet 2021; 140:897-913. [PMID: 33409574 DOI: 10.1007/s00439-020-02253-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/22/2020] [Indexed: 01/20/2023]
Abstract
Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.
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Affiliation(s)
- Kuo Yang
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China.,Institute for TCM-X, MOE Key Laboratory of Bioinformatics / Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 10084, China
| | - Kezhi Lu
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China.,imec-DistriNet, KU Leuven, Leuven, 3001, Belgium
| | - Yang Wu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jian Yu
- Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jianxin Chen
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Xuezhong Zhou
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China. .,Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
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156
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iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6683051. [PMID: 33488764 PMCID: PMC7803417 DOI: 10.1155/2021/6683051] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/16/2020] [Accepted: 12/19/2020] [Indexed: 12/16/2022]
Abstract
Metabolic pathway is an important type of biological pathways. It produces essential molecules and energies to maintain the life of living organisms. Each metabolic pathway consists of a chain of chemical reactions, which always need enzymes to participate in. Thus, chemicals and enzymes are two major components for each metabolic pathway. Although several metabolic pathways have been uncovered, the metabolic pathway system is still far from complete. Some hidden chemicals or enzymes are not discovered in a certain metabolic pathway. Besides the traditional experiments to detect hidden chemicals or enzymes, an alternative pipeline is to design efficient computational methods. In this study, we proposed a powerful multilabel classifier, called iMPTCE-Hnetwork, to uniformly assign chemicals and enzymes to metabolic pathway types reported in KEGG. Such classifier adopted the embedding features derived from a heterogeneous network, which defined chemicals and enzymes as nodes and the interactions between chemicals and enzymes as edges, through a powerful network embedding algorithm, Mashup. The popular RAndom k-labELsets (RAKEL) algorithm was employed to construct the classifier, which incorporated the support vector machine (polynomial kernel) as the basic classifier. The ten-fold cross-validation results indicated that such a classifier had good performance with accuracy higher than 0.800 and exact match higher than 0.750. Several comparisons were done to indicate the superiority of the iMPTCE-Hnetwork.
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157
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Vulliard L, Menche J. Complex Networks in Health and Disease. SYSTEMS MEDICINE 2021. [PMCID: PMC7263184 DOI: 10.1016/b978-0-12-801238-3.11640-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
From protein interactions to signal transduction, from metabolism to the nervous system: Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a wholistic fashion. They offer a unique combination of a highly intuitive, qualitative description, and a plethora of analytical, quantitative tools. Here we provide a brief introduction to the emerging field of network medicine. After an overview of the core concepts for connecting network characteristics to biological functions, we review commonly used networks, ranging from the molecular interaction networks that form the basis of all biological processes in the cell to the global transportation networks that govern the spread of global epidemics. Lastly, we highlight current conceptual and practical challenges.
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158
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Lakizadeh A, Hassan Mir-Ashrafi SM. Drug repurposing improvement using a novel data integration framework based on the drug side effect. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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159
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Shang H, Liu ZP. Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:336-346. [PMID: 31095494 DOI: 10.1109/tcbb.2019.2917190] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The prevalence of diabetes mellitus has been increasing rapidly in recent years. Type 2 diabetes makes up about 90 percent cases of diabetes. The interacting mixed effects of genetics and environments build possible interpretable pathogenesis. Thus, finding the causal disease genes is crucial in its clinical diagnosis and medical treatment. Currently, network-based computational method becomes a powerful tool of systematically analyzing complex diseases, such as the identification of candidate disease genes from networks. In this paper, we propose a bioinformatics framework of prioritizing type 2 diabetes genes by leveraging the modified PageRank algorithm on bilayer biomolecular networks consisting an ensemble gene-gene regulatory network and an integrative protein-protein interaction network. We specifically weigh the networks by differential mutual information for measuring the context specificities between genes and between proteins by transcriptomic and proteomic datasets, respectively. After formulating the network into two components of known disease genes and the other normal healthy genes, we rank the diabetes genes and others by bringing the orders in the bilayer network via an improved PageRank algorithm. We conclude that these known disease genes achieve significantly higher ranks compared to these randomly-selected normal genes, and the ranks are robust and consistent in multiple validation scenarios. In functional analysis, these high-ranked genes are identified to perform relevant risks and dysfunctions of type 2 diabetes.
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160
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Pathogen and Host-Pathogen Protein Interactions Provide a Key to Identify Novel Drug Targets. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11607-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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161
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Durmaz A, Henderson TAD, Bebek G. Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2021; 26:261-272. [PMID: 33691023 PMCID: PMC7958985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Molecular mechanisms characterizing cancer development and progression are complex and process through thousands of interacting elements in the cell. Understanding the underlying structure of interactions requires the integration of cellular networks with extensive combinations of dysregulation patterns. Recent pan-cancer studies focused on identifying common dysregulation patterns in a confined set of pathways or targeting a manually curated set of genes. However, the complex nature of the disease presents a challenge for finding pathways that would constitute a basis for tumor progression and requires evaluation of subnetworks with functional interactions. Uncovering these relationships is critical for translational medicine and the identification of future therapeutics. We present a frequent subgraph mining algorithm to find functional dysregulation patterns across the cancer spectrum. We mined frequent subgraphs coupled with biased random walks utilizing genomic alterations, gene expression profiles, and protein-protein interaction networks. In this unsupervised approach, we have recovered expert-curated pathways previously reported for explaining the underlying biology of cancer progression in multiple cancer types. Furthermore, we have clustered the genes identified in the frequent subgraphs into highly connected networks using a greedy approach and evaluated biological significance through pathway enrichment analysis. Gene clusters further elaborated on the inherent heterogeneity of cancer samples by both suggesting specific mechanisms for cancer type and common dysregulation patterns across different cancer types. Survival analysis of sample level clusters also revealed significant differences among cancer types (p < 0.001). These results could extend the current understanding of disease etiology by identifying biologically relevant interactions.Supplementary Information: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/FSM_Pancancer.
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Affiliation(s)
- Arda Durmaz
- Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University, 10900 Euclid Ave., Cleveland OH 44106, USA5The Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH 44195, USA,
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162
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Liu Y, Guo Y, Liu X, Wang C, Guo M. Pathogenic gene prediction based on network embedding. Brief Bioinform 2020; 22:6053103. [PMID: 33367541 DOI: 10.1093/bib/bbaa353] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/02/2020] [Accepted: 11/03/2020] [Indexed: 11/13/2022] Open
Abstract
In disease research, the study of gene-disease correlation has always been an important topic. With the emergence of large-scale connected data sets in biology, we use known correlations between the entities, which may be from different sets, to build a biological heterogeneous network and propose a new network embedded representation algorithm to calculate the correlation between disease and genes, using the correlation score to predict pathogenic genes. Then, we conduct several experiments to compare our method to other state-of-the-art methods. The results reveal that our method achieves better performance than the traditional methods.
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Affiliation(s)
- Yang Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yuchen Guo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
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163
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Lee S, Lim S, Lee T, Sung I, Kim S. Cancer subtype classification and modeling by pathway attention and propagation. Bioinformatics 2020; 36:3818-3824. [PMID: 32207514 DOI: 10.1093/bioinformatics/btaa203] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/13/2020] [Accepted: 03/19/2020] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Biological pathway is an important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only one-third of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification. RESULTS We present an explainable deep-learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. Then, a multi-attention-based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer datasets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions. AVAILABILITY AND IMPLEMENTATION The source code is available at http://biohealth.snu.ac.kr/software/GCN_MAE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sangseon Lee
- Department of Computer Science and Engineering, Institute of Engineering Research
| | | | - Taeheon Lee
- Department of Computer Science and Engineering, Institute of Engineering Research
| | - Inyoung Sung
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Institute of Engineering Research.,Bioinformatics Institute.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea
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164
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Long Y, Wu M, Kwoh CK, Luo J, Li X. Predicting human microbe-drug associations via graph convolutional network with conditional random field. Bioinformatics 2020; 36:4918-4927. [PMID: 32597948 PMCID: PMC7559035 DOI: 10.1093/bioinformatics/btaa598] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 05/31/2020] [Accepted: 06/20/2020] [Indexed: 12/23/2022] Open
Abstract
Motivation Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task. Results In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network, and a microbe-drug interaction network. We then proposed a novel Graph Convolutional Network (GCN) based framework for predicting human Microbe-Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e., microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart (RWR) based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e., Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations. Availability Python codes and dataset are available at: https://github.com/longyahui/GCNMDA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yahui Long
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410000, China.,School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Min Wu
- Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410000, China
| | - Xiaoli Li
- Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
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165
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Ata SK, Wu M, Fang Y, Ou-Yang L, Kwoh CK, Li XL. Recent advances in network-based methods for disease gene prediction. Brief Bioinform 2020; 22:6023077. [PMID: 33276376 DOI: 10.1093/bib/bbaa303] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/29/2020] [Accepted: 10/10/2020] [Indexed: 01/28/2023] Open
Abstract
Disease-gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative and complementary low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. In this survey, we aim to provide a comprehensive and up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.
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Affiliation(s)
- Sezin Kircali Ata
- School of Computer Science and Engineering Nanyang Technological University (NTU)
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, Singapore
| | - Yuan Fang
- School of Information Systems, Singapore Management University, Singapore
| | - Le Ou-Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen China
| | | | - Xiao-Li Li
- Department head and principal scientist at I2R, A*STAR, Singapore
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166
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Dursun C. NECo: A node embedding algorithm for multiplex heterogeneous networks. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2020; 2020:146-149. [PMID: 34584774 PMCID: PMC8466723 DOI: 10.1109/bibm49941.2020.9313595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Complex diseases such as hypertension, cancer, and diabetes cause nearly 70% of the deaths in the U.S. and involve multiple genes and their interactions with environmental factors. Therefore, identification of genetic factors to understand and decrease the morbidity and mortality from complex diseases is an important and challenging task. With the generation of an unprecedented amount of multi-omics datasets, network-based methods have become popular to represent the multilayered complex molecular interactions. Particularly node embeddings, the low-dimensional representations of nodes in a network are utilized for gene function prediction. Integrated network analysis of multi-omics data alleviates the issues related to missing data and lack of context-specific datasets. Most of the node embedding methods, however, are unable to integrate multiple types of datasets from genes and phenotypes. To address this limitation, we developed a node embedding algorithm called Node Embeddings of Complex networks (NECo) that can utilize multilayered heterogeneous networks of genes and phenotypes. We evaluated the performance of NECo using genotypic and phenotypic datasets from rat (Rattus norvegicus) disease models to classify hypertension disease-related genes. Our method significantly outperformed the state-of-the-art node embedding methods, with AUC of 94.97% compared 85.98% in the second-best performer, and predicted genes not previously implicated in hypertension.
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Affiliation(s)
- Cagatay Dursun
- Dept. of Biomedical Engineering, Marquette University – Medical, College of Wisconsin, Milwaukee WI USA
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167
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Porta‐Pardo E, Valencia A, Godzik A. Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era. FEBS Lett 2020; 594:4233-4246. [PMID: 32239503 PMCID: PMC7529711 DOI: 10.1002/1873-3468.13781] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 01/23/2020] [Accepted: 02/09/2020] [Indexed: 12/12/2022]
Abstract
One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.
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Affiliation(s)
- Eduard Porta‐Pardo
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Josep Carreras Leukaemia Research Institute (IJC)BadalonaSpain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Institucio Catalana de Recerca I Estudis Avançats (ICREA)BarcelonaSpain
| | - Adam Godzik
- Division of Biomedical SciencesUniversity of California Riverside School of MedicineRiversideCAUSA
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168
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Xu X, Ramanujam M, Visvanathan S, Assassi S, Liu Z, Li L. Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods. PLoS One 2020; 15:e0242863. [PMID: 33253326 PMCID: PMC7703909 DOI: 10.1371/journal.pone.0242863] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 11/10/2020] [Indexed: 11/19/2022] Open
Abstract
Pathophysiology of systemic sclerosis (SSc, Scleroderma), an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While many of the recent trials in SSc failed to meet the primary end points that predominantly relied on changes in modified Rodnan skin scores (MRSS), sub-group analysis, especially those focused on the basal skin transcriptomic data have provided insights into patient subsets that respond to therapies. These findings suggest that deeper understanding of the molecular changes in pathways is very important to define disease drivers in various patient subgroups. In view of these challenges, we performed meta-analysis on 9 public available SSc microarray studies using a novel pathway pivoted approach combining consensus clustering and machine learning assisted feature selection. Selected pathway modules were further explored through cluster specific topological network analysis in search of novel therapeutic concepts. In addition, we went beyond previously described SSc class divisions of 3 clusters (e.g. inflammation, fibro-proliferative, normal-like) and expanded into a much finer stratification in order to profile SSc patients more accurately. Our analysis unveiled an important 80 pathway signatures that differentiated SSc patients into 8 unique subtypes. The 5 pathway modules derived from such signature successfully defined the 8 SSc subsets and were validated by in-silico cellular deconvolution analysis. Myeloid cells and fibroblasts involvement in different clusters were confirmed and linked to corresponding pathway activities. Collectively, our findings revealed more complex disease subtypes in SSc; Key gene mediators such as IL6, FGFR1, TLR7, PLCG2, IRK2 identified by network analysis underscored the scientific rationale for exploring additional targets in treatment of SSc.
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Affiliation(s)
- Xiao Xu
- Computational Biology, Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, United States of America
| | - Meera Ramanujam
- Immunology and Respiratory Diseases Research, Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, United States of America
| | - Sudha Visvanathan
- Translational Medicine and Clinical Pharmacology, Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, United States of America
| | - Shervin Assassi
- Division of Rheumatology, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Zheng Liu
- Computational Biology, Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, United States of America
| | - Li Li
- Computational Biology, Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, United States of America
- * E-mail:
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169
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Yoo S, Yang HC, Lee S, Shin J, Min S, Lee E, Song M, Lee D. A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds. Front Pharmacol 2020; 11:584875. [PMID: 33519445 PMCID: PMC7845697 DOI: 10.3389/fphar.2020.584875] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/06/2020] [Indexed: 12/25/2022] Open
Abstract
Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.
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Affiliation(s)
- Sunyong Yoo
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Hyung Chae Yang
- Department of Otorhinolaryngology-Head and Neck Surgery, Chonnam National University Medical School and Chonnam National University Hospital, Gwangju, South Korea
| | - Seongyeong Lee
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Jaewook Shin
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Seyoung Min
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Eunjoo Lee
- Big Data Steering Department, National Health Insurance Service, Wonju, South Korea
| | - Minkeun Song
- Department of Physical and Rehabilitation Medicine, Research Institute of Medical Science, Cardiovascular Research Institute, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Doheon Lee
- Bio-Synergy Research Center, Daejeon, South Korea
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
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170
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Barel G, Herwig R. NetCore: a network propagation approach using node coreness. Nucleic Acids Res 2020; 48:e98. [PMID: 32735660 PMCID: PMC7515737 DOI: 10.1093/nar/gkaa639] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/22/2020] [Accepted: 07/21/2020] [Indexed: 02/07/2023] Open
Abstract
We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.
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Affiliation(s)
- Gal Barel
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Ralf Herwig
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
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171
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Petti M, Bizzarri D, Verrienti A, Falcone R, Farina L. Connectivity Significance for Disease Gene Prioritization in an Expanding Universe. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2155-2161. [PMID: 31484130 DOI: 10.1109/tcbb.2019.2938512] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A fundamental topic in network medicine is disease genes prioritization. The underlying hypothesis is that disease genes are organized as modules confined within the interactome. Here, we propose a novel algorithm called DiaBLE (DIAMOnD Background Local Expansion) which is a modified version of DIAMOnD, a successful algorithm based on the concept of connectivity significance. Instead of taking the whole interactome as the background model, DiaBLE considers as gene universe the smallest local expansion of the current seeds set at each iteration step. We show that DiaBLE significantly increases the overall DIAMOnD ranking quality of genes prioritization both in terms of cross-validation and biological consistency. Here, we focus on the two algorithms only since a comparative analysis among gene prioritization methods is beyond the scope of this study. Finally, we briefly discuss the improvement of biological insight provided by DiaBLE for two cancers (head and neck squamous cell carcinoma and kidney renal clear cell carcinoma).
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172
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Liang H, Hu B, Chen L, Wang S, Aorigele. Recognizing novel chemicals/drugs for anatomical therapeutic chemical classes with a heat diffusion algorithm. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165910. [DOI: 10.1016/j.bbadis.2020.165910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/20/2020] [Accepted: 08/03/2020] [Indexed: 12/14/2022]
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173
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Liu R, Mancuso CA, Yannakopoulos A, Johnson KA, Krishnan A. Supervised learning is an accurate method for network-based gene classification. Bioinformatics 2020; 36:3457-3465. [PMID: 32129827 PMCID: PMC7267831 DOI: 10.1093/bioinformatics/btaa150] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/01/2019] [Accepted: 02/27/2020] [Indexed: 12/22/2022] Open
Abstract
Background Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem. Results In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene’s full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation’s appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows. Availability and implementation The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available. Contact arjun@msu.edu Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Renming Liu
- Department of Computational Mathematics, Science and Engineering
| | | | | | - Kayla A Johnson
- Department of Computational Mathematics, Science and Engineering
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Arjun Krishnan
- Department of Computational Mathematics, Science and Engineering
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
- To whom correspondence should be addressed.
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174
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Ruan P, Wang S. DiSNEP: a Disease-Specific gene Network Enhancement to improve Prioritizing candidate disease genes. Brief Bioinform 2020; 22:5925270. [PMID: 33064143 DOI: 10.1093/bib/bbaa241] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 07/25/2020] [Accepted: 08/29/2020] [Indexed: 12/27/2022] Open
Abstract
Biological network-based strategies are useful in prioritizing genes associated with diseases. Several comprehensive human gene networks such as STRING, GIANT and HumanNet were developed and used in network-assisted algorithms to identify disease-associated genes. However, none of these networks are disease-specific and may not accurately reflect gene interactions for a specific disease. Aiming to improve disease gene prioritization using networks, we propose a Disease-Specific Network Enhancement Prioritization (DiSNEP) framework. DiSNEP first enhances a comprehensive gene network specifically for a disease through a diffusion process on a gene-gene similarity matrix derived from disease omics data. The enhanced disease-specific gene network thus better reflects true gene interactions for the disease and may improve prioritizing disease-associated genes subsequently. In simulations, DiSNEP that uses an enhanced disease-specific network prioritizes more true signal genes than comparison methods using a general gene network or without prioritization. Applications to prioritize cancer-associated gene expression and DNA methylation signal genes for five cancer types from The Cancer Genome Atlas (TCGA) project suggest that more prioritized candidate genes by DiSNEP are cancer-related according to the DisGeNET database than those prioritized by the comparison methods, consistently across all five cancer types considered, and for both gene expression and DNA methylation signal genes.
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175
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Yepes S, Tucker MA, Koka H, Xiao Y, Jones K, Vogt A, Burdette L, Luo W, Zhu B, Hutchinson A, Yeager M, Hicks B, Freedman ND, Chanock SJ, Goldstein AM, Yang XR. Using whole-exome sequencing and protein interaction networks to prioritize candidate genes for germline cutaneous melanoma susceptibility. Sci Rep 2020; 10:17198. [PMID: 33057211 PMCID: PMC7560829 DOI: 10.1038/s41598-020-74293-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 09/24/2020] [Indexed: 02/07/2023] Open
Abstract
Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein-protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC, and PROSER2. The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein-protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.
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Affiliation(s)
- Sally Yepes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Margaret A Tucker
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hela Koka
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Yanzi Xiao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kristine Jones
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Aurelie Vogt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Laurie Burdette
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Wen Luo
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Amy Hutchinson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Belynda Hicks
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alisa M Goldstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
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176
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Identification of Latent Oncogenes with a Network Embedding Method and Random Forest. BIOMED RESEARCH INTERNATIONAL 2020; 2020:5160396. [PMID: 33029511 PMCID: PMC7530476 DOI: 10.1155/2020/5160396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/09/2020] [Accepted: 09/14/2020] [Indexed: 12/29/2022]
Abstract
Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers. Experimental techniques in early time are quite popular in detecting oncogenes. However, their defects become more and more evident in recent years, such as high cost and long time. The newly proposed computational methods provide an alternative way to study oncogenes, which can provide useful clues for further investigations on candidate genes. Considering the limitations of some previous computational methods, such as lack of learning procedures and terming genes as individual subjects, a novel computational method was proposed in this study. The method adopted the features derived from multiple protein networks, viewing proteins in a system level. A classic machine learning algorithm, random forest, was applied on these features to capture the essential characteristic of oncogenes, thereby building the prediction model. All genes except validated oncogenes were ranked with a measurement yielded by the prediction model. Top genes were quite different from potential oncogenes discovered by previous methods, and they can be confirmed to become novel oncogenes. It was indicated that the newly identified genes can be essential supplements for previous results.
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177
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RWR-algorithm-based dissection of microRNA-506-3p and microRNA-140-5p as radiosensitive biomarkers in colorectal cancer. Aging (Albany NY) 2020; 12:20512-20522. [PMID: 33033230 PMCID: PMC7655152 DOI: 10.18632/aging.103907] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/21/2020] [Indexed: 12/12/2022]
Abstract
Radiotherapy resistance is one of the main causes for treatment failure in colorectal cancer (CRC), and it is associated with the deregulation of certain microRNAs. In this study, we constructed the microRNA-mRNA network consisting of 2275 microRNAs and 7045 target genes, collected the known microRNAs related to CRC-radiosensitivity (CRCR) (n=18) as the seed nodes, and applied the algorithm of random walk with restart (RWR) to the network to identify novel CRCR-related microRNAs (n=263). In functional analysis, 263 novel microRNAs shared a high proportion of the same biological processes and pathways with the known microRNAs. In topological analysis of the sub-network of the 263 microRNAs and their targets, hsa-mir-506-3p and hsa-mir-140-5p were identified as network hub nodes. In plasma, radiosensitive patients had a higher expression level of hsa-mir-506-3p and hsa-mir-140-5p than radioresistant patients. In experimental validation, both hsa-mir-506-3p and hsa-mir-140-5p over-expression could obviously decrease the cell proliferation, survival rate and colonality in CRC cells after radiation. In conclusion, this study combined the novel network-based method with experimental validation, and identified two novel radiosensitive biomarkers of hsa-mir-506-3p and hsa-mir-140-5p in CRC.
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178
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Jiang S, Zhang CY, Tang L, Zhao LX, Chen HZ, Qiu Y. Integrated Genomic Analysis Revealed Associated Genes for Alzheimer's Disease in APOE4 Non-Carriers. Curr Alzheimer Res 2020; 16:753-763. [PMID: 31441725 DOI: 10.2174/1567205016666190823124724] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/14/2019] [Accepted: 08/08/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND APOE4 is the strongest genetic risk factor for late-onset Alzheimer's disease (LOAD). LOAD patients carrying or not carrying APOE4 manifest distinct clinico-pathological characteristics. APOE4 has been shown to play a critical role in the pathogenesis of AD by affecting various aspects of pathological processes. However, the pathogenesis involved in LOAD not-carrying APOE4 remains elusive. OBJECTIVE We aimed to identify the associated genes involved in LOAD not-carrying APOE4. METHODS An integrated genomic analysis of datasets of genome-wide association study, genome-wide expression profiling and genome-wide linkage scan and protein-protein interaction network construction were applied to identify associated gene clusters in APOE4 non-carriers. The role of one of hub gene of an APOE4 non-carrier-associated gene cluster in tau phosphorylation was studied by knockdown and western blot. RESULTS We identified 12 gene clusters associated with AD APOE4 non-carriers. The hub genes associated with AD in these clusters were MAPK8, POU2F1, XRCC1, PRKCG, EXOC6, VAMP4, SIRT1, MME, NOS1, ABCA1 and LDLR. The associated genes for APOE4 non-carriers were enriched in hereditary disorder, neurological disease and psychological disorders. Moreover, knockdown of PRKCG to reduce the expression of protein kinase Cγ isoform enhanced tau phosphorylation at Thr181 and Thr231 and the expression of glycogen synthase kinase 3β and cyclin-dependent kinase 5 in the presence of APOE3 but not APOE4. CONCLUSION The study provides new insight into the mechanism of distinct pathogenesis of LOAD not carrying APOE4 and prompts the functional exploration of identified genes based on APOE genotypes.
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Affiliation(s)
- Shan Jiang
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Chun-Yun Zhang
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ling Tang
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lan-Xue Zhao
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hong-Zhuan Chen
- Institute of Interdisciplinary Integrative Biomedical Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201210, China
| | - Yu Qiu
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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179
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Zhang J, Zhang M, Zhao H, Xu X. Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165794. [DOI: 10.1016/j.bbadis.2020.165794] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/25/2020] [Accepted: 04/04/2020] [Indexed: 12/11/2022]
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180
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Cui H, Zuo S, Liu Z, Liu H, Wang J, You T, Zheng Z, Zhou Y, Qian X, Yao H, Xie L, Liu T, Sham PC, Yu Y, Li MJ. The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs. SCIENCE ADVANCES 2020; 6:eabb8543. [PMID: 33055159 PMCID: PMC7556838 DOI: 10.1126/sciadv.abb8543] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 08/28/2020] [Indexed: 05/04/2023]
Abstract
Cardiovascular dysfunction is one of the most common complications of long-term cancer treatment. Growing evidence has shown that antineoplastic drugs can increase cardiovascular risk during cancer therapy, seriously affecting patient survival. However, little is known about the genetic factors associated with the cardiovascular risk of antineoplastic drugs. We established a compendium of genetic evidence that supports cardiovascular risk induced by antineoplastic drugs. Most of this genetic evidence is attributed to causal alleles altering the expression of cardiovascular disease genes. We found that antineoplastic drugs predicted to induce cardiovascular risk are significantly enriched in drugs associated with cardiovascular adverse reactions, including many first-line cancer treatments. Functional experiments validated that retinoid X receptor agonists can reduce triglyceride lipolysis, thus modulating cardiovascular risk. Our results establish a link between the causal allele of cardiovascular disease genes and the direction of pharmacological modulation, which could facilitate cancer drug discovery and clinical trial design.
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Affiliation(s)
- Hui Cui
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Key Laboratory of Food Safety Research, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute for Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Shengkai Zuo
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Zipeng Liu
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Huanhuan Liu
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jianhua Wang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Tianyi You
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Zhanye Zheng
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yao Zhou
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xinyi Qian
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hongcheng Yao
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, China
| | - Tong Liu
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Pak Chung Sham
- Centre for PanorOmic Sciences-Genomics and Bioinformatics Cores, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ying Yu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
- Key Laboratory of Food Safety Research, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute for Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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181
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Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, Chimusa ER. Computational/in silico methods in drug target and lead prediction. Brief Bioinform 2020; 21:1663-1675. [PMID: 31711157 PMCID: PMC7673338 DOI: 10.1093/bib/bbz103] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 01/10/2023] Open
Abstract
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.
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Affiliation(s)
- Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- African Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa
| | - Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
| | - Christian D Bope
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
- School of Medical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana
| | - Anita Ghansah
- Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, PO Box LG 581, Legon, Ghana
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town, Observatory 7925, South Africa
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182
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Mohammadi E, Benfeitas R, Turkez H, Boren J, Nielsen J, Uhlen M, Mardinoglu A. Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning. Cancers (Basel) 2020; 12:E2694. [PMID: 32967266 PMCID: PMC7563533 DOI: 10.3390/cancers12092694] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.
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Affiliation(s)
- Elyas Mohammadi
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Department of Animal Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691 Stockholm, Sweden;
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, 25240 Erzurum, Turkey;
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, SE-41345 Gothenburg, Sweden;
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH–Royal Institute of Technology, SE-17121 Stockholm, Sweden; (E.M.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
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183
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Zhou JP, Chen L, Guo ZH. iATC-NRAKEL: an efficient multi-label classifier for recognizing anatomical therapeutic chemical classes of drugs. Bioinformatics 2020; 36:1391-1396. [PMID: 31593226 DOI: 10.1093/bioinformatics/btz757] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/10/2019] [Accepted: 10/01/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The anatomical therapeutic chemical (ATC) classification system plays an increasingly important role in drug repositioning and discovery. The correct identification of classes in each level of such system that a given drug may belong to is an essential problem. Several multi-label classifiers have been proposed in this regard. Although they provided satisfactory performance, the feature extraction procedures were still rough. More refined features may further improve the predicted quality. RESULTS In this article, we provide a novel multi-label classifier, called iATC-NRAKEL, to predict drug ATC classes in the first level. To obtain more informative drug features, we employed the drug association information in STITCH and KEGG, which was organized by seven drug networks. The powerful network embedding algorithm, Mashup, was adopted to extract informative drug features. The obtained features were fed into the RAndom k-labELsets (RAKEL) algorithm with support vector machine as the basic classification algorithm to construct the classifier. The 10-fold cross-validation of the benchmark dataset with 3883 drugs showed that the accuracy and absolute true were 76.56 and 74.51%, respectively. The comparison results indicated that iATC-NRAKEL was much superior to all previous reported classifiers. Finally, the contribution of each network was analyzed. AVAILABILITY AND IMPLEMENTATION The codes of iATC-NRAKEL are available at https://github.com/zhou256/iATC-NRAKEL.
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Affiliation(s)
- Jian-Peng Zhou
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.,Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, People's Republic of China
| | - Zi-Han Guo
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China
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184
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Zeng X, Lin Y, He Y, Lu L, Min X, Rodriguez-Paton A. Deep Collaborative Filtering for Prediction of Disease Genes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1639-1647. [PMID: 30932845 DOI: 10.1109/tcbb.2019.2907536] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve such problems. Inductive Matrix Completion (IMC) is one of the most reliable models for its well-established framework and its superior performance in predicting gene-disease associations. However, the IMC method does not hierarchically extract deep features, which might limit the quality of recovery. In this case, the architecture of deep learning, which obtains high-level representations and handles noises and outliers presented in large-scale biological datasets, is introduced into the side information of genes in our Deep Collaborative Filtering (DCF) model. Further, for lack of negative examples, we also exploit Positive-Unlabeled (PU) learning formulation to low-rank matrix completion. Our approach achieves substantially improved performance over other state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database. Our approach is 10 percent more efficient than standard IMC in detecting a true association, and significantly outperforms other alternatives in terms of the precision-recall metric at the top-k predictions. Moreover, we also validate the disease with no previously known gene associations and newly reported OMIM associations. The experimental results show that DCF is still satisfactory for ranking novel disease phenotypes as well as mining unexplored relationships. The source code and the data are available at https://github.com/xzenglab/DCF.
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185
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Zhang X, Chen L. Prediction of membrane protein types by fusing protein-protein interaction and protein sequence information. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1868:140524. [PMID: 32858174 DOI: 10.1016/j.bbapap.2020.140524] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/17/2020] [Accepted: 07/30/2020] [Indexed: 11/30/2022]
Abstract
Membrane proteins are gatekeepers to the cell and essential for determination of the function of cells. Identification of the types of membrane proteins is an essential problem in cell biology. It is time-consuming and expensive to identify the type of membrane proteins with traditional experimental methods. The alternative way is to design effective computational methods, which can provide quick and reliable predictions. To date, several computational methods have been proposed in this regard. Several of them used the features extracted from the sequence information of individual proteins. Recently, networks are more and more popular to tackle different protein-related problems, which can organize proteins in a system level and give an overview of all proteins. However, such form weakens the essential properties of proteins, such as their sequence information. In this study, a novel feature fusion scheme was proposed, which integrated the information of protein sequences and protein-protein interaction network. The fused features of a protein were defined as the linear combination of sequence features of all proteins in the network, where the combination coefficients were the probabilities yielded by the random walk with restart algorithm with the protein as the seed node. Several models with such fused features and different classification algorithms were built and evaluated. Their performance for predicting the type of membrane proteins was improved compared with the models only with the sequence features or network information.
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Affiliation(s)
- Xiaolin Zhang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
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186
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Li Y, Burgman B, McGrail DJ, Sun M, Qi D, Shukla SA, Wu E, Capasso A, Lin SY, Wu CJ, Eckhardt SG, Mills GB, Li B, Sahni N, Yi SS. Integrated Genomic Characterization of the Human Immunome in Cancer. Cancer Res 2020; 80:4854-4867. [PMID: 32855206 DOI: 10.1158/0008-5472.can-20-0384] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 07/10/2020] [Accepted: 08/24/2020] [Indexed: 12/15/2022]
Abstract
Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output across cancer types is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in patients with cancer. To do so, we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Identified mutations were enriched in essential protein domains and genes identified by NIPPER were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted therapeutics. SIGNIFICANCE: This study demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.
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Affiliation(s)
- Yongsheng Li
- Department of Oncology, The University of Texas at Austin, Dell Medical School, Livestrong Cancer Institutes, Austin, Texas
| | - Brandon Burgman
- Department of Oncology, The University of Texas at Austin, Dell Medical School, Livestrong Cancer Institutes, Austin, Texas.,Institute for Cellular and Molecular Biology (ICMB), College of Natural Sciences, The University of Texas at Austin, Austin, Texas
| | - Daniel J McGrail
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ming Sun
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Dan Qi
- Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, Texas
| | - Sachet A Shukla
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Erxi Wu
- Department of Oncology, The University of Texas at Austin, Dell Medical School, Livestrong Cancer Institutes, Austin, Texas.,Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, Texas.,Departments of Surgery and Pharmaceutical Sciences, Texas A & M University Health Science Center, Colleges of Medicine and Pharmacy, Temple, Texas
| | - Anna Capasso
- Department of Oncology, The University of Texas at Austin, Dell Medical School, Livestrong Cancer Institutes, Austin, Texas.,Institute for Cellular and Molecular Biology (ICMB), College of Natural Sciences, The University of Texas at Austin, Austin, Texas
| | - Shiaw-Yih Lin
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Catherine J Wu
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - S Gail Eckhardt
- Department of Oncology, The University of Texas at Austin, Dell Medical School, Livestrong Cancer Institutes, Austin, Texas.,Institute for Cellular and Molecular Biology (ICMB), College of Natural Sciences, The University of Texas at Austin, Austin, Texas
| | - Gordon B Mills
- Department of Cell, Developmental and Cancer Biology, School of Medicine, Oregon Health & Science University, Portland, Oregon.,Precision Oncology, Knight Cancer Institute, Portland, Oregon
| | - Bo Li
- Lyda Hill Department of Bioinformatics, Department of Immunology, UT Southwestern Medical Center, Dallas, Texas.
| | - Nidhi Sahni
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas. .,Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, Texas.,Quantitative and Computational Biosciences Program, Baylor College of Medicine, Houston, Texas
| | - S Stephen Yi
- Department of Oncology, The University of Texas at Austin, Dell Medical School, Livestrong Cancer Institutes, Austin, Texas. .,Institute for Cellular and Molecular Biology (ICMB), College of Natural Sciences, The University of Texas at Austin, Austin, Texas.,Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, Texas.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, Texas
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187
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Yu L, Zhou D, Gao L, Zha Y. Prediction of drug response in multilayer networks based on fusion of multiomics data. Methods 2020; 192:85-92. [PMID: 32798653 DOI: 10.1016/j.ymeth.2020.08.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/22/2020] [Accepted: 08/09/2020] [Indexed: 12/14/2022] Open
Abstract
Predicting the response of each individual patient to a drug is a key issue assailing personalized medicine. Our study predicted drug response based on the fusion of multiomics data with low-dimensional feature vector representation on a multilayer network model. We named this new method DREMO (Drug Response prEdiction based on MultiOmics data fusion). DREMO fuses similarities between cell lines and similarities between drugs, thereby improving the ability to predict the response of cancer cell lines to therapeutic agents. First, a multilayer similarity network related to cell lines and drugs was constructed based on gene expression profiles, somatic mutation, copy number variation (CNV), drug chemical structures, and drug targets. Next, low-dimensional feature vector representation was used to fuse the biological information in the multilayer network. Then, a machine learning model was applied to predict new drug-cell line associations. Finally, our results were validated using the well-established GDSC/CCLE databases, literature, and the functional pathway database. Furthermore, a comparison was made between DREMO and other methods. Results of the comparison showed that DREMO improves predictive capabilities significantly.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
| | - Dandan Zhou
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Yunhong Zha
- Department of Neurology, Institute of Neural Regeneration and Repair, Three Gorges University College of Medicine, The First Hospital of Yichang, Yichang, China.
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188
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Xing L, Mi W, Zhang Y, Tian S, Zhang Y, Qi R, Lou G, Zhang C. The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis. J Cell Mol Med 2020; 24:9839-9852. [PMID: 32762026 PMCID: PMC7520306 DOI: 10.1111/jcmm.15567] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 05/31/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Ovarian cancer is the most lethal gynaecological cancer, and resistance of platinum‐based chemotherapy is the main reason for treatment failure. The aim of the present study was to identify candidate genes involved in ovarian cancer platinum response by analysing genes from homologous recombination and Fanconi anaemia pathways. Associations between these two functional genes were explored in the study, and we performed a random walk algorithm based on reconstructed gene‐gene network, including protein‐protein interaction and co‐expression relations. Following the random walk, all genes were ranked and GSEA analysis showed that the biological functions focused primarily on autophagy, histone modification and gluconeogenesis. Based on three types of seed nodes, the top two genes were utilized as examples. We selected a total of six candidate genes (FANCA, FANCG, POLD1, KDM1A, BLM and BRCA1) for subsequent verification. The validation results of the six candidate genes have significance in three independent ovarian cancer data sets with platinum‐resistant and platinum‐sensitive information. To explore the correlation between biomarkers and clinical prognostic factors, we performed differential analysis and multivariate clinical subgroup analysis for six candidate genes at both mRNA and protein levels. And each of the six candidate genes and their neighbouring genes with a mutation rate greater than 10% were also analysed by network construction and functional enrichment analysis. In the meanwhile, the survival analysis for platinum‐treated patients was performed in the current study. Finally, the RT‐qPCR assay was used to determine the performance of candidate genes in ovarian cancer platinum response. Taken together, this research demonstrated that comprehensive bioinformatics methods could help to understand the molecular mechanism of platinum response and provide new strategies for overcoming platinum resistance in ovarian cancer treatment.
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Affiliation(s)
- Linan Xing
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wanqi Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongjian Zhang
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Songyu Tian
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yunyang Zhang
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Rui Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ge Lou
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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189
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Koch E, Rosenthal B, Lundquist A, Chen CH, Kauppi K. Interactome overlap between schizophrenia and cognition. Schizophr Res 2020; 222:167-174. [PMID: 32546371 DOI: 10.1016/j.schres.2020.06.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/20/2020] [Accepted: 06/01/2020] [Indexed: 12/12/2022]
Abstract
Cognitive impairments constitute a core feature of schizophrenia, and a genetic overlap between schizophrenia and cognitive functioning in healthy individuals has been identified. However, due to the high polygenicity and complex genetic architecture of both traits, overlapping biological pathways have not yet been identified between schizophrenia and normal cognitive ability. Network medicine offers a framework to study underlying biological pathways through protein-protein interactions among risk genes. Here, established network-based methods were used to characterize the biological relatedness of schizophrenia and cognition by examining the genetic link between schizophrenia risk genes and genes associated with cognitive performance in healthy individuals, through the protein interactome. First, network separation showed a profound interactome overlap between schizophrenia risk genes and genes associated with cognitive performance (SAB = -0.22, z-score = -6.80, p = 5.38e-12). To characterize this overlap, network propagation was thereafter used to identify schizophrenia risk genes that are close to cognition-associated genes in the interactome network space (n = 140, of which 54 were part of the direct genetic overlap). Schizophrenia risk genes close to cognition were enriched for pathways including long-term potentiation and Alzheimer's disease, and included genes with a role in neurotransmitter systems important for cognitive functioning, such as glutamate and dopamine. These results pinpoint a subset of schizophrenia risk genes that are of particular interest for further examination in schizophrenia patient groups, of which some are druggable genes with potential as candidate targets for cognitive enhancing drugs.
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Affiliation(s)
- Elise Koch
- Umeå University, Department of Integrative Medical Biology, Sweden
| | - Brin Rosenthal
- University of California San Diego, Center for Computational Biology and Bioinformatics, United States of America
| | - Anders Lundquist
- Umeå University, Department of Statistics, School of Business, Economics and Statistics, Sweden
| | - Chi-Hua Chen
- University of California San Diego, Department of Radiology and Center for Multimodal Imaging and Genetics, United States of America
| | - Karolina Kauppi
- Umeå University, Department of Integrative Medical Biology, Sweden; Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Sweden.
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190
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Identification of Differentially Expressed Genes and miRNAs Associated with Esophageal Squamous Cell Carcinoma by Integrated Analysis of Microarray Data. BIOMED RESEARCH INTERNATIONAL 2020; 2020:1980921. [PMID: 32714975 PMCID: PMC7352135 DOI: 10.1155/2020/1980921] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 06/10/2020] [Indexed: 11/28/2022]
Abstract
To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.
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191
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Suresh NT, E R V, U K. Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways. Comput Biol Chem 2020; 88:107323. [PMID: 32653778 DOI: 10.1016/j.compbiolchem.2020.107323] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 12/23/2022]
Abstract
Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.
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Affiliation(s)
- Nikhila T Suresh
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India
| | - Vimina E R
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
| | - Krishnakumar U
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India
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192
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Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4256301. [PMID: 32685484 PMCID: PMC7345912 DOI: 10.1155/2020/4256301] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 06/26/2020] [Indexed: 12/15/2022]
Abstract
Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.
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193
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Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 2020; 35:5191-5198. [PMID: 31116390 DOI: 10.1093/bioinformatics/btz418] [Citation(s) in RCA: 249] [Impact Index Per Article: 62.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/22/2019] [Accepted: 05/14/2019] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. RESULTS In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide). AVAILABILITY AND IMPLEMENTATION Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. SUPPLEMENTARY INFORMATION Supplementary data are available online at Bioinformatics.
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Affiliation(s)
- Xiangxiang Zeng
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Siyi Zhu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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194
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Altuntas V, Gok M, Kahveci T. Stability Analysis of Biological Networks' Diffusion State. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1406-1418. [PMID: 30452376 DOI: 10.1109/tcbb.2018.2881887] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Computational knowledge acquired from noisy networks is not reliable and the network topology determines the reliability. Protein-protein interaction networks have uncertain topologies and noise that contain false positive and false negative edges at high rates. In this study, we analyze effects of the existing mutations in a network topology to the diffusion state of that network. To evaluate the sensitivity of the diffusion state, we derive the fitness measures based on the mathematically defined stability of a network. Searching for an influential set of edges in a network is a difficult problem. We handle the computational challenge by developing a novel metaheuristic optimization method and we find influential mutations time-efficiently. Our experiments, conducted on both synthetic and real networks from public databases, demonstrated that our method obtained better results than competitors for all types of network topologies. This is the first-time that the diffusion has been evaluated under topological mutations. Our analysis identifies significant biological results about the stability of biological - synthetic networks and diffusion state. In this manner, mutations in protein-protein interaction network topologies have a significant influence on the diffusion state of the network. Network stability is more affected by the network model than the network size.
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195
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Xiang J, Zhang NR, Zhang JS, Lv XY, Li M. PrGeFNE: Predicting disease-related genes by fast network embedding. Methods 2020; 192:3-12. [PMID: 32610158 DOI: 10.1016/j.ymeth.2020.06.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/13/2020] [Accepted: 06/22/2020] [Indexed: 12/14/2022] Open
Abstract
Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; Neuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, 410219 Hunan, China
| | - Ning-Rui Zhang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Jia-Shuai Zhang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xiao-Yi Lv
- School of Software, Xinjiang University, Urumqi 830046, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
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196
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Hristov BH, Chazelle B, Singh M. uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes. Cell Syst 2020; 10:470-479.e3. [PMID: 32684276 PMCID: PMC7821437 DOI: 10.1016/j.cels.2020.05.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/24/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022]
Abstract
Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.
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Affiliation(s)
- Borislav H Hristov
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Bernard Chazelle
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
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197
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Bean DM, Al-Chalabi A, Dobson RJB, Iacoangeli A. A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis. Genes (Basel) 2020; 11:E668. [PMID: 32575372 PMCID: PMC7349022 DOI: 10.3390/genes11060668] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/13/2020] [Accepted: 06/16/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.
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Affiliation(s)
- Daniel M. Bean
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
| | - Ammar Al-Chalabi
- King′s College Hospital, Bessemer Road, Denmark Hill, Brixton, London SE5 9RS, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
| | - Richard J. B. Dobson
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, UK
| | - Alfredo Iacoangeli
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
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198
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Yang J, Tian S, Zhao J, Zhang W. Exploring the mechanism of TCM formulae in the treatment of different types of coronary heart disease by network pharmacology and machining learning. Pharmacol Res 2020; 159:105034. [PMID: 32565312 DOI: 10.1016/j.phrs.2020.105034] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/15/2020] [Accepted: 06/15/2020] [Indexed: 12/14/2022]
Abstract
Traditional Chinese medicine (TCM) has long been used in the clinical treatment of coronary heart disease (CHD). TCM is characterized by syndrome-based medication, which is, using different TCM formulae for different syndromes. However, the underlying mode of action remains unclear. In this work, we utilized network pharmacology and machine learning to explore the mechanism of eight classic TCM formulae in the treatment of different types of CHD. First, by integrating multiple databases, a total of 669 potential bioactive compounds and 581 targets of the eight formulae were screened. Then, the effectiveness of these formulae on CHD was evaluated using two network-based indicators. The results showed that each formula's targets were significantly correlated with CHD associated genes and overlapped with the targets of 9 classes of drugs for cardio vascular diseases (CVD) to some degree. Next, from 5 different levels, i.e., herb, symptom, compound, target, and pathway level, we systematically compared the eight formulae using network clustering and hierarchical clustering. We found that all the formulae could be grouped into five clusters and the clustering results were approximately consistent at different levels. All the formulae were involved in 7 pathways closely related to CHD and may exhibit the common effect of relieving angina. Formulae in the same group collectively regulated some unique pathways and suggest further specific indications. For example, the three formulae used for Qi stagnation and blood stasis, Qi deficiency and blood stasis, and Qi-Yin deficiency syndromes acted on two special pathways (TNF signaling pathway, NF-kappa B signaling pathway) and may exert anti-inflammatory and immune-enhancing effects; the two formulae for Yin deficiency of heart and kidney, and Yang deficiency of heart and kidney syndromes regulated two special pathways (PPAR signaling pathway, thyroid hormone signaling pathway) in endocrine system and could improve renal function. Subsequently, we designed a rank algorithm, which integrated network topology with biological function, to identify important targets of these formulae. The results were consistent with the multi-level clustering results. At last, our literature mining validated about 20 % putative targets, as well as clustering results and effects of the formulae by experimental evidences. This study explained the medication patterns and scientific significance of TCM formulae on different types of CHD from perspective of systems biology. It may facilitate the understanding of different types of CHD described by traditional Chinese medicine from the perspectives of modern biology.
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Affiliation(s)
- Jian Yang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Saisai Tian
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China
| | - Jing Zhao
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Weidong Zhang
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China; Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
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199
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Kerr CH, Skinnider MA, Andrews DDT, Madero AM, Chan QWT, Stacey RG, Stoynov N, Jan E, Foster LJ. Dynamic rewiring of the human interactome by interferon signaling. Genome Biol 2020; 21:140. [PMID: 32539747 PMCID: PMC7294662 DOI: 10.1186/s13059-020-02050-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network. RESULTS We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis. CONCLUSIONS Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.
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Affiliation(s)
- Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
- Current Address: Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Daniel D T Andrews
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Angel M Madero
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Eric Jan
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
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200
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Hoang NV, Choe G, Zheng Y, Aliaga Fandino AC, Sung I, Hur J, Kamran M, Park C, Kim H, Ahn H, Kim S, Fei Z, Lee JY. Identification of Conserved Gene-Regulatory Networks that Integrate Environmental Sensing and Growth in the Root Cambium. Curr Biol 2020; 30:2887-2900.e7. [PMID: 32531282 DOI: 10.1016/j.cub.2020.05.046] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/09/2020] [Accepted: 05/13/2020] [Indexed: 02/07/2023]
Abstract
Cambium drives the lateral growth of stems and roots, contributing to diverse plant growth forms. The root crop is one of the outstanding examples of the cambium-driven growth. To understand its molecular basis, we used radish to generate a compendium of root-tissue- and stage-specific transcriptomes from two contrasting inbred lines during root growth. Expression patterns of key cambium regulators and hormone signaling components were validated. Clustering and gene ontology (GO) enrichment analyses of radish datasets followed by a comparative analysis against the newly established Arabidopsis early cambium data revealed evolutionary conserved stress-response transcription factors that may intimately control the cambium. Indeed, an in vivo network consisting of selected stress-response and cambium regulators indicated ERF-1 as a potential key checkpoint of cambial activities, explaining how cambium-driven growth is altered in response to environmental changes. The findings here provide valuable information about dynamic gene expression changes during cambium-driven root growth and have implications with regard to future engineering schemes, leading to better crop yields.
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Affiliation(s)
- Nam V Hoang
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Goh Choe
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Yi Zheng
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | | | - Inyoung Sung
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Jaeryung Hur
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Muhammad Kamran
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Chulmin Park
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Hyoujin Kim
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Hongryul Ahn
- Bioinformatics Institute, Seoul National University, Gwanak-ro, Seoul 08826, Korea; Division of Data Science, University of Suwon, Hwaseong, Gyeonggi-do 18323, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro, Seoul 08826, Korea; Bioinformatics Institute, Seoul National University, Gwanak-ro, Seoul 08826, Korea; Department of Computer Science and Engineering, Seoul National University, Gwanak-ro, Seoul 08826, Korea
| | - Zhangjun Fei
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | - Ji-Young Lee
- School of Biological Sciences, Seoul National University, Gwanak-ro, Seoul 08826, Korea; Plant Genomics and Breeding Institute, Seoul National University, Gwanak-ro, Seoul 08826, Korea.
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