1
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Wang M, Li F, Wu H, Liu Q, Li S. PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest. Interdiscip Sci 2022; 14:697-711. [PMID: 35488998 DOI: 10.1007/s12539-022-00520-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 12/12/2022]
Abstract
Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.
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Affiliation(s)
- Miao Wang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC, 3000, Australia
| | - Hao Wu
- School of Software, Shandong University, Jinan, 250100, Shandong, China
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China.
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China.
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2
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Ai C, Yang H, Ding Y, Tang J, Guo F. A multi-layer multi-kernel neural network for determining associations between non-coding RNAs and diseases. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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3
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Guo Y, Cheng H, Yuan Z, Liang Z, Wang Y, Du D. Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies. Front Genet 2021; 12:801261. [PMID: 34956337 PMCID: PMC8693929 DOI: 10.3389/fgene.2021.801261] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.
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Affiliation(s)
- Yingjie Guo
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Honghong Cheng
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Zhian Yuan
- Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
| | - Zhen Liang
- School of Life Science, Shanxi University, Taiyuan, China
| | - Yang Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Debing Du
- Beidahuang Industry Group General Hospital, Harbin, China
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4
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Le DH. A network-based method for predicting disease-associated enhancers. PLoS One 2021; 16:e0260432. [PMID: 34879086 PMCID: PMC8654176 DOI: 10.1371/journal.pone.0260432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/09/2021] [Indexed: 11/18/2022] Open
Abstract
Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.
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Affiliation(s)
- Duc-Hau Le
- School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam
- * E-mail:
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RFLMDA: A Novel Reinforcement Learning-Based Computational Model for Human MicroRNA-Disease Association Prediction. Biomolecules 2021; 11:biom11121835. [PMID: 34944479 PMCID: PMC8699433 DOI: 10.3390/biom11121835] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
Numerous studies have confirmed that microRNAs play a crucial role in the research of complex human diseases. Identifying the relationship between miRNAs and diseases is important for improving the treatment of complex diseases. However, traditional biological experiments are not without restrictions. It is an urgent necessity for computational simulation to predict unknown miRNA-disease associations. In this work, we combine Q-learning algorithm of reinforcement learning to propose a RFLMDA model, three submodels CMF, NRLMF, and LapRLS are fused via Q-learning algorithm to obtain the optimal weight S. The performance of RFLMDA was evaluated through five-fold cross-validation and local validation. As a result, the optimal weight is obtained as S (0.1735, 0.2913, 0.5352), and the AUC is 0.9416. By comparing the experiments with other methods, it is proved that RFLMDA model has better performance. For better validate the predictive performance of RFLMDA, we use eight diseases for local verification and carry out case study on three common human diseases. Consequently, all the top 50 miRNAs related to Colorectal Neoplasms and Breast Neoplasms have been confirmed. Among the top 50 miRNAs related to Colon Neoplasms, Gastric Neoplasms, Pancreatic Neoplasms, Kidney Neoplasms, Esophageal Neoplasms, and Lymphoma, we confirm 47, 41, 49, 46, 46 and 48 miRNAs respectively.
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Zhang J, Zhang Z, Pu L, Tang J, Guo F. AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1831-1840. [PMID: 31985437 DOI: 10.1109/tcbb.2020.2968419] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://github.com/guofei-tju/Ensemble-classifier-chain-model.
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Yang H, Ding Y, Tang J, Guo F. Identifying potential association on gene-disease network via dual hypergraph regularized least squares. BMC Genomics 2021; 22:605. [PMID: 34372777 PMCID: PMC8351363 DOI: 10.1186/s12864-021-07864-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/29/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. RESULTS In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. CONCLUSION Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.
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Affiliation(s)
- Hongpeng Yang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Yijie Ding
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China.
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China.
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Dai Q, Chu Y, Li Z, Zhao Y, Mao X, Wang Y, Xiong Y, Wei DQ. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Comput Biol Med 2021; 136:104706. [PMID: 34371319 DOI: 10.1016/j.compbiomed.2021.104706] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 01/17/2023]
Abstract
MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. In this study, we developed MDA-CF to identify underlying miRNA-disease associations based on a cascade forest model. In this method, multi-source information was integrated to represent miRNAs and diseases comprehensively, and the autoencoder was utilized for dimension reduction to obtain the optimal feature space. The cascade forest model was then employed for miRNA-disease association prediction. As a result, the average AUC of MDA-CF was 0.9464 on HMDD v3.2 in five-fold cross-validation. Compared with previous computational methods, MDA-CF performed better on HMDD v2.0 with an average AUC of 0.9258. Moreover, MDA-CF was implemented to investigate colon neoplasm, breast neoplasm, and gastric neoplasm, and 100%, 86%, 88% of the top 50 potential miRNAs were validated by authoritative databases. In conclusion, MDA-CF appears to be a reliable method to uncover disease-associated miRNAs. The source code of MDA-CF is available at https://github.com/a1622108/MDA-CF.
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Affiliation(s)
- Qiuying Dai
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhiqi Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yusong Zhao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China.
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9
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Qian Y, Jiang L, Ding Y, Tang J, Guo F. A sequence-based multiple kernel model for identifying DNA-binding proteins. BMC Bioinformatics 2021; 22:291. [PMID: 34058979 PMCID: PMC8167993 DOI: 10.1186/s12859-020-03875-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 11/13/2020] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. RESULTS In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([Formula: see text]) and PDB186 ([Formula: see text]), respectively. CONCLUSION Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.
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Affiliation(s)
- Yuqing Qian
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, People's Republic of China
| | - Limin Jiang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, People's Republic of China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, People's Republic of China.
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, People's Republic of China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, People's Republic of China.
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10
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Zeng R, Cheng S, Liao M. 4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism. Front Cell Dev Biol 2021; 9:664669. [PMID: 34041243 PMCID: PMC8141656 DOI: 10.3389/fcell.2021.664669] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 03/17/2021] [Indexed: 01/10/2023] Open
Abstract
DNA methylation is one of the most extensive epigenetic modifications. DNA 4mC modification plays a key role in regulating chromatin structure and gene expression. In this study, we proposed a generic 4mC computational predictor, namely, 4mCPred-MTL using multi-task learning coupled with Transformer to predict 4mC sites in multiple species. In this predictor, we utilize a multi-task learning framework, in which each task is to train species-specific data based on Transformer. Extensive experimental results show that our multi-task predictive model can significantly improve the performance of the model based on single task and outperform existing methods on benchmarking comparison. Moreover, we found that our model can sufficiently capture better characteristics of 4mC sites as compared to existing commonly used feature descriptors, demonstrating the strong feature learning ability of our model. Therefore, based on the above results, it can be expected that our 4mCPred-MTL can be a useful tool for research communities of interest.
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Affiliation(s)
- Rao Zeng
- Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China
| | - Song Cheng
- Department of Thoracic Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Minghong Liao
- Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China
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11
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Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021; 22:6261915. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Xuhong Wang
- School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, China
| | - Qiuying Dai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, China
| | | | | | - Dennis Russell Salahub
- Department of Chemistry, University of Calgary, Fellow Royal Society of Canada and Fellow of the American Association for the Advancement of Science, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
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12
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Zou Y, Wu H, Guo X, Peng L, Ding Y, Tang J, Guo F. MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200607173829] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive.
Objective:
In recent years, the rise of computational biology methods based on Machine Learning
(ML) has greatly improved the detection efficiency of DBPs.
Method:
In this study, the Multiple Kernel-based Fuzzy SVM Model with Support Vector Data
Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted
from the protein sequence. Secondly, multiple kernels are constructed via these sequence features.
Then, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel
Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with
Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs.
Results:
Our model is evaluated on several benchmark datasets. Compared with other methods, MKFSVM-
SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and
PDB2272 (0.5476).
Conclusion:
We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the
classifier for DNA-binding proteins identification.
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Affiliation(s)
- Yi Zou
- School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, No. 1 Kerui Road, 215009, Suzhou, China
| | - Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Li Peng
- School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, No. 1 Kerui Road, 215009, Suzhou, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
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13
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Guo X, Zhou W, Shi B, Wang X, Du A, Ding Y, Tang J, Guo F. An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200614172536] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Dry Weight (DW) is the lowest weight after dialysis, and patients with
lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches
have been presented to assess the dry weight of hemodialysis patients. However, these traditional
methods all depend on special instruments and professional technicians.
Objective:
In order to avoid this limitation, we need to find a machine-independent way to assess dry
weight, therefore we collected some clinical influencing characteristic data and constructed a
Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients.
Methods::
In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements,
and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body
Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were
closely related to their dry weight. All these relevant data were used to enter the regression equation.
Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS)
model was proposed to predict the dry weight of hemodialysis patients.
Result:
The experimental results show that dry weight is positively correlated with BMI and HR.
And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively
correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was
1.3817.
Conclusion:
Our proposed model could serve as a viable alternative for dry weight estimation of
hemodialysis patients, thus providing a new way for clinical practice. Our proposed model could serve as a viable alternative of dry weight estimation for hemodialysis patients,
thus providing a new way for the clinic.
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Affiliation(s)
- Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Wei Zhou
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Bin Shi
- Hemodialysis Center, Northern Jiangsu People's Hospital, 225001, Yangzhou, China
| | - Xiaohua Wang
- Department of Urology, the First Affiliated Hospital of Soochow University, 215006, Suzhou, China
| | - Aiyan Du
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
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14
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Yang X, Ye X, Li X, Wei L. iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool. Front Genet 2021; 12:663572. [PMID: 33868390 PMCID: PMC8044371 DOI: 10.3389/fgene.2021.663572] [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: 02/03/2021] [Accepted: 03/02/2021] [Indexed: 02/04/2023] Open
Abstract
Motivation DNA N4-methylcytosine (4mC) and N6-methyladenine (6mA) are two important DNA modifications and play crucial roles in a variety of biological processes. Accurate identification of the modifications is essential to better understand their biological functions and mechanisms. However, existing methods to identify 4mA or 6mC sites are all single tasks, which demonstrates that they can identify only a certain modification in one species. Therefore, it is desirable to develop a novel computational method to identify the modification sites in multiple species simultaneously. Results In this study, we proposed a computational method, called iDNA-MT, to identify 4mC sites and 6mA sites in multiple species, respectively. The proposed iDNA-MT mainly employed multi-task learning coupled with the bidirectional gated recurrent units (BGRU) to capture the sharing information among different species directly from DNA primary sequences. Experimental comparative results on two benchmark datasets, containing different species respectively, show that either for identifying 4mA or for 6mC site in multiple species, the proposed iDNA-MT outperforms other state-of-the-art single-task methods. The promising results have demonstrated that iDNA-MT has great potential to be a powerful and practically useful tool to accurately identify DNA modifications.
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Affiliation(s)
- Xiao Yang
- School of Software, Shandong University, Jinan, China
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Xuehong Li
- Department of Rehabilitation, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Lesong Wei
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
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15
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Chen Z, Shen Z, Zhang Z, Zhao D, Xu L, Zhang L. RNA-Associated Co-expression Network Identifies Novel Biomarkers for Digestive System Cancer. Front Genet 2021; 12:659788. [PMID: 33841514 PMCID: PMC8033200 DOI: 10.3389/fgene.2021.659788] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 02/25/2021] [Indexed: 01/04/2023] Open
Abstract
Cancers of the digestive system are malignant diseases. Our study focused on colon cancer, esophageal cancer (ESCC), rectal cancer, gastric cancer (GC), and rectosigmoid junction cancer to identify possible biomarkers for these diseases. The transcriptome data were downloaded from the TCGA database (The Cancer Genome Atlas Program), and a network was constructed using the WGCNA algorithm. Two significant modules were found, and coexpression networks were constructed. CytoHubba was used to identify hub genes of the two networks. GO analysis suggested that the network genes were involved in metabolic processes, biological regulation, and membrane and protein binding. KEGG analysis indicated that the significant pathways were the calcium signaling pathway, fatty acid biosynthesis, and pathways in cancer and insulin resistance. Some of the most significant hub genes were hsa-let-7b-3p, hsa-miR-378a-5p, hsa-miR-26a-5p, hsa-miR-382-5p, and hsa-miR-29b-2-5p and SECISBP2 L, NCOA1, HERC1, HIPK3, and MBNL1, respectively. These genes were predicted to be associated with the tumor prognostic reference for this patient population.
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Affiliation(s)
- Zheng Chen
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Zijie Shen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Zilong Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Da Zhao
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lijun Zhang
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
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16
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Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L 2,1-Norm. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6627650. [PMID: 33628794 PMCID: PMC7880720 DOI: 10.1155/2021/6627650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 11/28/2022]
Abstract
Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient's dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.
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17
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Wang H, Tang J, Ding Y, Guo F. Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment. Brief Bioinform 2021; 22:6095847. [PMID: 33443536 DOI: 10.1093/bib/bbaa409] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/05/2020] [Accepted: 12/11/2020] [Indexed: 12/25/2022] Open
Abstract
Relationship of accurate associations between non-coding RNAs and diseases could be of great help in the treatment of human biomedical research. However, the traditional technology is only applied on one type of non-coding RNA or a specific disease, and the experimental method is time-consuming and expensive. More computational tools have been proposed to detect new associations based on known ncRNA and disease information. Due to the ncRNAs (circRNAs, miRNAs and lncRNAs) having a close relationship with the progression of various human diseases, it is critical for developing effective computational predictors for ncRNA-disease association prediction. In this paper, we propose a new computational method of three-matrix factorization with hypergraph regularization terms (HGRTMF) based on central kernel alignment (CKA), for identifying general ncRNA-disease associations. In the process of constructing the similarity matrix, various types of similarity matrices are applicable to circRNAs, miRNAs and lncRNAs. Our method achieves excellent performance on five datasets, involving three types of ncRNAs. In the test, we obtain best area under the curve scores of $0.9832$, $0.9775$, $0.9023$, $0.8809$ and $0.9185$ via 5-fold cross-validation and $0.9832$, $0.9836$, $0.9198$, $0.9459$ and $0.9275$ via leave-one-out cross-validation on five datasets. Furthermore, our novel method (CKA-HGRTMF) is also able to discover new associations between ncRNAs and diseases accurately. Availability: Codes and data are available: https://github.com/hzwh6910/ncRNA2Disease.git. Contact: fguo@tju.edu.cn.
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Affiliation(s)
- Hao Wang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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18
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Zhang Z, Ding J, Xu J, Tang J, Guo F. Multi-Scale Time-Series Kernel-Based Learning Method for Brain Disease Diagnosis. IEEE J Biomed Health Inform 2021; 25:209-217. [PMID: 32248130 DOI: 10.1109/jbhi.2020.2983456] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The functional magnetic resonance imaging (fMRI) is a noninvasive technique for studying brain activity, such as brain network analysis, neural disease automated diagnosis and so on. However, many existing methods have some drawbacks, such as limitations of graph theory, lack of global topology characteristic, local sensitivity of functional connectivity, and absence of temporal or context information. In addition to many numerical features, fMRI time series data also cover specific contextual knowledge and global fluctuation information. Here, we propose multi-scale time-series kernel-based learning model for brain disease diagnosis, based on Jensen-Shannon divergence. First, we calculate correlation value within and between brain regions over time. In addition, we extract multi-scale synergy expression probability distribution (interactional relation) between brain regions. Also, we produce state transition probability distribution (sequential relation) on single brain regions. Then, we build time-series kernel-based learning model based on Jensen-Shannon divergence to measure similarity of brain functional connectivity. Finally, we provide an efficient system to deal with brain network analysis and neural disease automated diagnosis. On Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our proposed method achieves accuracy of 0.8994 and AUC of 0.8623. On Major Depressive Disorder (MDD) dataset, our proposed method achieves accuracy of 0.9166 and AUC of 0.9263. Experiments show that our proposed method outperforms other existing excellent neural disease automated diagnosis approaches. It shows that our novel prediction method performs great accurate for identification of brain diseases as well as existing outstanding prediction tools.
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19
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Ding Y, Jiang L, Tang J, Guo F. Identification of human microRNA-disease association via hypergraph embedded bipartite local model. Comput Biol Chem 2020; 89:107369. [DOI: 10.1016/j.compbiolchem.2020.107369] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/03/2020] [Accepted: 08/31/2020] [Indexed: 12/16/2022]
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20
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Li Y, Zhang Z, Teng Z, Liu X. PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8845133. [PMID: 33294004 PMCID: PMC7700051 DOI: 10.1155/2020/8845133] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/06/2020] [Accepted: 10/31/2020] [Indexed: 01/20/2023]
Abstract
Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.
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Affiliation(s)
- Yanjuan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zitong Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Xiaoyan Liu
- College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150040, China
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21
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Li S, Jiang L, Tang J, Gao N, Guo F. Kernel Fusion Method for Detecting Cancer Subtypes via Selecting Relevant Expression Data. Front Genet 2020; 11:979. [PMID: 33133130 PMCID: PMC7511763 DOI: 10.3389/fgene.2020.00979] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 08/03/2020] [Indexed: 12/19/2022] Open
Abstract
Recently, cancer has been characterized as a heterogeneous disease composed of many different subtypes. Early diagnosis of cancer subtypes is an important study of cancer research, which can be of tremendous help to patients after treatment. In this paper, we first extract a novel dataset, which contains gene expression, miRNA expression, and isoform expression of five cancers from The Cancer Genome Atlas (TCGA). Next, to avoid the effect of noise existing in 60, 483 genes, we select a small number of genes by using LASSO that employs gene expression and survival time of patients. Then, we construct one similarity kernel for each expression data by using Chebyshev distance. And also, We used SKF to fused the three similarity matrix composed of gene, Iso, and miRNA, and finally clustered the fused similarity matrix with spectral clustering. In the experimental results, our method has better P-value in the Cox model than other methods on 10 cancer data from Jiang Dataset and Novel Dataset. We have drawn different survival curves for different cancers and found that some genes play a key role in cancer. For breast cancer, we find out that HSPA2A, RNASE1, CLIC6, and IFITM1 are highly expressed in some specific groups. For lung cancer, we ensure that C4BPA, SESN3, and IRS1 are highly expressed in some specific groups. The code and all supporting data files are available from https://github.com/guofei-tju/Uncovering-Cancer-Subtypes-via-LASSO.
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Affiliation(s)
- Shuhao Li
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States
| | - Nan Gao
- School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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22
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Zhuang H, Zhang Y, Yang S, Cheng L, Liu SL. A Mendelian Randomization Study on Infant Length and Type 2 Diabetes Mellitus Risk. Curr Gene Ther 2020; 19:224-231. [PMID: 31553296 DOI: 10.2174/1566523219666190925115535] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/15/2019] [Accepted: 06/16/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Infant length (IL) is a positively associated phenotype of type 2 diabetes mellitus (T2DM), but the causal relationship of which is still unclear. Here, we applied a Mendelian randomization (MR) study to explore the causal relationship between IL and T2DM, which has the potential to provide guidance for assessing T2DM activity and T2DM- prevention in young at-risk populations. MATERIALS AND METHODS To classify the study, a two-sample MR, using genetic instrumental variables (IVs) to explore the causal effect was applied to test the influence of IL on the risk of T2DM. In this study, MR was carried out on GWAS data using 8 independent IL SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated by the inverse-variance weighted method for the assessment of the risk the shorter IL brings to T2DM. Sensitivity validation was conducted to identify the effect of individual SNPs. MR-Egger regression was used to detect pleiotropic bias of IVs. RESULTS The pooled odds ratio from the IVW method was 1.03 (95% CI 0.89-1.18, P = 0.0785), low intercept was -0.477, P = 0.252, and small fluctuation of ORs ranged from -0.062 ((0.966 - 1.03) / 1.03) to 0.05 ((1.081 - 1.03) / 1.03) in leave-one-out validation. CONCLUSION We validated that the shorter IL causes no additional risk to T2DM. The sensitivity analysis and the MR-Egger regression analysis also provided adequate evidence that the above result was not due to any heterogeneity or pleiotropic effect of IVs.
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Affiliation(s)
- He Zhuang
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine- Pharmaceutics of China), Harbin Medical University, Harbin, China.,HMU-UCFM Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, 150001, Harbin, China
| | - Shuo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shu-Lin Liu
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine- Pharmaceutics of China), Harbin Medical University, Harbin, China.,HMU-UCFM Centre for Infection and Genomics, Harbin Medical University, Harbin, China.,Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Canada.,Department of Infectious Diseases, The First Affiliated Hospital, Harbin Medical University, Harbin, China.,Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China
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23
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A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4675395. [PMID: 32596314 PMCID: PMC7275954 DOI: 10.1155/2020/4675395] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 04/08/2020] [Indexed: 01/01/2023]
Abstract
All drugs usually have side effects, which endanger the health of patients. To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming. So, computation-based methods have been developed to accurately and quickly predict side effects. To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model. TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA). LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data. To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively. Compared with other methods, our model achieves better performance on three benchmark datasets. The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.
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24
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Wang C, Zhang Y, Han S. Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification. BIOMED RESEARCH INTERNATIONAL 2020; 2020:2468789. [PMID: 32566672 PMCID: PMC7275950 DOI: 10.1155/2020/2468789] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/20/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022]
Abstract
Fungi play essential roles in many ecological processes, and taxonomic classification is fundamental for microbial community characterization and vital for the study and preservation of fungal biodiversity. To cope with massive fungal barcode data, tools that can implement extensive volumes of barcode sequences, especially the internal transcribed spacer (ITS) region, are necessary. However, high variation in the ITS region and computational requirements for processing high-dimensional features remain challenging for existing predictors. In this study, we developed Its2vec, a bioinformatics tool for the classification of fungal ITS barcodes to the species level. An ITS database covering more than 25,000 species in a broad range of fungal taxa was assembled. For dimensionality reduction, a word embedding algorithm was used to represent an ITS sequence as a dense low-dimensional vector. A random forest-based classifier was built for species identification. Benchmarking results showed that our model achieved an accuracy comparable to that of several state-of-the-art predictors, and more importantly, it could implement large datasets and greatly reduce dimensionality. We expect the Its2vec model to be helpful for fungal species identification and, thus, for revealing microbial community structures and in deepening our understanding of their functional mechanisms.
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Affiliation(s)
- Chao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin 150088, China
| | - Shuguang Han
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 60054, China
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25
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Feng C, Ma Z, Yang D, Li X, Zhang J, Li Y. A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features. Front Bioeng Biotechnol 2020; 8:285. [PMID: 32432088 PMCID: PMC7214540 DOI: 10.3389/fbioe.2020.00285] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/18/2020] [Indexed: 11/13/2022] Open
Abstract
The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.
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Affiliation(s)
- Changli Feng
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Zhaogui Ma
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Deyun Yang
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Xin Li
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Jun Zhang
- Department of Rehabilitation, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yanjuan Li
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
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26
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Sun S, Wang C, Ding H, Zou Q. Machine learning and its applications in plant molecular studies. Brief Funct Genomics 2019; 19:40-48. [DOI: 10.1093/bfgp/elz036] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/06/2019] [Accepted: 09/15/2019] [Indexed: 01/16/2023] Open
Abstract
Abstract
The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies.
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Affiliation(s)
- Shanwen Sun
- University of Bayreuth in Germany. He is now a postdoctoral fellow at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
| | - Chunyu Wang
- Harbin Institute of Technology in China. He is an associate professor in the School of Computer Science and Technology, Harbin Institute of Technology
| | - Hui Ding
- Inner Mongolia University in China. She is an associate professor in the Center for Informational Biology, University of Electronic Science and Technology of China
| | - Quan Zou
- Harbin Institute of Technology in China. He is a professor in the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China
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27
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Ru X, Cao P, Li L, Zou Q. Selecting Essential MicroRNAs Using a Novel Voting Method. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:16-23. [PMID: 31479921 PMCID: PMC6727015 DOI: 10.1016/j.omtn.2019.07.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 06/20/2019] [Accepted: 07/08/2019] [Indexed: 02/06/2023]
Abstract
Among the large number of known microRNAs (miRNAs), some miRNAs play negligible roles in cell regulation. Therefore, selecting essential miRNAs is an important initial step for a deeper understanding of miRNAs and their functions. In this study, we generated 60 classification models by combining 12 representative feature extraction methods and 5 commonly used classification algorithms. The optimal model for essential miRNA classification that we obtained is based on the Mismatch feature extraction method combined with the random forest algorithm. The F-Measure, area under the curve, and accuracy values of this model were 93.2%, 96.7%, and 93.0%, respectively. We also found that the distribution of the positive and negative examples of the first few features greatly influenced the classification results. The feature extraction methods performed best when the differences between the positive and negative examples were obvious, and this led to better classification of essential miRNAs. Because each classifier's predictions for the same sample may be different, we employed a novel voting method to improve the accuracy of the classification of essential miRNAs. The performance results showed that the best classification results were obtained when five classification models were used in the voting. The five classification models were constructed based on the Mismatch, pseudo-distance structure status pair composition, Subsequence, Kmer, and Triplet feature extraction methods. The voting result was 95.3%. Our results suggest that the voting method can be an important tool for selecting essential miRNAs.
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Affiliation(s)
- Xiaoqing Ru
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China
| | - Peigang Cao
- Department of Cardiology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Lihong Li
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
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28
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Zhong W, Zhong B, Zhang H, Chen Z, Chen Y. Identification of Anti-cancer Peptides Based on Multi-classifier System. Comb Chem High Throughput Screen 2019; 22:694-704. [PMID: 31793417 DOI: 10.2174/1386207322666191203141102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 07/18/2019] [Accepted: 07/30/2019] [Indexed: 01/01/2023]
Abstract
AIMS AND OBJECTIVE Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti-cancer peptides through experiments take a lot of time and money, therefore, it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are a good choice. MATERIALS AND METHODS In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. RESULTS AND CONCLUSION The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.
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Affiliation(s)
- Wanben Zhong
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Bineng Zhong
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China.,Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Hongbo Zhang
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Ziyi Chen
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
| | - Yan Chen
- School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, China
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Zhao T, Wang D, Hu Y, Zhang N, Zang T, Wang Y. Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering. Curr Gene Ther 2019; 19:216-223. [DOI: 10.2174/1566523219666190924113737] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/05/2019] [Accepted: 06/12/2019] [Indexed: 01/14/2023]
Abstract
Background:
More and more scholars are trying to use it as a specific biomarker for Alzheimer’s
Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that
miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early
events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of
AD, and may also be involved in the disease through some specific molecular mechanisms.
Objective:
Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early
diagnosis.
Materials and Methods:
We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein
interaction network is used to find more AD-related genes by known AD-related genes. Then,
each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each
miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not
generate negative samples randomly with using classification method to identify AD-related miRNAs.
Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers
and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers).
Results and Conclusion:
We identified 257 novel AD-related miRNAs and compare our method with
SVM which is applied by generating negative samples. The AUC of our method is much higher than
SVM and we did case studies to prove that our results are reliable.
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Affiliation(s)
- Tianyi Zhao
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Donghua Wang
- Department of General Surgery, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yang Hu
- School of life Science and Tenchnology, Harbin Institute of Technology, Harbin, China
| | - Ningyi Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tianyi Zang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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30
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Chen Z, Wang X, Gao P, Liu H, Song B. Predicting Disease Related microRNA Based on Similarity and Topology. Cells 2019; 8:cells8111405. [PMID: 31703479 PMCID: PMC6912199 DOI: 10.3390/cells8111405] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/31/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022] Open
Abstract
It is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a higher score indicates a greater probability of a potential connection. However, this approach does not utilize information within the network. Therefore, in this study, we propose a machine learning method, called STIM, which uses network topology information to predict disease-miRNA associations. In contrast to the conventional approach, STIM constructs features according to information on similarity and topology in networks and then uses a machine learning model to predict potential associations. To verify the reliability and accuracy of our method, we compared STIM to other classical algorithms. The results of fivefold cross validation demonstrated that STIM outperforms many existing methods, particularly in terms of the area under the curve. In addition, the top 30 candidate miRNAs recommended by STIM in a case study of lung neoplasm have been confirmed in previous experiments, which proved the validity of the method.
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Affiliation(s)
- Zhihua Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
| | - Xinke Wang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Peng Gao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hongju Liu
- College of Information Technology and Computer Science, University of the Cordilleras, Baguio 2600, Philippines
| | - Bosheng Song
- School of Information Science and Engineering, Hunan University, Changsha 410082, China
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31
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Cheng L, Zhao H, Wang P, Zhou W, Luo M, Li T, Han J, Liu S, Jiang Q. Computational Methods for Identifying Similar Diseases. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:590-604. [PMID: 31678735 PMCID: PMC6838934 DOI: 10.1016/j.omtn.2019.09.019] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/11/2019] [Accepted: 09/12/2019] [Indexed: 02/01/2023]
Abstract
Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms “category-based,” “simulated-patient-based,” and “benchmark-data-based” were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity.
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Affiliation(s)
- Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hengqiang Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Meng Luo
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Tianxin Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Shulin Liu
- Systemomics Center, College of Pharmacy, and Genomics Research Center (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), Harbin Medical University, Harbin, Heilongjiang, China; Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada.
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
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32
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Liang X, Zhu W, Lv Z, Zou Q. Molecular Computing and Bioinformatics. Molecules 2019; 24:E2358. [PMID: 31247973 PMCID: PMC6651761 DOI: 10.3390/molecules24132358] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 06/25/2019] [Indexed: 02/06/2023] Open
Abstract
Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of-and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions-even trillions-of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.
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Affiliation(s)
- Xin Liang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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33
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Zhao Q, Yang Y, Ren G, Ge E, Fan C. Integrating Bipartite Network Projection and KATZ Measure to Identify Novel CircRNA-Disease Associations. IEEE Trans Nanobioscience 2019; 18:578-584. [PMID: 31199265 DOI: 10.1109/tnb.2019.2922214] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Accumulating biological experiments have shown that circRNAs are closely related to the occurrence and development of many complex human diseases. During recent years, the associations of circRNA with disease have caused more and more researchers to pay attention and to analyze their correlation mechanisms. However, experimental methods for determining the associations of circRNA with a particular disease are still expensive, difficult, and time consuming. Moreover, the available databases related to circRNA-disease correlations have only recently been updated, and only a few computational methods are constructed to predict potential circRNA-disease correlations. Taking into account the limitations of experimental studies, we develop a novel computational method, named IBNPKATZ, for predicting potential circRNA-disease associations, which integrates the bipartite network projection algorithm and KATZ measure. This model is based on the known circRNA-disease associations, combining circRNA similarity and disease similarity. Specifically, the circRNA similarity is derived from the average of the semantic similarity and the Gaussian interaction profile (GIP) kernel similarity of circRNA. Similarly, disease similarity is the mean of the semantic similarity and the GIP kernel similarity of disease. Furthermore, it is semi-supervised and does not require negative samples. Finally, IBNPKATZ achieves reliable AUC of 0.9352 in the leave-one-out cross validation, and case studies show that the circRNA-disease correlations predicted by our method can be successfully demonstrated by relevant experiments. The IBNPKATZ is expected to be a useful biomedical research tool for predicting potential circRNA-disease associations.
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Xuan P, Sun C, Zhang T, Ye Y, Shen T, Dong Y. Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs. Front Genet 2019; 10:459. [PMID: 31214240 PMCID: PMC6555260 DOI: 10.3389/fgene.2019.00459] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 04/30/2019] [Indexed: 02/01/2023] Open
Abstract
Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Chang Sun
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin, China
| | - Yilin Ye
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tonghui Shen
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Yihua Dong
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
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35
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Wang Y, Huang X, Liu J, Zhao X, Yu H, Cai Y. A Systems Analysis of the Relationships Between Anemia and Ischemic Stroke Rehabilitation Based on RNA-Seq Data. Front Genet 2019; 10:456. [PMID: 31178893 PMCID: PMC6543706 DOI: 10.3389/fgene.2019.00456] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 04/30/2019] [Indexed: 12/18/2022] Open
Abstract
Ischemic stroke (IS) is one of the main causes of morbidity and disability worldwide due to its complex mechanism. Anemia was characterized as a risk factor of IS because the direct connection between central nervous system, blood supply, and tissue oxygen delivery. As the key oxygen-carrying molecule in the blood, hemoglobin (Hb) may be decisive in the destiny of penumbral area or influence the brain recovery and neurologic function, which could finally affect the outcome of IS. However, more detailed information on the expression levels of Hb related genes were still lacking possibly because the concentration of Hb was determined by the genes’ expression several hours ago, which may make the research more difficult to perform. This time gap between gene expressions and protein concentration could make these genes predictive bio-markers for IS outcome. In this study, we choose 28 IS patients, of which 12 were suffering from anemia. Statistical analysis results showed that the outcomes of the patients were different when dividing them into two groups characterized by Hb concentration. 2 sex and age matched patients were first chosen to perform RNA-seq analysis on, on two occasions at two different time points, after which the Hb counts were tested at least 24 h after sequencing. Results showed that the outcome of anemia patients was poor compared with non-anemia patients. Two other patients were then chosen for analysis which excluded the coincidence of other factors. The results showed that the low value of Hb under 13 g/dL in men were closely related to the poor outcome of IS patients. Differently expressed Hb related genes were tested and six genes were shown to be positively correlated with the recovery degree of IS patients: ELANE, FGF23, HBB, PIEZO1, RASA4, and PRTN3. Gene CPM was shown to be negatively correlated with clinical outcomes. All of the seven genes were validated to be related to strokes using real-time PCR or literature searches. Taken together, these genes could be considered as new predictors for the recovery of IS patients.
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Affiliation(s)
- Yingying Wang
- Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technologies, Chinese Academy of Sciences, Shenzhen, China
| | - Xingxian Huang
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Jianfeng Liu
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuefei Zhao
- Institute of Harbin Hematology & Oncology, The First Hospital of Harbin, Harbin, China
| | - Haibo Yu
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Yunpeng Cai
- Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technologies, Chinese Academy of Sciences, Shenzhen, China
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36
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Wang TT, Lee CY, Lai LC, Tsai MH, Lu TP, Chuang EY. anamiR: integrated analysis of MicroRNA and gene expression profiling. BMC Bioinformatics 2019; 20:239. [PMID: 31088348 PMCID: PMC6518761 DOI: 10.1186/s12859-019-2870-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 05/02/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND With advancements in high-throughput technologies, the cost of obtaining expression profiles of both mRNA and microRNA in the same individual has substantially decreased. Integrated analysis of these profiles can help to elucidate the functional effects of RNA expression in complex diseases, such as cancer. However, fundamental discrepancies are observed in the results from microRNA-mRNA target gene prediction algorithms, and few packages can be used to analyze microRNA and mRNA expression levels simultaneously. RESULTS To address these issues, an R package, anamiR, was developed. A total of 10 experimental/prediction databases were integrated. Two analytical functions are provided in anamiR, including the single marker test and functional gene set enrichment analysis, and several parameters can be changed by users. Here we demonstrate the potential application of the anamiR package to 2 publicly available microarray datasets. CONCLUSION The anamiR package is effective for an integrated analysis of both RNA and microRNA profiles. By characterizing biological functions and signaling pathways, this package helps identify dysregulated genes/miRNAs from biological and medical experiments. The source code and manual of the anamiR package are freely available at https://bioconductor.org/packages/release/bioc/html/anamiR.html .
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Affiliation(s)
- Ti-Tai Wang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan
| | - Chien-Yueh Lee
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan
| | - Liang-Chuan Lai
- Graduate Institute of Physiology, National Taiwan University, Taipei, 10051, Taiwan
| | - Mong-Hsun Tsai
- Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.,Institute of Biotechnology, National Taiwan University, Taipei, 10672, Taiwan.,Center for Biotechnology, National Taiwan University, Taipei, 10672, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 10055, Taiwan.
| | - Eric Y Chuang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan. .,Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.
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Xie G, Wu C, Sun Y, Fan Z, Liu J. LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm. Front Genet 2019; 10:343. [PMID: 31057602 PMCID: PMC6482170 DOI: 10.3389/fgene.2019.00343] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 03/29/2019] [Indexed: 12/26/2022] Open
Abstract
According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA.
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Affiliation(s)
- Guobo Xie
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Cuiming Wu
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Zhiliang Fan
- School of Computers, Guangdong University of Technology, Guangzhou, China
| | - Jianghui Liu
- Department of Emergency, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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38
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Li Y, Niu M, Zou Q. ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm. J Proteome Res 2019; 18:1392-1401. [DOI: 10.1021/acs.jproteome.9b00012] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Yanjuan Li
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Mengting Niu
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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