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Yang Y, Tang X, Lin Z, Zheng T, Zhang S, Liu T, Yang X. An integrative evaluation of circadian gene TIMELESS as a pan-cancer immunological and predictive biomarker. Eur J Med Res 2023; 28:563. [PMID: 38053143 DOI: 10.1186/s40001-023-01519-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/09/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND The gene TIMELESS, which is involved in the circadian clock and the cell cycle, has recently been linked to various human cancers. Nevertheless, the association between TIMELESS expression and the prognosis of individuals afflicted with pan-cancer remains largely unknown. OBJECTIVES The present study aims to exhaustively scrutinize the expression patterns, functional attributes, prognostic implications, and immunological contributions of TIMELESS across diverse types of human cancer. METHODS The expression of TIMELESS in normal and malignant tissues was examined, as well as their clinicopathologic and survival data. The characteristics of genetic alteration and molecular subtypes of cancers were also investigated. In addition, the relationship of TIMELESS with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and drug sensitivity was illustrated. Immunohistochemistry (IHC) was used to validate the expression of TIMELESS in clinical patients with several types of cancer. RESULTS In contrast to the matching normal controls, most tumor types were found to often overexpress TIMELESS. Abnormal expression of TIMELESS was significantly related to more advanced tumor stage and poorer prognosis of breast cancer, as well as infiltrating immune cells such as cancer-associated fibroblast infiltration in various tumors. Multiple cancer types exhibited abnormal expression of TIMELESS, which was also highly correlated with MSI and TMB. More crucially, TIMELESS showed promise in predicting the effectiveness of immunotherapy and medication sensitivity in cancer therapy. Moreover, cell cycle, DNA replication, circadian rhythm, and mismatch repair were involved in the functional mechanisms of TIMELESS on carcinogenesis. Furthermore, immunohistochemical results manifested that the TIMELESS expression was abnormal in some cancers. CONCLUSIONS This study provides new insights into the link between the circadian gene TIMELESS and the development of various malignant tumors. The findings suggest that TIMELESS could be a prospective prognostic and immunological biomarker for pan-cancer.
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Affiliation(s)
- Yaocheng Yang
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, 136 Renmin Middle Road, Changsha, Hunan, 410011, People's Republic of China
| | - Xianzhe Tang
- Department of Orthopedics, Chenzhou First People's Hospital, Chenzhou, Hunan, China
| | - Zhengjun Lin
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, 136 Renmin Middle Road, Changsha, Hunan, 410011, People's Republic of China
| | - Tao Zheng
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, 136 Renmin Middle Road, Changsha, Hunan, 410011, People's Republic of China
| | - Sheng Zhang
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Tang Liu
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, 136 Renmin Middle Road, Changsha, Hunan, 410011, People's Republic of China
| | - Xiaolun Yang
- Department of Stomatology, The Second Xiangya Hospital, Central South University, 136 Renmin Middle Road, Changsha, Hunan, 410011, People's Republic of China.
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2
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Liu S, Cai T, Tang X, Zhang Y, Wang C. COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention. Comput Biol Med 2022; 149:106065. [PMID: 36081225 PMCID: PMC9433340 DOI: 10.1016/j.compbiomed.2022.106065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/07/2022] [Accepted: 08/27/2022] [Indexed: 12/11/2022]
Abstract
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.
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3
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Qiu F, Zheng P, Heidari AA, Liang G, Chen H, Karim FK, Elmannai H, Lin H. Mutational Slime Mould Algorithm for Gene Selection. Biomedicines 2022; 10:biomedicines10082052. [PMID: 36009599 PMCID: PMC9406076 DOI: 10.3390/biomedicines10082052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 02/02/2023] Open
Abstract
A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.
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Affiliation(s)
- Feng Qiu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Pan Zheng
- Information Systems, University of Canterbury, Christchurch 8014, New Zealand
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
- Correspondence:
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Haiping Lin
- Department of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou 310018, China
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4
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Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2516653. [PMID: 36004205 PMCID: PMC9393965 DOI: 10.1155/2022/2516653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/25/2022] [Accepted: 07/29/2022] [Indexed: 12/17/2022]
Abstract
The cell cycle is composed of a series of ordered, highly regulated processes through which a cell grows and duplicates its genome and eventually divides into two daughter cells. According to the complex changes in cell structure and biosynthesis, the cell cycle is divided into four phases: gap 1 (G1), DNA synthesis (S), gap 2 (G2), and mitosis (M). Determining which cell cycle phases a cell is in is critical to the research of cancer development and pharmacy for targeting cell cycle. However, current detection methods have the following problems: (1) they are complicated and time consuming to perform, and (2) they cannot detect the cell cycle on a large scale. Rapid developments in single-cell technology have made dissecting cells on a large scale possible with unprecedented resolution. In the present research, we construct efficient classifiers and identify essential gene biomarkers based on single-cell RNA sequencing data through Boruta and three feature ranking algorithms (e.g., mRMR, MCFS, and SHAP by LightGBM) by utilizing four advanced classification algorithms. Meanwhile, we mine a series of classification rules that can distinguish different cell cycle phases. Collectively, we have provided a novel method for determining the cell cycle and identified new potential cell cycle-related genes, thereby contributing to the understanding of the processes that regulate the cell cycle.
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Zhang S, Sun X, Mou M, Amahong K, Sun H, Zhang W, Shi S, Li Z, Gao J, Zhu F. REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research. Comput Biol Med 2022; 148:105825. [PMID: 35872412 DOI: 10.1016/j.compbiomed.2022.105825] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 12/24/2022]
Abstract
Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.
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Affiliation(s)
- Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kuerbannisha Amahong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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6
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Huang Z, Yang L, Chen J, Li S, Huang J, Chen Y, Liu J, Wang H, Yu H. CCDC134 as a Prognostic-Related Biomarker in Breast Cancer Correlating With Immune Infiltrates. Front Oncol 2022; 12:858487. [PMID: 35311121 PMCID: PMC8927640 DOI: 10.3389/fonc.2022.858487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 02/08/2022] [Indexed: 12/24/2022] Open
Abstract
Background The expression of Coiled-Coil Domain Containing 134(CCDC134) is up-regulated in different pan-cancer species. However, its prognostic value and correlation with immune infiltration in breast cancer are unclear. Therefore, we evaluated the prognostic role of CCDC134 in breast cancer and its correlation with immune invasion. Methods We downloaded the transcription profile of CCDC134 between breast cancer and normal tissues from the Cancer Genome Atlas (TCGA). CCDC134 protein expression was assessed by the Clinical Proteomic Cancer Analysis Consortium (CPTAC) and the Human Protein Atlas. Gene set enrichment analysis (GSEA) was also used for pathway analysis. Receiver operating characteristic (ROC) curve was used to differentiate breast cancer from adjacent normal tissues. Kaplan-Meier method was used to evaluate the effect of CCDC134 on survival rate. The protein-protein interaction (PPI) network is built from STRING. Function expansion analysis is performed using the ClusterProfiler package. Through tumor Immune Estimation Resource (TIMER) and tumor Immune System Interaction database (TISIDB) to determine the relationship between CCDC134 expression level and immune infiltration. CTD database is used to predict drugs that inhibit CCDC134 and PubChem database is used to determine the molecular structure of identified drugs. Results The expression of CCDC134 in breast cancer tissues was significantly higher than that of CCDC134 mRNA expression in adjacent normal tissues. ROC curve analysis showed that the AUC value of CCDC134 was 0.663. Kaplan-meier survival analysis showed that patients with high CCDC134 had a lower prognosis (57.27 months vs 36.96 months, P = 2.0E-6). Correlation analysis showed that CCDC134 mRNA expression was associated with tumor purity immune invasion. In addition, CTD database analysis identified abrine, Benzo (A) Pyrene, bisphenol A, Soman, Sunitinib, Tetrachloroethylene, Valproic Acid as seven targeted therapy drugs that may be effective treatments for seven targeted therapeutics. It may be an effective treatment for inhibiting CCDC134. Conclusion In breast cancer, upregulated CCDC134 is significantly associated with lower survival and immune infiltrates invasion. Our study suggests that CCDC134 can serve as a biomarker of poor prognosis and a potential immunotherapy target in breast cancer. Seven drugs with significant potential to inhibit CCDC134 were identified.
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Affiliation(s)
- Zhijian Huang
- Department of Breast Surgical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China.,The Graduate School of Fujian Medical University, Fuzhou, China
| | - Linhui Yang
- Department of Breast Surgical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Jian Chen
- Department of Breast Surgical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Shixiong Li
- Department of Breast Surgical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Jing Huang
- Department of Pharmacy, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Yijie Chen
- Department of Ultrasound, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Jingbo Liu
- Pathology Department, Daqing Longnan Hospital, The Fifth Affiliated Hospital of Qiqihar Medical College, Daqing, China
| | - Hongyan Wang
- Department of Pathology, Daqing Oilfield General Hospital, Daqing, China
| | - Hui Yu
- Department of Pharmacy, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
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7
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Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7843990. [PMID: 35211187 PMCID: PMC8863443 DOI: 10.1155/2022/7843990] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/12/2022] [Accepted: 01/27/2022] [Indexed: 12/18/2022]
Abstract
Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.
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8
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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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9
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Kan Y, Jiang L, Guo Y, Tang J, Guo F. Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. Brief Bioinform 2021; 23:6426028. [PMID: 34791034 DOI: 10.1093/bib/bbab429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 08/30/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.
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Affiliation(s)
- Yingxin Kan
- 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.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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10
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Lv H, Shi L, Berkenpas JW, Dao FY, Zulfiqar H, Ding H, Zhang Y, Yang L, Cao R. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Brief Bioinform 2021; 22:bbab320. [PMID: 34410360 PMCID: PMC8511807 DOI: 10.1093/bib/bbab320] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.
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Affiliation(s)
- Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai 200433, China
| | | | - Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Liming Yang
- Department of Pathophysiology, Harbin Medical University-Daqing, Daqing, 163319, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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11
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iEnhancer-RD: Identification of enhancers and their strength using RKPK features and deep neural networks. Anal Biochem 2021; 630:114318. [PMID: 34364858 DOI: 10.1016/j.ab.2021.114318] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/02/2021] [Accepted: 07/27/2021] [Indexed: 11/20/2022]
Abstract
Enhancers are regulatory elements involved in gene expression.It is a part of DNA, which can enhance the transcription rate of gene. However, the identification of enhancer by biological experimental methods is time-consuming and expensive. Therefore, there is an urgent need for more efficient methods to identify them.In this study, we propose a new feature extraction method RKPK, which combines three feature methods and uses the recursive feature elimination algorithm for feature selection, and apply deep neural network as classifier to construct the iEnhancer-RD calculation method for enhancer identification. It is a two-layer classification architecture in which the first layer(layer I) identifies enhancers from a set of DNA sequences, and the second layer(layer II) divides the identified enhancers into two subgroups, namely strong and weak enhancers. Independent dataset test indicates that the proposed method is significantly better than most existing methods, and attains the accuracy of 78.8% and 70.5% in the two layers, respectively. Our iEnhancer-RD architecture is implemented in Python and is available at https://github.com/YangHuan639/iEnhancer-RD.
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Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9036322. [PMID: 34367320 PMCID: PMC8337127 DOI: 10.1155/2021/9036322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/10/2021] [Indexed: 01/09/2023]
Abstract
Maintenance hemodialysis is the main method for the treatment of end-stage renal disease in China. The Kt/V value is the gold standard of hemodialysis adequacy. However, Kt/V requires repeated blood drawing and evaluation; it is hard to monitor dialysis adequacy frequently. In order to meet the need for repeated clinical assessments of dialysis adequacy, we want to find a noninvasive way to assess dialysis adequacy. Therefore, we collect some clinically relevant data and develop a machine learning- (ML-) based model to predict dialysis adequacy for clinical hemodialysis patients. We collect 250 patients, including gender, age, ultrafiltration (UF), predialysis body weight (preBW), postdialysis body weights (postBW), blood pressure (BP), heart rate (HR), and blood flow (BF). An efficient graph-based Takagi-Sugeno-Kang Fuzzy System (G-TSK-FS) model is proposed to predict the dialysis adequacy of hemodialysis patients. The root mean square error (RMSE) of our model is 0.1578. The proposed model can be used as a feasible method to predict dialysis adequacy, providing a new way for clinical practice. Our G-TSK-FS model could be used as a feasible method to predict dialysis adequacy, providing a new way for clinical practice.
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A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:2464821. [PMID: 34367315 PMCID: PMC8337133 DOI: 10.1155/2021/2464821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/30/2021] [Accepted: 07/08/2021] [Indexed: 01/09/2023]
Abstract
In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.
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Wang L, Li J, Liu J, Chang M. RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5584684. [PMID: 34122617 PMCID: PMC8172296 DOI: 10.1155/2021/5584684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/12/2021] [Indexed: 11/17/2022]
Abstract
In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.
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Affiliation(s)
- Lei Wang
- Department of Basic Science Teaching, Henan Polytechnic Institute, Nanyang, 473000 Henan, China
| | - Juntao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
| | - Juanfang Liu
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
| | - Mingming Chang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
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Chen L. Novel Computational Methods in Current Biomedicine and Biopharmacy. Curr Bioinform 2020. [DOI: 10.2174/157489361509201224092120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Lei Chen
- College of Information Engineering Shanghai Maritime University Shanghai 201306,China
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