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Pan C, Zhang Q, Zhu Y, Kong S, Liu J, Zhang C, Wang F, Zhang X. Module control of network analysis in psychopathology. iScience 2024; 27:110302. [PMID: 39045106 PMCID: PMC11263636 DOI: 10.1016/j.isci.2024.110302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/12/2024] [Accepted: 06/14/2024] [Indexed: 07/25/2024] Open
Abstract
The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.
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Affiliation(s)
- Chunyu Pan
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Northeastern University, Shenyang, Liaoning 110169, China
| | - Quan Zhang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
- Institute for Healthy China, Tsinghua University, Beijing 100084, China
| | - Yue Zhu
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
| | - Shengzhou Kong
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
| | - Juan Liu
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
| | | | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu 210024, China
- Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210033, China
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Yu D, Zhang C, Zhou Y, Yang H, Peng C, Zhang F, Liao X, Zhu Y, Deng W, Li B, Zhang S. ncR2Met (lncR2metasta v2.0): An updated database for experimentally supported ncRNAs during cancer metastatic events. Genomics 2023; 115:110569. [PMID: 36736440 DOI: 10.1016/j.ygeno.2023.110569] [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: 11/22/2022] [Revised: 01/09/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
Non-coding RNAs (ncRNAs) are widely involved in cancer metastatic events (CMEs, e.g., cancer cell invasion, intravasation, extravasation, proliferation), which collaboratively accelerate tumor spread and cause high patient mortality. In early 2020, we developed a manually curated database named 'lncR2metasta' to provide a comprehensive repository for long ncRNA (lncRNA) regulation during CMEs. We updated this database by supplementing other two important ncRNA types, microRNAs (miRNAs) and circular RNAs (circRNAs), for their involvement during CMEs after a thorough manual curation from published studies. ncR2metasta documents 1565 lncRNA-associated, 882 miRNA-associated, and 628 circRNA-associated entries for ncRNA-CME associations during 50 CMEs across 63 human cancer subtypes. ncR2Met has a concise web interface for researchers to easily browse, search and download as well as to submit novel ncRNA-CME associations. We anticipated that it could be a valuable resource, which will significantly improve our understanding of ncRNA functions in metastasis. It is freely available at http://ncr2met.wchoda.com.
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Affiliation(s)
- De'en Yu
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Cheng Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yeman Zhou
- College of Science, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Heng Yang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Chen Peng
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Feng Zhang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Xinghua Liao
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yuan Zhu
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Wensheng Deng
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China.
| | - Bo Li
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.
| | - Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China.
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Cheng X, Amanullah M, Liu W, Liu Y, Pan X, Zhang H, Xu H, Liu P, Lu Y. WMDS.net: a network control framework for identifying key players in transcriptome programs. Bioinformatics 2023; 39:7023921. [PMID: 36727489 PMCID: PMC9925106 DOI: 10.1093/bioinformatics/btad071] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/16/2023] [Accepted: 02/01/2023] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes. RESULTS To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall. AVAILABILITY AND IMPLEMENTATION https://github.com/chaofen123/WMDS.net. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiang Cheng
- Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310006, China.,Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
| | - Md Amanullah
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China.,Department of Respiratory Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Weigang Liu
- Department of Respiratory Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Yi Liu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China.,Department of Respiratory Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Xiaoqing Pan
- Department of Mathematics, Shanghai Normal University, Xuhui 200234, China
| | - Honghe Zhang
- Department of Pathology, Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy, Chinese Academy of Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
| | - Pengyuan Liu
- Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310006, China.,Department of Physiology, Center of Systems Molecular Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA.,Cancer Center, Zhejiang University, Hangzhou 310029, China
| | - Yan Lu
- Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310006, China.,Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China.,Cancer Center, Zhejiang University, Hangzhou 310029, China
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Domination based classification algorithms for the controllability analysis of biological interaction networks. Sci Rep 2022; 12:11897. [PMID: 35831440 PMCID: PMC9279401 DOI: 10.1038/s41598-022-15464-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/23/2022] [Indexed: 11/08/2022] Open
Abstract
Deciding the size of a minimum dominating set is a classic NP-complete problem. It has found increasing utility as the basis for classifying vertices in networks derived from protein-protein, noncoding RNA, metabolic, and other biological interaction data. In this context it can be helpful, for example, to identify those vertices that must be present in any minimum solution. Current classification methods, however, can require solving as many instances as there are vertices, rendering them computationally prohibitive in many applications. In an effort to address this shortcoming, new classification algorithms are derived and tested for efficiency and effectiveness. Results of performance comparisons on real-world biological networks are reported.
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