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Basak US, Sattari S, Hossain M, Horikawa K, Komatsuzaki T. Transfer entropy dependent on distance among agents in quantifying leader-follower relationships. Biophys Physicobiol 2021; 18:131-144. [PMID: 34178564 PMCID: PMC8214925 DOI: 10.2142/biophysico.bppb-v18.015] [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: 03/19/2021] [Accepted: 05/13/2021] [Indexed: 12/01/2022] Open
Abstract
Synchronized movement of (both unicellular and multicellular) systems can be observed almost everywhere. Understanding of how organisms are regulated to synchronized behavior is one of the challenging issues in the field of collective motion. It is hypothesized that one or a few agents in a group regulate(s) the dynamics of the whole collective, known as leader(s). The identification of the leader (influential) agent(s) is very crucial. This article reviews different mathematical models that represent different types of leadership. We focus on the improvement of the leader-follower classification problem. It was found using a simulation model that the use of interaction domain information significantly improves the leader-follower classification ability using both linear schemes and information-theoretic schemes for quantifying influence. This article also reviews different schemes that can be used to identify the interaction domain using the motion data of agents.
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Affiliation(s)
- Udoy S. Basak
- Graduate School of Life Science, Transdisciplinary Life Science Course, Hokkaido University, Sapporo, Hokkaido 060-0812, Japan
- Pabna University of Science and Technology, Pabna 6600, Bangladesh
| | - Sulimon Sattari
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan
| | - Motaleb Hossain
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan
- University of Dhaka, Dhaka 1000, Bangladesh
| | - Kazuki Horikawa
- Department of Optical Imaging, The Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima 770-8503, Japan
| | - Tamiki Komatsuzaki
- Graduate School of Life Science, Transdisciplinary Life Science Course, Hokkaido University, Sapporo, Hokkaido 060-0812, Japan
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo, Hokkaido 001-0020, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- Graduate School of Chemical Sciences and Engineering Materials Chemistry and Engineering Course, Hokkaido University, Sapporo, Hokkaido 060-0812, Japan
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Cai L, Huang T, Su J, Zhang X, Chen W, Zhang F, He L, Chou KC. Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia. MOLECULAR THERAPY. NUCLEIC ACIDS 2018; 12:433-442. [PMID: 30195780 PMCID: PMC6041437 DOI: 10.1016/j.omtn.2018.05.026] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 05/19/2018] [Accepted: 05/30/2018] [Indexed: 12/21/2022]
Abstract
Schizophrenia (SCZ) is a devastating genetic mental disorder. Identification of the SCZ risk genes in brains is helpful to understand this disease. Thus, we first used the minimum Redundancy-Maximum Relevance (mRMR) approach to integrate the genome-wide sequence analysis results on SCZ and the expression quantitative trait locus (eQTL) data from ten brain tissues to identify the genes related to SCZ. Second, we adopted the variance inflation factor regression algorithm to identify their interacting genes in brains. Third, using multiple analysis methods, we explored and validated their roles. By means of the aforementioned procedures, we have found that (1) the cerebellum may play a crucial role in the pathogenesis of SCZ and (2) ITIH4 may be utilized as a clinical biomarker for the diagnosis of SCZ. These interesting findings may stimulate novel strategy for developing new drugs against SCZ. It has not escaped our notice that the approach reported here is of use for studying many other genome diseases as well.
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Affiliation(s)
- Lei Cai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Gordon Life Science Institute, Boston, MA 02478, USA; Shanghai Center for Women and Children's Health, Shanghai 200062, China.
| | - Tao Huang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jingjing Su
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, China
| | - Xinxin Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China
| | - Wenzhong Chen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China
| | - Fuquan Zhang
- Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi 214015, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Shanghai Center for Women and Children's Health, Shanghai 200062, China.
| | - Kuo-Chen Chou
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Genetics and Development, Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai 200240, China; Gordon Life Science Institute, Boston, MA 02478, USA; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Li J, Lan CN, Kong Y, Feng SS, Huang T. Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods. Front Genet 2018; 9:246. [PMID: 30214455 PMCID: PMC6125376 DOI: 10.3389/fgene.2018.00246] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 06/22/2018] [Indexed: 12/15/2022] Open
Abstract
Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy.
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Affiliation(s)
- Jing Li
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chun-Na Lan
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ying Kong
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Song-Shan Feng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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Abstract
SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides.
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Affiliation(s)
- Henrik Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, Bldg., 208, 3500 Kgs., Lyngby, Denmark.
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SCN1B gene variants in Brugada Syndrome: a study of 145 SCN5A-negative patients. Sci Rep 2014; 4:6470. [PMID: 25253298 PMCID: PMC5377327 DOI: 10.1038/srep06470] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 08/14/2014] [Indexed: 11/08/2022] Open
Abstract
Brugada syndrome is characterised by a typical ECG with ST segment elevation in the right precordial leads. Individuals with this condition are susceptible to ventricular arrhythmias and sudden cardiac death. The principal gene responsible for this syndrome is SCN5A, which encodes the α-subunit of the Nav1.5 voltage-gated sodium channel. Mutations involving other genes have been increasingly reported, but their contribution to Brugada syndrome has been poorly investigated. Here we focused on the SCN1B gene, which encodes the β1-subunit of the voltage-gated sodium channel and its soluble β1b isoform. SCN1B mutations have been associated with Brugada syndrome as well as with other cardiac arrhythmias and familial epilepsy. In this study, we have analysed SCN1B exons (including the alternatively-spliced exon 3A) and 3'UTR in 145 unrelated SCN5A-negative patients from a single centre. We took special care to report all identified variants (including polymorphisms), following the current nomenclature guidelines and considering both isoforms. We found two known and two novel (and likely deleterious) SCN1B variants. We also found two novel changes with low evidence of pathogenicity. Our findings contribute more evidence regarding the occurrence of SCN1B variants in Brugada syndrome, albeit with a low prevalence, which is in agreement with previous reports.
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Wu Y, Jing R, Jiang L, Jiang Y, Kuang Q, Ye L, Yang L, Li Y, Li M. Combination use of protein–protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms. Amino Acids 2014; 46:2025-35. [PMID: 24849655 DOI: 10.1007/s00726-014-1760-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Accepted: 05/03/2014] [Indexed: 11/27/2022]
Affiliation(s)
- Yiming Wu
- College of Chemistry, Sichuan University, Chengdu, 610064, People's Republic of China
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Yang X, Guo Y, Luo J, Pu X, Li M. Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles. PLoS One 2013; 8:e84439. [PMID: 24391954 PMCID: PMC3877298 DOI: 10.1371/journal.pone.0084439] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 11/07/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Type III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed. RESULTS Based on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30-100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model's correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.
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Affiliation(s)
- Xiaojiao Yang
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Jiesi Luo
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, P.R.China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, P.R.China
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