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For: Yang Q, Li B, Tang J, Cui X, Wang Y, Li X, Hu J, Chen Y, Xue W, Lou Y, Qiu Y, Zhu F. Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Briefings in Bioinformatics 2020;21:1058-68. [DOI: 10.1093/bib/bbz049] [Cited by in Crossref: 25] [Cited by in F6Publishing: 33] [Article Influence: 8.3] [Reference Citation Analysis]
Number Citing Articles
1 Huang X, Jiang H, Pei J, Wu Q, Wu W, Ren C, Zhou L, zhou Y, Xian B, Chen C, Yan Y, Lu L, Wang Y, Zhu X. Study on the potential mechanism, therapeutic drugs and prescriptions of insomnia based on bioinformatics and molecular docking. Computers in Biology and Medicine 2022;149:106001. [DOI: 10.1016/j.compbiomed.2022.106001] [Reference Citation Analysis]
2 Yang Q, Li Y, Li B, Gong Y. A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data. Comput Biol Med 2022;148:105956. [PMID: 35981456 DOI: 10.1016/j.compbiomed.2022.105956] [Reference Citation Analysis]
3 Zheng W, Wang T, Liu C, Yan Q, Zhan S, Li G, Liu X, Jiang Y. Liver transcriptomics reveals microRNA features of the host response in a mouse model of dengue virus infection. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106057] [Reference Citation Analysis]
4 Babu G, Nobel FA. Identification of differentially expressed genes and their major pathways among the patient with COVID-19, cystic fibrosis, and chronic kidney disease. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.101038] [Reference Citation Analysis]
5 deAndrés-Galiana EJ, Fernández-martínez JL, Álvarez-machancoses Ó, Bea G, Galmarini CM, Kloczkowski A. Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106029] [Reference Citation Analysis]
6 Iqbal N, Kumar P. Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data. Computers in Biology and Medicine 2022;147:105684. [DOI: 10.1016/j.compbiomed.2022.105684] [Reference Citation Analysis]
7 Yin H, Tao J, Peng Y, Xiong Y, Li B, Li S, Yang H. MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning. Comput Struct Biotechnol J 2022;20:3783-95. [PMID: 35891786 DOI: 10.1016/j.csbj.2022.07.022] [Reference Citation Analysis]
8 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] [Reference Citation Analysis]
9 Nosrati V, Rahmani M. An ensemble framework for microarray data classification based on feature subspace partitioning. Comput Biol Med 2022;148:105820. [PMID: 35872409 DOI: 10.1016/j.compbiomed.2022.105820] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Fan R, Suo B, Ding Y. Identification of Vesicle Transport Proteins via Hypergraph Regularized K-Local Hyperplane Distance Nearest Neighbour Model. Front Genet 2022;13:960388. [DOI: 10.3389/fgene.2022.960388] [Reference Citation Analysis]
11 Lawal B, Kuo YC, Rachmawati Sumitra M, Wu ATH, Huang HS. Identification of a novel immune-inflammatory signature of COVID-19 infections, and evaluation of pharmacokinetics and therapeutic potential of RXn-02, a novel small-molecule derivative of quinolone. Comput Biol Med 2022;148:105814. [PMID: 35841781 DOI: 10.1016/j.compbiomed.2022.105814] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 de Fátima Cobre A, Surek M, Stremel DP, Fachi MM, Lobo Borba HH, Tonin FS, Pontarolo R. Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning. Computers in Biology and Medicine 2022;146:105659. [DOI: 10.1016/j.compbiomed.2022.105659] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
13 Bagherzadeh S, Shahabi MS, Shalbaf A. Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Computers in Biology and Medicine 2022;146:105570. [DOI: 10.1016/j.compbiomed.2022.105570] [Reference Citation Analysis]
14 Azim R, Wang S, Dipu SA. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts. Computers in Biology and Medicine 2022;146:105658. [DOI: 10.1016/j.compbiomed.2022.105658] [Reference Citation Analysis]
15 Yang M, Yang H, Ji L, Hu X, Tian G, Wang B, Yang J. A multi-omics machine learning framework in predicting the survival of colorectal cancer patients. Computers in Biology and Medicine 2022;146:105516. [DOI: 10.1016/j.compbiomed.2022.105516] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
16 Hua Y, Wang H, Ye Z, Zheng D, Zhang X. An integrated pan-cancer analysis of identifying biomarkers about the EGR family genes in human carcinomas. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105889] [Reference Citation Analysis]
17 Li F, Yin J, Lu M, Yang Q, Zeng Z, Zhang B, Li Z, Qiu Y, Dai H, Chen Y, Zhu F. ConSIG: consistent discovery of molecular signature from OMIC data. Brief Bioinform 2022:bbac253. [PMID: 35758241 DOI: 10.1093/bib/bbac253] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Peng X, Wang X, Guo Y, Ge Z, Li F, Gao X, Song J. RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins. Brief Bioinform 2022:bbac215. [PMID: 35649392 DOI: 10.1093/bib/bbac215] [Reference Citation Analysis]
19 Feng G, Yao H, Li C, Liu R, Huang R, Fan X, Ge R, Miao Q. ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides. Computers in Biology and Medicine 2022;145:105459. [DOI: 10.1016/j.compbiomed.2022.105459] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Alam MS, Rahaman MM, Sultana A, Wang G, Mollah MNH. Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer. Comput Biol Med 2022;145:105508. [PMID: 35447458 DOI: 10.1016/j.compbiomed.2022.105508] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
21 Zhang C, Mou M, Zhou Y, Zhang W, Lian X, Shi S, Lu M, Sun H, Li F, Wang Y, Zeng Z, Li Z, Zhang B, Qiu Y, Zhu F, Gao J. Biological activities of drug inactive ingredients. Brief Bioinform 2022:bbac160. [PMID: 35524477 DOI: 10.1093/bib/bbac160] [Reference Citation Analysis]
22 Ding W, Nan Y, Wu J, Han C, Xin X, Li S, Liu H, Zhang L. Combining multi-dimensional molecular fingerprints to predict the hERG cardiotoxicity of compounds. Computers in Biology and Medicine 2022;144:105390. [DOI: 10.1016/j.compbiomed.2022.105390] [Reference Citation Analysis]
23 Lei Y, Meng Y, Guo X, Ning K, Bian Y, Li L, Hu Z, Anashkina AA, Jiang Q, Dong Y, Zhu X. Overview of structural variation calling: Simulation, identification, and visualization. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105534] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Dessie EY, Chang J, Chang Y. A nine-gene signature identification and prognostic risk prediction for patients with lung adenocarcinoma using novel machine learning approach. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105493] [Reference Citation Analysis]
25 Xia W, Zheng L, Fang J, Li F, Zhou Y, Zeng Z, Zhang B, Li Z, Li H, Zhu F. PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods. Comput Biol Med 2022;145:105465. [PMID: 35366467 DOI: 10.1016/j.compbiomed.2022.105465] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
26 Yu S, Chen Z, Heidari AA, Zhou W, Chen H, Xiao L. Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer. IET Renewable Power Gen. [DOI: 10.1049/rpg2.12451] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
27 Wang Y, Zhu C, Wang Y, Sun J, Ling D, Wang L. Survival risk prediction model for ESCC based on relief feature selection and CNN. Comput Biol Med 2022;145:105460. [PMID: 35364307 DOI: 10.1016/j.compbiomed.2022.105460] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Režen T, Martins A, Mraz M, Zimic N, Rozman D, Moškon M. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models. Comput Biol Med 2022;145:105428. [PMID: 35339845 DOI: 10.1016/j.compbiomed.2022.105428] [Reference Citation Analysis]
29 Khan U, Habibur Rahman M, Salauddin Khan M, Shahadat Hossain M, Morsaline Billah M. Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology. Brain Res 2022;:147889. [PMID: 35339428 DOI: 10.1016/j.brainres.2022.147889] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Zheng Z, Li Y, Lu X, Zhang J, Liu Q, Zhou D, Deng X, Qiu Y, Chen Q, Zheng H, Dai J. A novel mTOR-associated gene signature for predicting prognosis and evaluating tumor immune microenvironment in lung adenocarcinoma. Comput Biol Med 2022;145:105394. [PMID: 35325730 DOI: 10.1016/j.compbiomed.2022.105394] [Reference Citation Analysis]
31 Wang Y, Qin D, Jin L, Liang G. Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation. Comput Biol Med 2022;145:105410. [PMID: 35325732 DOI: 10.1016/j.compbiomed.2022.105410] [Reference Citation Analysis]
32 Li X, Wei S, Niu S, Ma X, Li H, Jing M, Zhao Y. Network pharmacology prediction and molecular docking-based strategy to explore the potential mechanism of Huanglian Jiedu Decoction against sepsis. Comput Biol Med 2022;144:105389. [PMID: 35303581 DOI: 10.1016/j.compbiomed.2022.105389] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022;:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Reference Citation Analysis]
34 Li F, Zhou Y, Zhang Y, Yin J, Qiu Y, Gao J, Zhu F. POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability. Brief Bioinform 2022:bbac040. [PMID: 35183059 DOI: 10.1093/bib/bbac040] [Cited by in Crossref: 15] [Cited by in F6Publishing: 2] [Article Influence: 15.0] [Reference Citation Analysis]
35 Xue W, Fu T, Deng S, Yang F, Yang J, Zhu F. Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram. ACS Chem Neurosci 2022;13:340-51. [PMID: 35041375 DOI: 10.1021/acschemneuro.1c00694] [Cited by in Crossref: 29] [Cited by in F6Publishing: 1] [Article Influence: 29.0] [Reference Citation Analysis]
36 Wang Z, Zhang Y, Li Q, Zou Q, Liu Q. A road map for happiness: The psychological factors related cell types in various parts of human body from single cell RNA-seq data analysis. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105286] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Chen Y, Wang Y, Ding Y, Su X, Wang C. RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105322] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
38 Li L, Liu Z. A connected network-regularized logistic regression model for feature selection. Appl Intell. [DOI: 10.1007/s10489-021-02877-3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Yang Q, Gong Y. Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data. Front Genet 2022;12:791349. [DOI: 10.3389/fgene.2021.791349] [Reference Citation Analysis]
40 Yan Q, Wu X, Zhou P, Zhou Y, Li X, Liu Z, Tan H, Yao W, Xia Y, Zhu F. HERV-W Envelope Triggers Abnormal Dopaminergic Neuron Process through DRD2/PP2A/AKT1/GSK3 for Schizophrenia Risk. Viruses 2022;14:145. [PMID: 35062349 DOI: 10.3390/v14010145] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
41 Wei L, Long W, Wei L. MDL-CPI: multi-view deep learning model for compound-protein interaction prediction. Methods 2022. [DOI: 10.1016/j.ymeth.2022.01.008] [Reference Citation Analysis]
42 Lin X. Genomic Variation Prediction: A Summary From Different Views. Front Cell Dev Biol 2021;9:795883. [PMID: 34901036 DOI: 10.3389/fcell.2021.795883] [Reference Citation Analysis]
43 Ao C, Zou Q, Yu L. NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences. Brief Bioinform 2021:bbab480. [PMID: 34850821 DOI: 10.1093/bib/bbab480] [Reference Citation Analysis]
44 Zhang Z, Cui F, Cao C, Wang Q, Zou Q. Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections. Comput Biol Med 2021;140:105092. [PMID: 34864302 DOI: 10.1016/j.compbiomed.2021.105092] [Cited by in Crossref: 38] [Cited by in F6Publishing: 20] [Article Influence: 38.0] [Reference Citation Analysis]
45 Fu T, Li F, Zhang Y, Yin J, Qiu W, Li X, Liu X, Xin W, Wang C, Yu L, Gao J, Zheng Q, Zeng S, Zhu F. VARIDT 2.0: structural variability of drug transporter. Nucleic Acids Res 2021:gkab1013. [PMID: 34747471 DOI: 10.1093/nar/gkab1013] [Reference Citation Analysis]
46 Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res 2021:gkab953. [PMID: 34718717 DOI: 10.1093/nar/gkab953] [Reference Citation Analysis]
47 Wang X, Li F, Qiu W, Xu B, Li Y, Lian X, Yu H, Zhang Z, Wang J, Li Z, Xue W, Zhu F. SYNBIP: synthetic binding proteins for research, diagnosis and therapy. Nucleic Acids Res 2021:gkab926. [PMID: 34664670 DOI: 10.1093/nar/gkab926] [Reference Citation Analysis]
48 Zhang S, Amahong K, Zhang C, Li F, Gao J, Qiu Y, Zhu F. RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection. Brief Bioinform 2021:bbab397. [PMID: 34585235 DOI: 10.1093/bib/bbab397] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
49 Fu TT, Tu G, Ping M, Zheng GX, Yang FY, Yang JY, Zhang Y, Yao XJ, Xue WW, Zhu F. Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors. Acta Pharmacol Sin 2021;42:1354-67. [PMID: 33122823 DOI: 10.1038/s41401-020-00541-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
50 Jiao S, Xu L, Ju Y. CWLy-RF: A novel approach for identifying cell wall lyases based on random forest classifier. Genomics 2021;113:2919-24. [PMID: 34186189 DOI: 10.1016/j.ygeno.2021.06.038] [Reference Citation Analysis]
51 Yin J, Li X, Li F, Lu Y, Zeng S, Zhu F. Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease. Comput Struct Biotechnol J 2021;19:2318-28. [PMID: 33995923 DOI: 10.1016/j.csbj.2021.04.035] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
52 Fu J, Zhang Y, Liu J, Lian X, Tang J, Zhu F. Pharmacometabonomics: data processing and statistical analysis. Brief Bioinform 2021:bbab138. [PMID: 33866355 DOI: 10.1093/bib/bbab138] [Reference Citation Analysis]
53 Zhang S, Amahong K, Sun X, Lian X, Liu J, Sun H, Lou Y, Zhu F, Qiu Y. The miRNA: a small but powerful RNA for COVID-19. Brief Bioinform 2021;22:1137-49. [PMID: 33675361 DOI: 10.1093/bib/bbab062] [Cited by in Crossref: 7] [Cited by in F6Publishing: 10] [Article Influence: 7.0] [Reference Citation Analysis]
54 Xu L, Jiao S, Zhang D, Wu S, Zhang H, Gao B. Identification of long noncoding RNAs with machine learning methods: a review. Brief Funct Genomics 2021;20:174-80. [PMID: 33758917 DOI: 10.1093/bfgp/elab017] [Reference Citation Analysis]
55 Mao X, Chen S, Li G. Identification of a ten-long noncoding RNA signature for predicting the survival and immune status of patients with bladder urothelial carcinoma based on the GEO database: a superior machine learning model. Aging (Albany NY) 2021;13:6957-81. [PMID: 33621953 DOI: 10.18632/aging.202553] [Reference Citation Analysis]
56 Zhao Q, Ma J, Xie F, Wang Y, Zhang Y, Li H, Sun Y, Wang L, Guo M, Han K. Recent Advances in Predicting Protein S-Nitrosylation Sites. Biomed Res Int 2021;2021:5542224. [PMID: 33628788 DOI: 10.1155/2021/5542224] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
57 Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2020;21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
58 Yin J, Li F, Zhou Y, Mou M, Lu Y, Chen K, Xue J, Luo Y, Fu J, He X, Gao J, Zeng S, Yu L, Zhu F. INTEDE: interactome of drug-metabolizing enzymes. Nucleic Acids Res 2021;49:D1233-43. [PMID: 33045737 DOI: 10.1093/nar/gkaa755] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
59 Liang G, Wu J, Xu L. A prognosis-related based method for miRNA selection on liver hepatocellular carcinoma prediction. Comput Biol Chem 2021;91:107433. [PMID: 33540232 DOI: 10.1016/j.compbiolchem.2020.107433] [Reference Citation Analysis]
60 Chen Z, Shen Z, Zhao D, Xu L, Zhang L, Zou Q. Genome-Wide Analysis of LysM-Containing Gene Family in Wheat: Structural and Phylogenetic Analysis during Development and Defense. Genes (Basel) 2020;12:31. [PMID: 33383636 DOI: 10.3390/genes12010031] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
61 Yang Q, Wang Y, Zhang Y, Li F, Xia W, Zhou Y, Qiu Y, Li H, Zhu F. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res 2020;48:W436-48. [PMID: 32324219 DOI: 10.1093/nar/gkaa258] [Cited by in Crossref: 31] [Cited by in F6Publishing: 28] [Article Influence: 15.5] [Reference Citation Analysis]
62 Yang Q, Li B, Chen S, Tang J, Li Y, Li Y, Zhang S, Shi C, Zhang Y, Mou M, Xue W, Zhu F. MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis. J Proteomics 2021;232:104023. [PMID: 33130111 DOI: 10.1016/j.jprot.2020.104023] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
63 Hou R, Wu J, Xu L, Zou Q, Wu YJ. Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features. ACS Omega 2020;5:27470-9. [PMID: 33134710 DOI: 10.1021/acsomega.0c03972] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
64 Guo Z, Wang P, Liu Z, Zhao Y. Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction. Front Bioeng Biotechnol 2020;8:584807. [PMID: 33195148 DOI: 10.3389/fbioe.2020.584807] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
65 Tao Z, Li Y, Teng Z, Zhao Y. A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD. Comput Math Methods Med 2020;2020:8926750. [PMID: 33133228 DOI: 10.1155/2020/8926750] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
66 Zhang P, Wu J, Zhai H, Li S. ABCModeller: an automatic data mining tool based on a consistent voting method with a user-friendly graphical interface. Brief Bioinform 2021;22:bbaa247. [PMID: 33057581 DOI: 10.1093/bib/bbaa247] [Reference Citation Analysis]
67 Li Q, Zhou W, Wang D, Wang S, Li Q. Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model. Front Bioeng Biotechnol 2020;8:892. [PMID: 32903381 DOI: 10.3389/fbioe.2020.00892] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
68 Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020;18:2012-25. [PMID: 32802273 DOI: 10.1016/j.csbj.2020.07.009] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
69 Wang C, Zhao N, Sun K, Zhang Y. A Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups. Front Oncol 2020;10:1159. [PMID: 32637361 DOI: 10.3389/fonc.2020.01159] [Reference Citation Analysis]
70 Wang Y, Zhang S, Li F, Zhou Y, Zhang Y, Wang Z, Zhang R, Zhu J, Ren Y, Tan Y, Qin C, Li Y, Li X, Chen Y, Zhu F. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res 2020;48:D1031-41. [PMID: 31691823 DOI: 10.1093/nar/gkz981] [Cited by in Crossref: 80] [Cited by in F6Publishing: 139] [Article Influence: 40.0] [Reference Citation Analysis]
71 Zhang S, Zhou Y, Wang Y, Wang Z, Xiao Q, Zhang Y, Lou Y, Qiu Y, Zhu F. The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years. Brief Bioinform 2021;22:1860-83. [PMID: 32249290 DOI: 10.1093/bib/bbaa023] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
72 Meng C, Hu Y, Zhang Y, Guo F. PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides. Front Bioeng Biotechnol 2020;8:245. [PMID: 32296690 DOI: 10.3389/fbioe.2020.00245] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
73 Zhang Y, Zheng G, Fu T, Hong J, Li F, Yao X, Xue W, Zhu F. The binding mode of vilazodone in the human serotonin transporter elucidated by ligand docking and molecular dynamics simulations. Phys Chem Chem Phys 2020;22:5132-44. [PMID: 32073004 DOI: 10.1039/c9cp05764a] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
74 Huang Q, Zhang J, Wei L, Guo F, Zou Q. 6mA-RicePred: A Method for Identifying DNA N 6-Methyladenine Sites in the Rice Genome Based on Feature Fusion. Front Plant Sci 2020;11:4. [PMID: 32076430 DOI: 10.3389/fpls.2020.00004] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 5.5] [Reference Citation Analysis]
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76 Han Z, Hua J, Xue W, Zhu F. Integrating the Ribonucleic Acid Sequencing Data From Various Studies for Exploring the Multiple Sclerosis-Related Long Noncoding Ribonucleic Acids and Their Functions. Front Genet 2019;10:1136. [PMID: 31781177 DOI: 10.3389/fgene.2019.01136] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
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