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For: 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] [Cited by in Crossref: 16] [Cited by in F6Publishing: 20] [Article Influence: 16.0] [Reference Citation Analysis]
Number Citing Articles
1 Tang H, Sun L, Huang J, Yang Z, Li C, Zhou X. The mechanism and biomarker function of Cavin-2 in lung ischemia-reperfusion injury. Computers in Biology and Medicine 2022;151:106234. [DOI: 10.1016/j.compbiomed.2022.106234] [Reference Citation Analysis]
2 Zhang J, Jiang H, Shi T. ASE-Net: A tumor segmentation method based on image pseudo enhancement and adaptive-scale attention supervision module. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106363] [Reference Citation Analysis]
3 Xiang J, Wang X, Wang X, Zhang J, Yang S, Yang W, Han X, Liu Y. Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106340] [Reference Citation Analysis]
4 Falchetti M, Delgobo M, Zancanaro H, Almeida K, das Neves RN, dos Santos B, Stefanes NM, Bishop A, Santos-silva MC, Zanotto-filho A. Omics-based identification of an NRF2-related auranofin resistance signature in cancer: Insights into drug repurposing. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106347] [Reference Citation Analysis]
5 Liu C, Zhou Y, Zhou Y, Tang X, Tang L, Wang J. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106388] [Reference Citation Analysis]
6 Yang Q, Li B, Wang P, Xie J, Feng Y, Liu Z, Zhu F. LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data. Briefings in Bioinformatics 2022. [DOI: 10.1093/bib/bbac455] [Reference Citation Analysis]
7 Sun X, Zhang Y, Li H, Zhou Y, Shi S, Chen Z, He X, Zhang H, Li F, Yin J, Mou M, Wang Y, Qiu Y, Zhu F. DRESIS: the first comprehensive landscape of drug resistance information. Nucleic Acids Res 2022:gkac812. [PMID: 36243960 DOI: 10.1093/nar/gkac812] [Reference Citation Analysis]
8 Li F, Yin J, Lu M, Mou M, Li Z, Zeng Z, Tan Y, Wang S, Chu X, Dai H, Hou T, Zeng S, Chen Y, Zhu F. DrugMAP: molecular atlas and pharma-information of all drugs. Nucleic Acids Res 2022:gkac813. [PMID: 36243961 DOI: 10.1093/nar/gkac813] [Reference Citation Analysis]
9 Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Computers in Biology and Medicine 2022;149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Reference Citation Analysis]
10 Rong Z, Liu Z, Song J, Cao L, Yu Y, Qiu M, Hou Y. MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data. Comput Biol Med 2022;150:106085. [PMID: 36162197 DOI: 10.1016/j.compbiomed.2022.106085] [Reference Citation Analysis]
11 Zhou K, Cai C, He Y, Chen Z. Potential prognostic biomarkers of sudden cardiac death discovered by machine learning. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.106154] [Reference Citation Analysis]
12 Fu Z, Liu W, Huang C, Mei T. A Review of Performance Prediction Based on Machine Learning in Materials Science. Nanomaterials 2022;12:2957. [DOI: 10.3390/nano12172957] [Reference Citation Analysis]
13 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] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
14 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]
15 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: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 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: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
17 Tee KB, Ibrahim L, Hashim NM, Saiman MZ, Zakaria ZH, Huri HZ. Pharmacokinetic-Pharmacometabolomic Approach in Early-Phase Clinical Trials: A Way Forward for Targeted Therapy in Type 2 Diabetes. Pharmaceutics 2022;14:1268. [PMID: 35745841 DOI: 10.3390/pharmaceutics14061268] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
18 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] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 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: 12] [Cited by in F6Publishing: 14] [Article Influence: 12.0] [Reference Citation Analysis]
20 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]
21 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]
22 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: 25] [Cited by in F6Publishing: 25] [Article Influence: 25.0] [Reference Citation Analysis]
23 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: 33] [Cited by in F6Publishing: 35] [Article Influence: 33.0] [Reference Citation Analysis]
24 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: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]