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For: Asgari E, Garakani K, McHardy AC, Mofrad MRK. MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples. Bioinformatics 2018;34:i32-42. [PMID: 29950008 DOI: 10.1093/bioinformatics/bty296] [Cited by in Crossref: 28] [Cited by in F6Publishing: 20] [Article Influence: 9.3] [Reference Citation Analysis]
Number Citing Articles
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2 Qu K, Guo F, Liu X, Lin Y, Zou Q. Application of Machine Learning in Microbiology.Front Microbiol. 2019;10:827. [PMID: 31057526 DOI: 10.3389/fmicb.2019.00827] [Cited by in Crossref: 60] [Cited by in F6Publishing: 50] [Article Influence: 20.0] [Reference Citation Analysis]
3 Madani A, Bakhaty A, Kim J, Mubarak Y, Mofrad M. Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis. J Biomech Eng 2019. [PMID: 30912802 DOI: 10.1115/1.4043290] [Cited by in Crossref: 15] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
4 Seneviratne CJ, Balan P, Suriyanarayanan T, Lakshmanan M, Lee DY, Rho M, Jakubovics N, Brandt B, Crielaard W, Zaura E. Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches. Crit Rev Microbiol 2020;46:288-99. [PMID: 32434436 DOI: 10.1080/1040841X.2020.1766414] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
5 Song K, Wright FA, Zhou YH. Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction. Front Mol Biosci 2020;7:610845. [PMID: 33392266 DOI: 10.3389/fmolb.2020.610845] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Zhou YH, Gallins P. A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Front Genet. 2019;10:579. [PMID: 31293616 DOI: 10.3389/fgene.2019.00579] [Cited by in Crossref: 50] [Cited by in F6Publishing: 41] [Article Influence: 16.7] [Reference Citation Analysis]
7 McCord BR, Gauthier Q, Cho S, Roig MN, Gibson-Daw GC, Young B, Taglia F, Zapico SC, Mariot RF, Lee SB, Duncan G. Forensic DNA Analysis. Anal Chem 2019;91:673-88. [PMID: 30485738 DOI: 10.1021/acs.analchem.8b05318] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
8 Lee SJ, Rho M. Multimodal deep learning applied to classify healthy and disease states of human microbiome. Sci Rep 2022;12:824. [PMID: 35039534 DOI: 10.1038/s41598-022-04773-3] [Reference Citation Analysis]
9 Curry KD, Nute MG, Treangen TJ. It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. Emerg Top Life Sci 2021;5:815-27. [PMID: 34779841 DOI: 10.1042/ETLS20210213] [Reference Citation Analysis]
10 Xia Y. Correlation and association analyses in microbiome study integrating multiomics in health and disease. Prog Mol Biol Transl Sci 2020;171:309-491. [PMID: 32475527 DOI: 10.1016/bs.pmbts.2020.04.003] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
11 Chen X, Liu L, Zhang W, Yang J, Wong KC. Human host status inference from temporal microbiome changes via recurrent neural networks. Brief Bioinform 2021:bbab223. [PMID: 34151933 DOI: 10.1093/bib/bbab223] [Reference Citation Analysis]
12 Namkung J. Machine learning methods for microbiome studies. J Microbiol 2020;58:206-16. [DOI: 10.1007/s12275-020-0066-8] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
13 Asgari E, McHardy AC, Mofrad MRK. Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX). Sci Rep 2019;9:3577. [PMID: 30837494 DOI: 10.1038/s41598-019-38746-w] [Cited by in Crossref: 25] [Cited by in F6Publishing: 16] [Article Influence: 8.3] [Reference Citation Analysis]
14 Furusawa C, Tanabe K, Ishii C, Kagata N, Tomita M, Fukuda S. Decoding gut microbiota by imaging analysis of fecal samples. iScience 2021;24:103481. [PMID: 34927025 DOI: 10.1016/j.isci.2021.103481] [Reference Citation Analysis]
15 Bahai A, Asgari E, Mofrad MRK, Kloetgen A, McHardy AC. EpitopeVec: Linear Epitope Prediction Using Deep Protein Sequence Embeddings. Bioinformatics 2021:btab467. [PMID: 34180989 DOI: 10.1093/bioinformatics/btab467] [Reference Citation Analysis]
16 LaPierre N, Ju CJ, Zhou G, Wang W. MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction. Methods 2019;166:74-82. [PMID: 30885720 DOI: 10.1016/j.ymeth.2019.03.003] [Cited by in Crossref: 34] [Cited by in F6Publishing: 18] [Article Influence: 11.3] [Reference Citation Analysis]
17 Chrisman BS, Paskov KM, Stockham N, Jung JY, Varma M, Washington PY, Tataru C, Iwai S, DeSantis TZ, David M, Wall DP. Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers. BMC Bioinformatics 2021;22:509. [PMID: 34666677 DOI: 10.1186/s12859-021-04427-7] [Reference Citation Analysis]
18 Zhao Z, Woloszynek S, Agbavor F, Mell JC, Sokhansanj BA, Rosen GL. Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network. PLoS Comput Biol 2021;17:e1009345. [PMID: 34550967 DOI: 10.1371/journal.pcbi.1009345] [Reference Citation Analysis]
19 [DOI: 10.1101/2020.07.02.184713] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Brester C, Ryzhikov I, Siponen S, Jayaprakash B, Ikonen J, Pitkänen T, Miettinen IT, Torvinen E, Kolehmainen M. Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling. Sci Total Environ 2020;717:137249. [PMID: 32092807 DOI: 10.1016/j.scitotenv.2020.137249] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
21 Asgari E, Münch PC, Lesker TR, McHardy AC, Mofrad MRK. DiTaxa: nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection. Bioinformatics 2019;35:2498-500. [PMID: 30500871 DOI: 10.1093/bioinformatics/bty954] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
22 Jin S, Zeng X, Xia F, Huang W, Liu X. Application of deep learning methods in biological networks. Brief Bioinform 2021;22:1902-17. [PMID: 32363401 DOI: 10.1093/bib/bbaa043] [Cited by in Crossref: 14] [Cited by in F6Publishing: 18] [Article Influence: 7.0] [Reference Citation Analysis]
23 Deng Z, Zhang J, Li J, Zhang X. Application of Deep Learning in Plant-Microbiota Association Analysis. Front Genet 2021;12:697090. [PMID: 34691142 DOI: 10.3389/fgene.2021.697090] [Reference Citation Analysis]
24 Carrieri AP, Haiminen N, Maudsley-Barton S, Gardiner LJ, Murphy B, Mayes AE, Paterson S, Grimshaw S, Winn M, Shand C, Hadjidoukas P, Rowe WPM, Hawkins S, MacGuire-Flanagan A, Tazzioli J, Kenny JG, Parida L, Hoptroff M, Pyzer-Knapp EO. Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences. Sci Rep 2021;11:4565. [PMID: 33633172 DOI: 10.1038/s41598-021-83922-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
25 Narayana JK, Mac Aogáin M, Goh WWB, Xia K, Tsaneva-Atanasova K, Chotirmall SH. Mathematical-based microbiome analytics for clinical translation. Comput Struct Biotechnol J 2021;19:6272-81. [PMID: 34900137 DOI: 10.1016/j.csbj.2021.11.029] [Reference Citation Analysis]
26 McElhinney JMWR, Catacutan MK, Mawart A, Hasan A, Dias J. Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges. Front Microbiol 2022;13:851450. [PMID: 35547145 DOI: 10.3389/fmicb.2022.851450] [Reference Citation Analysis]
27 Woloszynek S, Zhao Z, Chen J, Rosen GL. 16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses. PLoS Comput Biol 2019;15:e1006721. [PMID: 30807567 DOI: 10.1371/journal.pcbi.1006721] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
28 Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau EC, D'Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol 2021;12:635781. [PMID: 33692771 DOI: 10.3389/fmicb.2021.635781] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
29 Michel‐mata S, Wang X, Liu Y, Angulo MT. Predicting microbiome compositions from species assemblages through deep learning. iMeta 2022;1. [DOI: 10.1002/imt2.3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]