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Cited by in F6Publishing
For: Rahman MA, Rangwala H. IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data. Bioinformatics 2020;36:i39-47. [PMID: 32657370 DOI: 10.1093/bioinformatics/btaa477] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Lim H, Cankara F, Tsai C, Keskin O, Nussinov R, Gursoy A. Artificial intelligence approaches to human-microbiome protein–protein interactions. Current Opinion in Structural Biology 2022;73:102328. [DOI: 10.1016/j.sbi.2022.102328] [Reference Citation Analysis]
2 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]