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Cited by in F6Publishing
For: Kulmanov M, Hoehndorf R. DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier. PLoS Comput Biol 2020;16:e1008453. [PMID: 33206638 DOI: 10.1371/journal.pcbi.1008453] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Banihashem SY, Shishehchi S. Ontology-Based decision tree model for prediction of fatty liver diseases. Comput Methods Biomech Biomed Engin 2022;:1-11. [PMID: 35635206 DOI: 10.1080/10255842.2022.2081502] [Reference Citation Analysis]
2 Zha Y, Chong H, Qiu H, Kang K, Dun Y, Chen Z, Cui X, Ning K. Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches. Genome Med 2022;14:43. [PMID: 35473941 DOI: 10.1186/s13073-022-01047-5] [Reference Citation Analysis]
3 Liu L, Mamitsuka H, Zhu S. HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations. Bioinformatics 2021:btab729. [PMID: 34672333 DOI: 10.1093/bioinformatics/btab729] [Reference Citation Analysis]
4 Pourreza Shahri M, Kahanda I. Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes. BMC Bioinformatics 2021;22:500. [PMID: 34656098 DOI: 10.1186/s12859-021-04421-z] [Reference Citation Analysis]
5 Kulmanov M, Smaili FZ, Gao X, Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform 2021;22:bbaa199. [PMID: 33049044 DOI: 10.1093/bib/bbaa199] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 4.5] [Reference Citation Analysis]