A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations - Jia, Hao; Park, Sung-Joon; Nakai, Kenta (BMC Bioinformatics, 2021)
Keywords: Accessibility; Algorithms; Binding sites; Bioinformatics; Biology (General); Biomedical and Life Sciences; Cell lines; Chromatin; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer applications; Computer applications to medicine. Medical informatics; Conformation; Data mining; Datasets; Deep learning; Deoxyribonucleic acid; DNA; Epigenetics; Epigenome; Gene expression; Gene regulation; Genetic research; Genetic variation; Genomes; Genomics; Histone Code; Histones; Humans; Labels; Life Sciences; Machine learning; Methods; Microarrays; Mutation; Neural networks; Non-coding variants; Nucleotide sequence; Performance prediction; Pseudo label; QH301-705.5; R858-859.7; Semi-supervised learning; Software; Supervised Machine Learning; Support vector machines; Japan; Deep learning; Non-coding variants; Epigenome; Semi-supervised learning; Pseudo label