InterPro in 2019: improving coverage, classification and access to protein sequence annotations.
Alex L. MitchellTeresa K. AttwoodPatricia C. BabbittMatthias BlumPeer BorkAlan BridgeShoshana D. BrownHsin-Yu ChangSara El-GebaliMatthew FraserJulian GoughDavid HaftHongzhan HuangIvica LetunicRodrigo LopezAurelien LucianiFábio MadeiraAron Marchler-BauerHuaiyu MiDarren A. NataleMarco NecciGift NukaChristine A. OrengoArun Prasad PanduranganTyphaine Paysan-LafosseSebastien PesseatSimon C. PotterMatloob QureshiNeil D. RawlingsNicole RedaschiLorna J. RichardsonCatherine RivoireGustavo A. SalazarAmaia Sangrador-VegasChristian J. A. SigristIan SillitoeGranger G. SuttonNarmada ThankiPaul D. ThomasSilvio C. E. TosattoSiew-Yit YongRobert D. FinnPublished in: Nucleic Acids Res. (2019)
Keyphrases
- protein sequences
- protein classification
- classification accuracy
- feature selection
- image classification
- machine learning
- training set
- feature vectors
- feature set
- access control
- protein structure
- protein structure prediction
- secondary structure
- protein structure and function
- biological sequences
- computational biology
- fine grained
- support vector machine svm