@inproceedings{7004aaa04b204b7f865df27be6b075be,
title = "Incremental Semi-Supervised Learning for Functional Analysis of Protein Sequences",
abstract = "Current approaches for the functional annotation of proteins rely on training a classifier based on a fixed reference database. As more genes are sequenced, the size of the reference database grows and classifiers are retrained with the old and some new data. Considering the ever-increasing number of (meta-)genomic data, repeating this process is computationally expensive. An alternative is to update the classifier continuously based on a stream of data. Thus, in this study, we propose an incremental and semi-supervised learning approach to train a classifier for the functional analysis of protein sequences. Our method proves to have a low computational cost while maintaining high accuracy in nredicting protein functions.",
author = "Mali Halac and Bahrad Sokhansanj and Trimble, {William L.} and Thomas Coard and Sabin, {Norman C.} and Emrecan Ozdogan and Robi Polikar and Rosen, {Gail L.}",
note = "Funding Information: This work was supported in part by NSF grants #1919691 and #1936791. Funding Information: VI. ACKNOWLEDGEMENTS This work was supported by hardware provided by Drexel University{\textquoteright}s Research Computing Facility (URCF). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 ; Conference date: 05-12-2021 Through 07-12-2021",
year = "2021",
doi = "10.1109/SSCI50451.2021.9659958",
language = "English (US)",
series = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings",
address = "United States",
}