An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM.

ABSTRACT: Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe,

SUBMITTER: Karakaya O 

PROVIDER: S-EPMC10909209 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature