Biological Sequence Representation Methods and Recent Advances: A Review.

ABSTRACT: Biological-sequence representation methods are pivotal for advancing machine learning in computational biology, transforming nucleotide and protein sequences into formats that enhance predictive modeling and downstream task performance. This review categorizes these methods into three developmental stages: computational-based, word embedding-based, and large language model (LLM)-based, detailing their principles, applications, and limitations. Computational-based methods, such as k-mer counting and position-specific scoring matrices (PSSM), extract statistical and evolutionary patterns to support tasks like motif discovery and protein-protein interaction prediction. Word embedding-based approaches, including Word2Vec and GloVe, capture contextual relationships, enabling robust sequence cla

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12467266 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature