An Efficient Consolidation of Word Embedding and Deep Learning Techniques for Classifying Anticancer Peptides: FastText+BiLSTM

TL;DR

This paper presents a novel combination of FastText word embeddings and BiLSTM deep learning for accurately classifying anticancer peptides, outperforming existing methods on standard datasets.

Contribution

It introduces an efficient framework combining FastText embeddings with BiLSTM for peptide classification, achieving state-of-the-art accuracy.

Findings

Achieved 92.50% accuracy on ACPs250 dataset

Achieved 96.15% accuracy on Independent dataset

Outperforms previous state-of-the-art models

Abstract

Anticancer peptides (ACPs) are a group of peptides that exhibite 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 and FastText are evaluated as word embedding techniques for the purpose of extracting peptide sequences. Then, the output of word embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and Independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for Independent dataset, thence determining new state-of-the-art.