  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.
