ACC, SN, SP, MCC, F1, AUC, AUPR scores of different prediction models on the training (A, C) and independent test (B, D) datasets.

ACC, SN, SP, MCC, F1, AUC, AUPR scores of different prediction models on the training (A, C) and independent test (B, D) datasets.

<p><b>(A-B)</b> Performance comparison of BLSAM-TIP with conventional ML models. <b>(C-D)</b> Performance comparison of BLSAM-TIP with conventional DL models.</p>

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Bibliographic Details
Main Author:	Saeed Ahmed (417198) (author)
Other Authors:	Nalini Schaduangrat (13563349) (author), Pramote Chumnanpuen (1296072) (author), S. M. Hasan Mahmud (22394673) (author), Kah Ong Michael Goh (22394676) (author), Watshara Shoombuatong (453384) (author)
Published:	2025
Subjects:	Medicine Sociology Infectious Diseases Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified various pigmentation disorders protein language model natural language processing integrating bidirectional lstm extensive material resources encode comprehensive information deep neural networks bidirectional long short enhance feature representation tyrosinase inhibitory peptides received significant attention innovative computational approach source feature embeddings feature selection method div >< p feature embeddings tyrosinase plays computational methods attention mechanism proposed method new method whitening agents term memory specially developed silico </ results indicate melanin biosynthesis intensive processes experimental results experimental methods existing methods efficient tool cosmetic applications central role based encodings balanced accuracy
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