Machine learning methods for small data challenges in molecular science

B Dou, Z Zhu, E Merkurjev, L Ke, L Chen… - Chemical …, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Acp-bc: a model for accurate identification of anticancer peptides based on fusion features of bidirectional long short-term memory and chemically derived information

M Sun, H Hu, W Pang, Y Zhou - International Journal of Molecular …, 2023 - mdpi.com
Anticancer peptides (ACPs) have been proven to possess potent anticancer activities.
Although computational methods have emerged for rapid ACPs identification, their accuracy …

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation

M Arif, S Musleh, H Fida, T Alam - Scientific Reports, 2024 - nature.com
Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The
growing research on ACPs as therapeutic agent is increasing due to its minimal side effects …

Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review

H Lin - Medinformatics, 2024 - ojs.bonviewpress.com
In the 1950s and 1960s, in molecular biology, information technology was mainly applied to
the molecular evolution of proteins and DNA, and later expanded to multiple fields such as …

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

H Ghafoor, MN Asim, MA Ibrahim, S Ahmed… - Computers in Biology …, 2024 - Elsevier
Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity,
and lack of drug resistance make them ideal candidates for cancer therapies. To deeply …

Discovery of anticancer peptides from natural and generated sequences using deep learning

J Yue, T Li, J Xu, Z Chen, Y Li, S Liang, Z Liu… - International Journal of …, 2024 - Elsevier
Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment
due to their ability to selectively target and kill cancer cells. In recent years, numerous …

[HTML][HTML] LncCat: An ORF attention model to identify LncRNA based on ensemble learning strategy and fused sequence information

H Feng, S Wang, Y Wang, X Ni, Z Yang, X Hu… - Computational and …, 2023 - Elsevier
Background Long non-coding RNA (lncRNA) is one of the most essential forms of
transcripts, playing crucial regulatory roles in the development of cancers and diseases …

SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction

J Xu, X Ruan, J Yang, B Hu, S Li, J Hu - Computational Biology and …, 2024 - Elsevier
As a promising alternative to conventional antibiotic drugs in the biomedical field, functional
peptide has been widely used in disease treatment owing to its low toxicity, high absorption …

ACP-DRL: an anticancer peptides recognition method based on deep representation learning

X Xu, C Li, X Yuan, Q Zhang, Y Liu, Y Zhu… - Frontiers in …, 2024 - frontiersin.org
Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022.
Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal …

[HTML][HTML] TF-BAPred: A Universal Bioactive Peptide Predictor Integrating Multiple Feature Representations

Z Wu, X Guo, Y Sun, X Su, J Zhao - Mathematics, 2024 - mdpi.com
Bioactive peptides play essential roles in various biological processes and hold significant
therapeutic potential. However, predicting the functions of these peptides is challenging due …