CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning

X Qiang, C Zhou, X Ye, P Du, R Su… - Briefings in …, 2020 - academic.oup.com
Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering
cargoes into live cells, offering great potential as future therapeutics. It is essential to identify …

Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

L Wei, J Hu, F Li, J Song, R Su… - Briefings in …, 2020 - academic.oup.com
Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with
diverse cellular processes, such as cell–cell communication, and gene expression …

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

L Wei, C Zhou, H Chen, J Song, R Su - Bioinformatics, 2018 - academic.oup.com
Abstract Motivation Anti-cancer peptides (ACPs) have recently emerged as promising
therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in …

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

B Manavalan, S Basith, TH Shin, L Wei, G Lee - Bioinformatics, 2019 - academic.oup.com
Motivation Cardiovascular disease is the primary cause of death globally accounting for
approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular …

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

B Rao, C Zhou, G Zhang, R Su… - Briefings in bioinformatics, 2020 - academic.oup.com
Fast and accurate identification of the peptides with anticancer activity potential from large-
scale proteins is currently a challenging task. In this study, we propose a new machine …

DeepPhos: prediction of protein phosphorylation sites with deep learning

F Luo, M Wang, Y Liu, XM Zhao, A Li - Bioinformatics, 2019 - academic.oup.com
Motivation Phosphorylation is the most studied post-translational modification, which is
crucial for multiple biological processes. Recently, many efforts have been taken to develop …

Fast prediction of protein methylation sites using a sequence-based feature selection technique

L Wei, P Xing, G Shi, Z Ji, Q Zou - IEEE/ACM Transactions on …, 2017 - ieeexplore.ieee.org
Protein methylation, an important post-translational modification, plays crucial roles in many
cellular processes. The accurate prediction of protein methylation sites is fundamentally …

TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model

K Yan, H Lv, Y Guo, Y Chen, H Wu, B Liu - Bioinformatics, 2022 - academic.oup.com
Motivation Therapeutic peptide prediction is important for the discovery of efficient
therapeutic peptides and drug development. Researchers have developed several …

BBPpred: sequence-based prediction of blood-brain barrier peptides with feature representation learning and logistic regression

R Dai, W Zhang, W Tang, E Wynendaele… - Journal of Chemical …, 2021 - ACS Publications
Blood-brain barrier peptides (BBPs) have a large range of biomedical applications since
they can cross the blood-brain barrier based on different mechanisms. As experimental …

SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins

W Hussain, YD Khan, N Rasool, SA Khan… - Journal of theoretical …, 2019 - Elsevier
The protein prenylation (or S-prenylation) is one of the most essential modifications,
required for the association of membrane of a plethora of signalling proteins with the key …