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CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning
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 …
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
Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with
diverse cellular processes, such as cell–cell communication, and gene expression …
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
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 …
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
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 …
approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular …
Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach
The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-
2) has generated significant concern and posed a considerable challenge to global health …
2) has generated significant concern and posed a considerable challenge to global health …
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 …
scale proteins is currently a challenging task. In this study, we propose a new machine …
DeepPhos: prediction of protein phosphorylation sites with deep learning
Motivation Phosphorylation is the most studied post-translational modification, which is
crucial for multiple biological processes. Recently, many efforts have been taken to develop …
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
Protein methylation, an important post-translational modification, plays crucial roles in many
cellular processes. The accurate prediction of protein methylation sites is fundamentally …
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 …
therapeutic peptides and drug development. Researchers have developed several …
Umami-MRNN: Deep learning-based prediction of umami peptide using RNN and MLP
Umami components are an important part of food condiments, and the use of umami
peptides in the condiment industry has received great attention. However, traditional …
peptides in the condiment industry has received great attention. However, traditional …