Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
S Basith, B Manavalan, T Hwan Shin… - Medicinal research …, 2020 - Wiley Online Library
Discovery and development of biopeptides are time‐consuming, laborious, and dependent
on various factors. Data‐driven computational methods, especially machine learning (ML) …
on various factors. Data‐driven computational methods, especially machine learning (ML) …
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Motivation Therapeutic peptides failing at clinical trials could be attributed to their toxicity
profiles like hemolytic activity, which hamper further progress of peptides as drug …
profiles like hemolytic activity, which hamper further progress of peptides as drug …
[HTML][HTML] Computational prediction of secreted proteins in gram-negative bacteria
X Hui, Z Chen, J Zhang, M Lu, X Cai, Y Deng… - Computational and …, 2021 - Elsevier
Gram-negative bacteria harness multiple protein secretion systems and secrete a large
proportion of the proteome. Proteins can be exported to periplasmic space, integrated into …
proportion of the proteome. Proteins can be exported to periplasmic space, integrated into …
SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome
DNA N 6-adenine methylation (6mA) is an epigenetic modification in prokaryotes and
eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and …
eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and …
mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer
cells. The accurate prediction of ACPs from given peptide sequences remains as an open …
cells. The accurate prediction of ACPs from given peptide sequences remains as an open …
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction
Protein post-translational modification (PTM) is an important regulatory mechanism that
plays a key role in both normal and disease states. Acetylation on lysine residues is one of …
plays a key role in both normal and disease states. Acetylation on lysine residues is one of …
H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA
O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a
crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high …
crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high …
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
S-Palmitoylation is a uniquely reversible and biologically important post-translational
modification as it plays an essential role in a variety of cellular processes including signal …
modification as it plays an essential role in a variety of cellular processes including signal …
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome
Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription
factors enhance the transcription of related genes. Due to its sporadic distribution and …
factors enhance the transcription of related genes. Due to its sporadic distribution and …
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
DNA N 4-methylcytosine (4mC) is one of the key epigenetic alterations, playing essential
roles in DNA replication, differentiation, cell cycle, and gene expression. To better …
roles in DNA replication, differentiation, cell cycle, and gene expression. To better …