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) …
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification
X Liang, F Li, J Chen, J Li, H Wu, S Li… - Briefings in …, 2021 - academic.oup.com
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their
unique ability to target cancer cells without affecting healthy cells directly, they have been …
unique ability to target cancer cells without affecting healthy cells directly, they have been …
VARIDT 2.0: structural variability of drug transporter
The structural variability data of drug transporter (DT) are key for research on precision
medicine and rational drug use. However, these valuable data are not sufficiently covered …
medicine and rational drug use. However, these valuable data are not sufficiently covered …
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 …
Anticancer peptides prediction with deep representation learning features
Anticancer peptides constitute one of the most promising therapeutic agents for combating
common human cancers. Using wet experiments to verify whether a peptide displays …
common human cancers. Using wet experiments to verify whether a peptide displays …
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA
replication, play essential roles in DNA replication process. Detection of ORIs' distribution in …
replication, play essential roles in DNA replication process. Detection of ORIs' distribution in …
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors
One of the main challenges in applying machine learning algorithms to biological sequence
data is how to numerically represent a sequence in a numeric input vector. Feature …
data is how to numerically represent a sequence in a numeric input vector. Feature …
THRONE: a new approach for accurate prediction of human RNA N7-methylguanosine sites
Abstract N 7-methylguanosine (m7G) is an essential, ubiquitous, and positively charged
modification at the 5′ cap of eukaryotic mRNA, modulating its export, translation, and …
modification at the 5′ cap of eukaryotic mRNA, modulating its export, translation, and …
i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation
Abstract DNA N 6-methyladenine (6 mA) is one of the most vital epigenetic modifications
and involved in controlling the various gene expression levels. With the avalanche of DNA …
and involved in controlling the various gene expression levels. With the avalanche of DNA …
DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy
Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-
focused studies has identified a wide variety of VFs, and the growing mass of bacterial …
focused studies has identified a wide variety of VFs, and the growing mass of bacterial …