Sequence representation approaches for sequence-based protein prediction tasks that use deep learning
Deep learning has been increasingly used in bioinformatics, especially in sequence-based
protein prediction tasks, as large amounts of biological data are available and deep learning …
protein prediction tasks, as large amounts of biological data are available and deep learning …
A comprehensive review of the imbalance classification of protein post-translational modifications
Post-translational modifications (PTMs) play significant roles in regulating protein structure,
activity and function, and they are closely involved in various pathologies. Therefore, the …
activity and function, and they are closely involved in various pathologies. Therefore, the …
MK-FSVM-SVDD: a multiple kernel-based fuzzy SVM model for predicting DNA-binding proteins via support vector data description
Background: Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive. Objective: In recent years, the rise of …
methods is time-consuming and expensive. Objective: In recent years, the rise of …
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 …
sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks
Key message We proposed an ensemble convolutional neural network model to identify
sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for …
sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for …
Identification of sub-Golgi protein localization by use of deep representation learning features
Abstract Motivation The Golgi apparatus has a key functional role in protein biosynthesis
within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases …
within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases …
iTTCA-RF: a random forest predictor for tumor T cell antigens
S Jiao, Q Zou, H Guo, L Shi - Journal of translational medicine, 2021 - Springer
Background Cancer is one of the most serious diseases threatening human health. Cancer
immunotherapy represents the most promising treatment strategy due to its high efficacy and …
immunotherapy represents the most promising treatment strategy due to its high efficacy and …
[HTML][HTML] Prediction of antioxidant proteins using hybrid feature representation method and random forest
C Ao, W Zhou, L Gao, B Dong, L Yu - Genomics, 2020 - Elsevier
Natural antioxidant proteins are mainly found in plants and animals, which interact to
eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat …
eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat …
ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks
C Wang, Y Wang, P Ding, S Li, X Yu, B Yu - Computers in Biology and …, 2024 - Elsevier
The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in
bioinformatics research. Recent advancements in protein structure research have facilitated …
bioinformatics research. Recent advancements in protein structure research have facilitated …
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding
Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200
nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs …
nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs …