Drug–target interaction prediction based on protein features, using wrapper feature selection
Drug–target interaction prediction is a vital stage in drug development, involving lots of
methods. Experimental methods that identify these relationships on the basis of clinical …
methods. Experimental methods that identify these relationships on the basis of clinical …
Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information
Y Li, Z Wang, LP Li, ZH You, WZ Huang, XK Zhan… - Scientific Reports, 2021 - nature.com
Various biochemical functions of organisms are performed by protein–protein interactions
(PPIs). Therefore, recognition of protein–protein interactions is very important for …
(PPIs). Therefore, recognition of protein–protein interactions is very important for …
AttABseq: an attention-based deep learning prediction method for antigen–antibody binding affinity changes based on protein sequences
The optimization of therapeutic antibodies through traditional techniques, such as candidate
screening via hybridoma or phage display, is resource-intensive and time-consuming. In …
screening via hybridoma or phage display, is resource-intensive and time-consuming. In …
CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework
Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in
solid tumor and hematologic cancer therapies. Current approaches for the design and …
solid tumor and hematologic cancer therapies. Current approaches for the design and …
An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network
H Jiang, Y Huang - BMC bioinformatics, 2022 - Springer
Abstract Background Drug-disease associations (DDAs) can provide important information
for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs …
for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs …
Predicting circRNA-disease associations using deep generative adversarial network based on multi-source fusion information
Circular RNA (circRNA) is a kind of novel discovered non-coding RNA molecule with a
closed loop structure, which plays a critical regulatory role in human diseases. Identifying …
closed loop structure, which plays a critical regulatory role in human diseases. Identifying …
Inferring disease-associated Piwi-interacting RNAs via graph attention networks
Abstract Piwi proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human
cancers. However, it is time-consuming and costly to detect piRNA-disease associations …
cancers. However, it is time-consuming and costly to detect piRNA-disease associations …
SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs
Emerging evidence suggests that PIWI-interacting RNAs (piRNAs) are one of the most
influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI …
influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI …
DeepGly: A deep learning framework with recurrent and convolutional neural networks to identify protein glycation sites from imbalanced data
J Chen, R Yang, C Zhang, L Zhang, Q Zhang - IEEE Access, 2019 - ieeexplore.ieee.org
As an unavoidable non-enzymatic reaction between proteins and reducing sugars, glycation
can decline antioxidant defense mechanisms, damage cellular organelles, and form …
can decline antioxidant defense mechanisms, damage cellular organelles, and form …
Predicting circRNA-disease associations using similarity assessing graph convolution from multi-source information networks
Y Li, XG Hu, PP Li, L Wang… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Circular RNA (circRNA), a novel endogenous noncoding RNA molecule with a closed-loop
structure, can be used as a biomarker for many complex human diseases. Determining the …
structure, can be used as a biomarker for many complex human diseases. Determining the …