Structure prediction of cyclic peptides by molecular dynamics+ machine learning
Recent computational methods have made strides in discovering well-structured cyclic
peptides that preferentially populate a single conformation. However, many successful cyclic …
peptides that preferentially populate a single conformation. However, many successful cyclic …
DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information
DNA-binding proteins (DBPs) participate in various biological processes including DNA
replication, recombination, and repair. In the human genome, about 6–7% of these proteins …
replication, recombination, and repair. In the human genome, about 6–7% of these proteins …
AptaNet as a deep learning approach for aptamer–protein interaction prediction
N Emami, R Ferdousi - Scientific reports, 2021 - nature.com
Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively
bind to their specific targets with high specificity and affinity. As a powerful new class of …
bind to their specific targets with high specificity and affinity. As a powerful new class of …
Disease genes prioritizing mechanisms: a comprehensive and systematic literature review
E Seyyedrazzagi, NJ Navimipour - Network Modeling Analysis in Health …, 2017 - Springer
Recognition of disease genes and using the computational techniques are considered as
the basic issue in biomedical and bioinformatics examines. One of the initial problems in …
the basic issue in biomedical and bioinformatics examines. One of the initial problems in …
Classification of small GTPases with hybrid protein features and advanced machine learning techniques
Objective: Small GTPase is an important molecular switch that plays an important role in
numerous signaling transduction pathways, the aim is to explore its binary classification …
numerous signaling transduction pathways, the aim is to explore its binary classification …
Machine learning Ensemble for the Parkinson's disease using protein sequences
The most challenging issue in diagnosing and treating neurological disorders is gene
identification that causes the disease. Classification of the genes that cause or initiate …
identification that causes the disease. Classification of the genes that cause or initiate …
C-PUGP: A cluster-based positive unlabeled learning method for disease gene prediction and prioritization
A Vasighizaker, S Jalili - Computational biology and chemistry, 2018 - Elsevier
Disease gene detection is an important stage in the understanding disease processes and
treatment. Some candidate disease genes are identified using many machine learning …
treatment. Some candidate disease genes are identified using many machine learning …
Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method
Drug targets must be identified and positioned correctly to research and manufacture new
drugs. In this study, rather than using traditional methods for drug expansion, the drug target …
drugs. In this study, rather than using traditional methods for drug expansion, the drug target …
Predicting disease-related genes by path structure and community structure in protein–protein networks
K Hu, JB Hu, L Tang, J Xiang, JL Ma… - Journal of Statistical …, 2018 - iopscience.iop.org
Network-based computational approaches in the prediction of genes that are associated
with diseases are of considerable importance in uncovering the molecular basis of human …
with diseases are of considerable importance in uncovering the molecular basis of human …
Protein function prediction from protein–protein interaction network using gene ontology based neighborhood analysis and physico-chemical features
Protein Function Prediction from Protein–Protein Interaction Network (PPIN) and physico-
chemical features using the Gene Ontology (GO) classification are indeed very useful for …
chemical features using the Gene Ontology (GO) classification are indeed very useful for …