ToxinPred2: an improved method for predicting toxicity of proteins
Proteins/peptides have shown to be promising therapeutic agents for a variety of diseases.
However, toxicity is one of the obstacles in protein/peptide-based therapy. The current study …
However, toxicity is one of the obstacles in protein/peptide-based therapy. The current study …
Sequential properties representation scheme for recurrent neural network-based prediction of therapeutic peptides
The discovery of therapeutic peptides is often accelerated by means of virtual screening
supported by machine learning-based predictive models. The predictive performance of …
supported by machine learning-based predictive models. The predictive performance of …
Critical evaluation of web-based DNA N6-methyladenine site prediction tools
MM Hasan, W Shoombuatong, H Kurata… - Briefings in …, 2021 - academic.oup.com
Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that
plays pivotal roles in various biological processes. The accurate genome-wide identification …
plays pivotal roles in various biological processes. The accurate genome-wide identification …
Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19
Abstract Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss
of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; …
of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; …
SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning
One of the primary tasks in vaccine design and development of immunotherapeutic drugs is
to predict conformational B-cell epitopes corresponding to primary antibody binding sites …
to predict conformational B-cell epitopes corresponding to primary antibody binding sites …
Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
P Charoenkwan, W Chiangjong, VS Lee… - Scientific reports, 2021 - nature.com
As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several
approaches based on machine learning have been proposed for ACP identification …
approaches based on machine learning have been proposed for ACP identification …
Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection
Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as
potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To …
potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To …
Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm
Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting
them from protein sequences is of primary importance. These epitopes consist of amino …
them from protein sequences is of primary importance. These epitopes consist of amino …
Recent development of bioinformatics tools for microRNA target prediction
MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of
gene expression. Binding of miRNAs to target mRNAs can repress their translation by …
gene expression. Binding of miRNAs to target mRNAs can repress their translation by …
[HTML][HTML] Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach
MR Auwul, C Zhang, MR Rahman… - Saudi Journal of …, 2021 - Elsevier
COVID-19 has emerged as global health threats. Chronic kidney disease (CKD) patients are
immune-compromised and may have a high risk of infection by the SARS-CoV-2. We aimed …
immune-compromised and may have a high risk of infection by the SARS-CoV-2. We aimed …