AIMC Topic: Computational Biology

Clear Filters Showing 1661 to 1670 of 4583 articles

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Scientific reports
Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) h...

DeepCME: A deep learning framework for computing solution statistics of the chemical master equation.

PLoS computational biology
Stochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models, the Kolmogor...

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

Computational and mathematical methods in medicine
One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main...

EnTSSR: A Weighted Ensemble Learning Method to Impute Single-Cell RNA Sequencing Data.

IEEE/ACM transactions on computational biology and bioinformatics
The advancements of single-cell RNA sequencing (scRNA-seq) technologies have provided us unprecedented opportunities to characterize cellular states and investigate the mechanisms of complex diseases. Due to technical issues such as dropout events, s...

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.

IEEE/ACM transactions on computational biology and bioinformatics
A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially importan...

WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate diagnoses and treatments. However, it is still a challenge to effectively integrate multiple omics da...

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

IEEE/ACM transactions on computational biology and bioinformatics
Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classificatio...

An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data.

IEEE/ACM transactions on computational biology and bioinformatics
Myocardial infarctions and heart failure are the cause of more than 17 million deaths annually worldwide. ST-segment elevation myocardial infarctions (STEMI) require timely treatment, because delays of minutes have serious clinical impacts. Machine l...

Protein Fold Recognition From Sequences Using Convolutional and Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the p...

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions.

IEEE/ACM transactions on computational biology and bioinformatics
Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the e...