AIMC Topic: Computational Biology

Clear Filters Showing 2511 to 2520 of 4583 articles

Exploration of potential key pathways and genes in multiple ocular cancers through bioinformatics analysis.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Primary cancers of the eye are common in ocular diseases. The objective of this study was to explore the underlying mechanisms and the potential target genes in multiple ocular cancers by bioinformatics approach.

Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network.

International journal of molecular sciences
Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. W...

Improved Prediction of Drug-Target Interactions Using Self-Paced Learning with Collaborative Matrix Factorization.

Journal of chemical information and modeling
Identifying drug-target interactions (DTIs) plays an important role in the field of drug discovery, drug side-effects, and drug repositioning. However, in vivo or biochemical experimental methods for identifying new DTIs are extremely expensive and t...

ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction.

BMC medical genomics
BACKGROUND: Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-the...

Machine and deep learning meet genome-scale metabolic modeling.

PLoS computational biology
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine lea...

Protein Function Prediction: From Traditional Classifier to Deep Learning.

Proteomics
Deep learning demonstrates greater competence over traditional machine learning techniques for many tasks. In last several years, deep learning has been applied to protein function prediction and a series of good achievements has been obtained. These...

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine.

Advanced materials (Deerfield Beach, Fla.)
Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patie...

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.

Environmental pollution (Barking, Essex : 1987)
Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to i...

Automated feature engineering improves prediction of protein-protein interactions.

Amino acids
Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understandi...

Machine learning approaches to predict lupus disease activity from gene expression data.

Scientific reports
The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different mi...