AIMC Topic: Software

Clear Filters Showing 3131 to 3140 of 3675 articles

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.

Briefings in bioinformatics
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evalua...

Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data.

Briefings in bioinformatics
Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC seq...

Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.

Briefings in bioinformatics
A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core prom...

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.

Briefings in bioinformatics
Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechan...

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.

Briefings in bioinformatics
Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vas...

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network.

Briefings in bioinformatics
It is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, the...

TrimNet: learning molecular representation from triplet messages for biomedicine.

Briefings in bioinformatics
MOTIVATION: Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction (CPI) identification. A key challenge is to learn useful molecular represent...

Prediction of driver variants in the cancer genome via machine learning methodologies.

Briefings in bioinformatics
Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, an...

SICaRiO: short indel call filtering with boosting.

Briefings in bioinformatics
Despite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition of true indels is of prime importance in many applications, such as personalized health care, diseas...

Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasiona...