Genetics

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning.

BACKGROUND: Literature about SARS-CoV-2 widely discusses the effects of variations that have spread ...

PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network.

As the core of pollution prevention and management, accurate PM2.5 concentration prediction is cruci...

Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified.

Differentiating the intrinsic subtypes of breast cancer is crucial for deciding the best treatment s...

Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of t...

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19.

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants ...

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndro...

EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species.

Methylation is a major DNA epigenetic modification for regulating the biological processes without a...

A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images.

Evolutionary algorithms have been successfully employed to find the best structure for many learning...

High-throughput deep learning variant effect prediction with Sequence UNET.

Understanding coding mutations is important for many applications in biology and medicine but the va...

EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations.

Deep neural networks (DNNs) hold promise for functional genomics prediction, but their generalizatio...

Machine learning in cardiology: Clinical application and basic research.

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine le...

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Genome-wide association studies have proven their ability to improve human health outcomes by identi...

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields....

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

The number of published metagenome assemblies is rapidly growing due to advances in sequencing techn...

Robotic-navigated assistance in spine surgery.

The aim of this study was to assess the accuracy of pedicle screw placement, as well as intraoperati...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth i...

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

Recent advances in single-cell sequencing technology have made it possible to measure multiple paire...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of e...

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.

Protein-coding differences between species often fail to explain phenotypic diversity, suggesting th...

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations a...