Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

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Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing...

An automated, fully-integrated nucleic acid analyzer based on microfluidic liquid handling robot technique.

On-site nucleic acid testing (NAT) plays an important role for disease monitoring and pathogen diagn...

Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible ...

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia.

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence a...

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing...

Off the deep end: What can deep learning do for the gene expression field?

After a COVID-related hiatus, the fifth biennial symposium on Evolution and Core Processes in Gene R...

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease i...

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism.

Network representation learning or embedding aims to project the network into a low-dimensional spac...

CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes.

Metazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). ...

CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks.

DNA-binding proteins play a vital role in biological activity including DNA replication, DNA packing...

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders.

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understan...

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a st...

Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning.

In intracytoplasmic sperm injection (ICSI), a single sperm cell is selected and injected into an egg...

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Accurately predicting patient survival is essential for cancer treatment decision. However, the prog...

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NA...

Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation.

The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance ...

Artificial Intelligence Based Study Association between p53 Gene Polymorphism and Endometriosis: A Systematic Review and Meta-analysis.

BACKGROUND: The P53 gene is critical to the onset and progression of cancers. Currently, relevant st...

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.

Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient man...

Machine learning applications for transcription level and phenotype predictions.

Predicting phenotypes and complex traits from genomic variations has always been a big challenge in ...

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