Genetics

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

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Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare.

Agent-based approaches have been known to be appropriate as systems and methods in medical administr...

Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network.

Recent large-scale genome-wide association studies have identified common genetic variations that ma...

Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.

The study aims were to develop fracture prediction models by using machine learning approaches and g...

Machine-learning approach expands the repertoire of anti-CRISPR protein families.

The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the...

In-plane gait planning for earthworm-like metameric robots using genetic algorithm.

Locomotion of earthworm-like metameric robots results from shape changes of deformable segments. Mor...

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response ...

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms.

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response m...

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and e...

Pan-cancer image-based detection of clinically actionable genetic alterations.

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environm...

Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.

The knowledge of DNA-binding proteins would help to understand the functions of proteins better in c...

Combination of Estradiol with Leukemia Inhibitory Factor Stimulates Granulosa Cells Differentiation into Oocyte-Like Cells.

Previous studies have documented that cumulus granulosa cells (GCs) can trans-differentiation into ...

Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation.

For the past decades, simulation-based likelihood-free inference methods have enabled researchers to...

Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performe...

Spiking Neural P Systems with Delay on Synapses.

Based on the feature and communication of neurons in animal neural systems, spiking neural P systems...

Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

With advances in sequencing technology, a vast amount of genomic sequence information has become ava...

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biolog...

EDeepSSP: Explainable deep neural networks for exact splice sites prediction.

Splice site prediction is crucial for understanding underlying gene regulation, gene function for be...

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning.

N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic perfor...

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the cu...

Cell type prioritization in single-cell data.

We present Augur, a method to prioritize the cell types most responsive to biological perturbations ...

Cross-species regulatory sequence activity prediction.

Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences hav...

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