Genetics

Sub-micro scale cell segmentation using deep learning.

Automated cell segmentation is key for rapid and accurate investigation of cell responses. As instru...

Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.

In contrast to the well characterized mitotic machinery in eukaryotes it seems as if there is no uni...

For whom the artificial intelligence bell tolls: preimplantation genetic testing for aneuploidy, does it toll for thee?

Memristive Behaviors Dominated by Reversible Nucleation Dynamics of Phase-Change Nanoclusters.

One of the important steps for realizing artificial intelligence is identifying elementary units tha...

PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorde...

Deep learning identified glioblastoma subtypes based on internal genomic expression ranks.

BACKGROUND: Glioblastoma (GBM) can be divided into subtypes according to their genomic features, inc...

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by th...

A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of ...

A deep learning model for early risk prediction of heart failure with preserved ejection fraction by DNA methylation profiles combined with clinical features.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic...

Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription ...

A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data.

The fast-advancing single cell RNA sequencing (scRNA-seq) technology enables researchers to study th...

Multimodal deep learning applied to classify healthy and disease states of human microbiome.

Metagenomic sequencing methods provide considerable genomic information regarding human microbiomes,...

Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map.

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensab...

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. ...

[Moving towards a personalized oncology: The contribution of genomic techniques and artificial intelligence in the use of circulating tumor biomarkers].

Technological advances, in particular the development of high-throughput sequencing, have led to the...

Genetic Programming for Instance Transfer Learning in Symbolic Regression.

Transfer learning has attracted more attention in the machine-learning community recently. It aims t...

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients.

Mental disorders present a global health concern, while the diagnosis of mental disorders can be cha...

DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level.

The interaction between proteins and RNA is closely related to various human diseases. Computer-aide...

Forty years of directed evolution and its continuously evolving technology toolbox: A review of the patent landscape.

Generating functional protein variants with novel or improved characteristics has been a goal of the...

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.

Identifying relevant disease modules such as target cell types is a significant step for studying di...

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding can...