Genetics

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

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NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding th...

Current developments and perspectives in psoriasis.

The study of psoriasis has yielded fundamental new insights into immunologic regulation and innovati...

Hybrid optimized feature selection and deep learning based COVID-19 disease prediction.

The COVID-19 virus has affected many people around the globe with several issues. Moreover, it cause...

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United...

Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.

CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scient...

Graph Transformer for Drug Response Prediction.

Previous models have shown that learning drug features from their graph representation is more effic...

Self-Attention Based Neural Network for Predicting RNA-Protein Binding Sites.

Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which...

MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins.

DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of ...

Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA se...

Synthetic biology, genetic circuits and machine learning: a new age of cancer therapy.

Synthetic biology has made it possible to rewire natural cellular responses to treat disease, notabl...

Precision engineering of biological function with large-scale measurements and machine learning.

As synthetic biology expands and accelerates into real-world applications, methods for quantitativel...

CRMSNet: A deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence.

RNA-binding proteins (RBPs) play significant roles in many biological life activities, many algorith...

Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

With the continuous development of information technology and the running speed of computers, the de...

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.

We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM)...

Cell type-specific interpretation of noncoding variants using deep learning-based methods.

Interpretation of noncoding genomic variants is one of the most important challenges in human geneti...

Robust deep learning object recognition models rely on low frequency information in natural images.

Machine learning models have difficulty generalizing to data outside of the distribution they were t...

DL-TODA: A Deep Learning Tool for Omics Data Analysis.

Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billi...

Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning.

Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replicat...

Cue: a deep-learning framework for structural variant discovery and genotyping.

Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome an...

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