Genetics

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

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Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification.

RNA modifications are pivotal in the development of newly synthesized structures, showcasing a vast ...

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock.

A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in m...

Artificial Intelligence and Computational Biology in Gene Therapy: A Review.

One of the trending fields in almost all areas of science and technology is artificial intelligence....

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in r...

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of...

DNA shape features improve prediction of CRISPR/Cas9 activity.

The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biolog...

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mut...

CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.

CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged b...

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small liga...

A novel activation function based on DNA enzyme-free hybridization reaction and its implementation on nonlinear molecular learning systems.

With the advent of the post-Moore's Law era, the development of traditional silicon-based computers ...

ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data.

Various studies have linked several diseases, including cancer and COVID-19, to single nucleotide va...

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, po...

Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate iden...

Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.

OBJECTIVE: Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionize...

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.

Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoi...

Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers.

Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid N...

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