Genetics

Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth.

A complex interplay of genetic and environmental factors influences bacterial growth. Understanding these interactions is crucial for insights into complex living systems. This study employs a data-driven approach to uncover the principles governing bacterial growth changes due to genetic and environmental variation. A pilot survey is conducted across 115 Escherichia coli strains and 135 synthetic...

Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.

BACKGROUND: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hidden systemic biases such as GC bias, which can significantly affect the sensitivity and specificity of CNV detection. In many cases, the kit manifests...

Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular si...

Advancing Regulatory Genomics With Machine Learning.

In recent years, several machine learning (ML) approaches have been proposed to predict gene expression signal and chromatin features from the DNA seq...

ShinyGS-a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations.

Genomic prediction is a powerful approach for improving genetic gain and shortening the breeding cycles in animal and crop breeding programs. A series...

Urinary TYROBP and HCK as genetic biomarkers for non-invasive diagnosis and therapeutic targeting in IgA nephropathy.

BACKGROUND: IgA nephropathy (IgAN) is a leading cause of renal failure, but its pathogenesis remains unclear, complicating diagnosis and treatment. Th...

Liquid biopsy for Renal Cell Carcinoma: A comprehensive review of techniques, applications, and future prospects.

Liquid biopsy techniques have developed rapidly in recent years and demonstrated success in cancer detection, disease characterization, and ongoing di...

Predicting the infecting dengue serotype from antibody titre data using machine learning.

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant chal...

Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.

Essential genes are those whose presence is vital for a cell's survival and growth. Detecting these genes in disease-causing organisms is critical for...

Enhancement on selenium volatilization for phytoremediation: role of plant and soil microbe interaction.

This study aimed at quantifying the potential effects of plant and soil microbial interaction on selenium (Se) volatilization, with the specific objec...

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical app...

Artificial intelligence in pediatric allergy research.

UNLABELLED: Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous dis...

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks.

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and develop...

The Role of ctDNA in the Management of Non-Small-Cell Lung Cancer in the AI and NGS Era.

Liquid biopsy (LB) involves the analysis of circulating tumour-derived DNA (ctDNA), providing a minimally invasive method for gathering both quantitat...

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer.

Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need fo...

A sparse and wide neural network model for DNA sequences.

Accurate modeling of DNA sequences requires capturing distant semantic relationships between the nucleotide acid bases. Most existing deep neural netw...

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Netwo...

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference.

The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. Howeve...

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing...

Illuminating Entomological Dark Matter with DNA Barcodes in an Era of Insect Decline, Deep Learning, and Genomics.

Most insects encountered in the field are initially entomological dark matter in that they cannot be identified to species while alive. This explains ...