Autism

Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases.

The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-control study was conducted to evaluate the predictive effect of GSK3B genetic polymorphisms on the susceptibility to DKD, with the aim of providing a theoretical basis and laboratory rationale for the prediction of the risk of developing DKD in patients...

Epistasis regulates genetic control of cardiac hypertrophy.

Although genetic variant effects often interact nonadditively, strategies to uncover epistasis remain in their infancy. Here we develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy, using deep learning-derived left ventricular mass estimates from 29,661 UK Biobank cardiac magnetic resonance images. We report epistatic variants near...

Model parameterization of robotic systems through the bio-inspired optimization.

The accurate modeling of dynamic systems, particularly robotic ones, is crucial in the industry. It enables simulation-based approaches that facilitat...

Context-Aware Biosensor Design Through Biology-Guided Machine Learning and Dynamical Modeling.

Addressing the challenge of achieving a global circular bioeconomy requires efficient and robust bio-based processes operating at different scales. Th...

An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engagi...

Predicting genetic merit in Harnali sheep using machine learning techniques.

Machine learning techniques offer promising avenues for enhancing animal breeding programs by leveraging genomic and phenotypic data to predict valuab...

Prime editor with rational design and AI-driven optimization for reverse editing window and enhanced fidelity.

Prime editing (PE) is a precise tool for introducing genetic mutations in eukaryotes. Extending the efficient editing scope and mitigating undesired b...

ICN intrusion detection method based on GA-CNN.

The current industrial control system network is susceptible to data theft attacks such as SQL injection in practical applications, resulting in data ...

Molecular Optimization Based on a Monte Carlo Tree Search and Multiobjective Genetic Algorithm.

In the realm of medicinal chemistry, the predominant challenge in molecular design lies in managing extensive molecular data sets and effectively scre...

Performance of deep-learning-based approaches to improve polygenic scores.

Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. N...

Inverse Neural Network Approach for Optimizing Chemical Composition in Shielded Metal Arc Weld Metals.

This study presents a hybrid machine learning framework combining an artificial neural network and a genetic algorithm to optimize chemical compositio...

Attention-driven deep learning framework for EEG analysis in ADHD detection.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects cognitive functions such as attention, impulse control, ...

Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety.

BACKGROUND: Internet-delivered cognitive behavioural therapy (ICBT) is an effective and accessible treatment for mild to moderate depression and anxie...

A solution to the postantibiotic era: phages as precision medicine.

Antibiotic-resistant bacterial infections pose a significant global health challenge. Phage therapy provides a promising alternative to antibiotics th...

Breaking barriers in rare disease research: The RARE-X Open Science Data Challenge as a model for collaborative innovation and community partnership.

Trzupek et al. describe a rare disease Open Science Data Challenge, using data collected systematically on RARE-X across 27 neurodevelopmental disorde...

Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.

Drug combination therapy is promising for cancer treatment by reducing resistance and improving efficacy. Machine learning approaches to predicting dr...

Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition that challenges early diagnosis due to its diverse manifestations across...

An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer.

Genetic testing for pathogenic germline variants is critical for the personalized management of high-risk breast cancers, guiding targeted therapies a...

Brugada syndrome.

Brugada syndrome (BrS) is a cardiac channelopathy associated with an elevated risk of arrhythmias and sudden cardiac death compared with the general p...

Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke.

Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe...