Autism

Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks.

UNLABELLED: To enhance the prediction progression of Alzheimer's Disease is very excavating process. PROBLEM STATEMENT: It is often very difficult to implement in the chronic neurodegenerative disease-related preprocessed gene expression data. Especially, Alzheimer's Disease (AD) prediction is a very crucial process in AD metadata diagnosis. Novelty: To explore this challenging prediction process ...

Transdiagnostic behavioral and sociodemographic influences on the cognitive-adaptive functioning gap in neurodivergent children.

Adaptive functioning typically aligns with cognitive ability in the general population, but this relationship appears more complex in neurodivergent populations. Individuals with autism, attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD) frequently show a discrepancy between cognitive ability and adaptive functioning. However, the behavioral and sociodemograph...

Artificial intelligence platform for promoting learning skills of people with autism spectrum disorder.

The World Health Organization (WHO) reports that more than one billion people globally suffer from substantial disability. Involving and integrating i...

An interpretable multimodal framework using compact biomarkers and Kolmogorov-Arnold networks improves the early diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease (AD), especially accurate identification at the mild cognitive impairment (MCI) stage, is crucial for slowing d...

Internally validated machine learning models identify asthma and its phenotypes using multicenter real-world data.

Asthma exhibits significant clinical heterogeneity, necessitating precise phenotyping for personalized treatment. This study aims to develop and inter...

A 2004-2025 Bibliometric Study of Genetic Variation and Multiomics Biomarkers in Sepsis Based on 940 Publications.

Sepsis is a highly heterogeneous syndrome, and conventional clinical indicators and single biomarkers often fail to capture its biological complexity ...

Using Deep Learning Models as a Genetic Architecture for the Simulation of Breeding Schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations t...

Swimming in the future already: the use of AI in zebrafish neurobehavioral research and CNS drug screening.

Artificial intelligence (AI) is rapidly revolutionizing biomedical research. Empowered by enhanced object recognition and modern machine learning prot...

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome.

BACKGROUND: Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). ...

Enhancing the Effectiveness of Attention-Deficit/Hyperactivity Disorder Screening Using the SNAP-IV: A Deep Learning Approach.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition requiring early and accurate identification to optimize outco...

Unveiling the identification of the genetic features and biological characteristics shared between cervical cancer and venous thromboembolism.

OBJECTIVE: The association between cervical cancer (CC) and venous thromboembolism (VTE) has been well documented, but the underlying molecular charac...

Engineering EVs Via ZnBGs-Integrated 3D Dynamic Culture for Type II Diabetic Pressure Ulcer Therapy.

Pressure ulcers (PUs) under type II diabetic conditions pose a significant clinical challenge due to damage to the vascular and neural networks, a sit...

Integrative Chemical Genetics Platform Identifies Condensate Modulators Linked to Neurological Disorders.

Dysregulation of biomolecular condensates is implicated across multiple neurological disorders. However, approaches to systematically identify their m...

Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subj...

Cross-attention-guided subject-adaptive graph learning for multimodal autism classification: integrating structural and functional MRI data.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by structural atypicality and abnormal functional connectivity. It rem...

The role and application of task-state fNIRS in identifying biomarkers in children and adolescents with ADHD: a systematic review.

Attention Deficit Hyperactivity Disorder (ADHD), a prevalent neurodevelopmental condition, urgently requires objective biomarkers to improve diagnosti...

AI/ML-Enabled Multi-Omics Integration of Host Genetics, Immunity, and the Gut Microbiome in Crohn's Disease: From Diagnosis to Theranostics.

Crohn's disease is a long-term inflammatory disorder arising from the interaction of genetic risk factors, immune system dysfunction, and alterations ...

Phylogenetic analysis and machine learning identify signatures of selection and predict deleterious mutations in common bean (Phaseolus vulgaris L.).

BACKGROUND: Mutations are a continuous source of new alleles and genetic diversity in populations. Domestication and selection influence the accumulat...

Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.

BACKGROUND: The advent of health technologies associated with artificial intelligence (AI) is deemed a transformative shift in the delivery of medical...

A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.

Developmental neurotoxicity (DNT) represents a critical yet underevaluated toxicity endpoint within current chemical safety assessment frameworks, par...