Autism

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy....

Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears.

The detection of genetic aberrations is crucial for early therapy decisions in acute myeloid leukemia (AML) and recommended for all patients. Because genetic testing is expensive and time consuming, a need remains for cost-effective, fast, and broadly accessible tests to predict these aberrations in this aggressive malignancy. Here, we developed a novel fully automated end-to-end deep learning pip...

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct an...

Investigations on druggable gene mutations related to AML/ALL lineage genes in Advanced Phases of CML: Implications in patient-tailored therapy of blast crisis CML in TKI era

Chronic Myeloid Leukemia (CML) is a myeloproliferative stem cell malignancy. Chronic Phase CML (CP-CML) is treatable with overall survival equivalent ...

A Comprehensive System for Searching and Evaluating Genomic Variant Evidence Using AI and Knowledge Bases to Support Personalized Medicine.

We introduce an innovative automated system for the search and assessment of genetic variant evidence, meticulously aligned with ACMG guidelines. Leve...

Unlocking Early Cancer Detection: Leveraging Machine Learning in Cell-Free DNA Analysis for Precision Oncology.

This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning al...

Using AI and ML to Predict Autism Spectrum Disorder.

Autism spectrum disorder is a condition that showcases the potential usefulness of artificial intelligence (AI) and machine learning (ML). This is an ...

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

BACKGROUND: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods most...

What Legal Frameworks Should Govern Use of Genetic Test Results by Private Health Insurers in New Zealand?

The rising cost of private health insurance and constraints within public health systems are global concerns. Genetic testing presents a transformativ...

ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.

MOTIVATION: Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more...

State of the Journal, 2023.

The American Journal of Occupational Therapy (AJOT) has maintained its top-ranking status in the field of occupational therapy, as evidenced by an inc...

Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches.

Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetic...

Chatbot Artificial Intelligence for Genetic Cancer Risk Assessment and Counseling: A Systematic Review and Meta-Analysis.

PURPOSE: Most individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessme...

Automatically Predicting Perceived Conversation Quality in a Pediatric Sample Enriched for Autism.

Social interaction quality ratings derived from short natural conversations can differentiate children with and without autism at the group level. In ...

ChatGPT for phenotypes extraction: one model to rule them all?

Information Extraction (IE) is a core task in Natural Language Processing (NLP) where the objective is to identify factual knowledge in textual docume...

Deep-Learning Markerless Tracking of Infant General Movements using Standard Video Recordings.

Monitoring spontaneous General Movements (GM) of infants 6-20 weeks post-term age is a reliable tool to assess the quality of neurodevelopment in earl...

A deep learning algorithm-based visual strategy intervention study for children with autism spectrum disorders - extraction and detection of children's behavioral features.

OBJECTIVE: Autism spectrum disorder is a group of neurodevelopmental disorders. The disease's etiology is unclear, and there is no specific drug treat...

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. Howe...

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple...

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants.

Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multip...