Autism

A Hierarchical Graph Convolutional Network With Infomax-Guided Graph Embedding for Population-Based ASD Detection.

Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential for accurately detecting autism spectrum disorders (ASD). Meanwhile, the successful use of graph convolution networks (GCNs) methods based on fMRI information has improved the classification accuracy of ASD. However, many graph convolution-based method...

Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery.

The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characteristics, and the patients' true risks cannot be fully revealed. The evaluation results based on imaging ignore the clinical symptoms and changes in qualit...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This stu...

Accelerating autism spectrum disorder care: A rapid review of data science applications in diagnosis and intervention.

Integrating data science techniques, including machine learning, natural language processing, and big data analytics, has revolutionized the diagnosis...

Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits.

Genomic prediction (GP) aims to predict the breeding values of multiple complex traits, usually assumed to be multivariate normally distributed by the...

3-D contour-aware U-Net for efficient rectal tumor segmentation in magnetic resonance imaging.

Magnetic resonance imaging (MRI), as a non-invasive detection method, is crucial for the clinical diagnosis and treatment plan of rectal cancer. Howev...

Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications.

OBJECTIVE: The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient ...

Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism.

BACKGROUND: Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care.

BACKGROUND: Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data a...

Exploring pesticide risk in autism via integrative machine learning and network toxicology.

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both genetic and environmental factors, including pesticide e...

PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset

Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognosti...

Beyond FACS: Data-driven Facial Expression Dictionaries, with Application to Predicting Autism

The Facial Action Coding System (FACS) has been used by numerous studies to investigate the links between facial behavior and mental health. The lab...

Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.

Chromatin remodeling complexes, such as the Saccharomyces cerevisiae INO80 complex, exemplify how dynamic protein interaction networks govern cellular...

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering

Diffusion models often exhibit inconsistent sample quality due to stochastic variations inherent in their sampling trajectories. Although training-b...

Machine learning positioning algorithms for long semi-monolithic scintillator PET detectors.

In this work, machine learning positioning algorithms are developed to improve the spatial resolutions of the semi-monolithic scintillator detectors i...

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis

We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discove...

Imaging biomarkers for detection and longitudinal monitoring of ventricular abnormalities from birth to childhood.

This narrative review examines the use of imaging biomarkers for diagnosing and monitoring hydrocephalus from birth through childhood. Early detection...

Fetal origins of adult disease: transforming prenatal care by integrating Barker's Hypothesis with AI-driven 4D ultrasound.

INTRODUCTION: The fetal origins of adult disease, widely known as Barker's Hypothesis, suggest that adverse fetal environments significantly impact th...

Genetic Influences on Brain Aging: Analyzing Sex Differences in the UK Biobank using Structural MRI

Brain aging trajectories differ between males and females, yet the genetic factors underlying these differences remain underexplored. Using structur...

Bio inspired optimization techniques for disease detection in deep learning systems.

Numerous contemporary computer-aided disease detection methodologies predominantly depend on feature engineering techniques; yet, they possess several...