Autism

Predicting the outcome of psychological treatments for borderline personality disorder and posttraumatic stress disorder: a machine learning approach to predict long-term outcome of Narrative Exposure Therapy vs. Dialectical Behavioral Therapy based treatment.

: A comorbidity between Borderline Personality Disorder (BPD) and Posttraumatic Stress Disorder (PTSD) is common, severely disabling, and hard to treat. The choice of an optimal psychotherapy based on patient characteristics remains challenging. This study develops models to predict the outcome of two psychotherapies for comorbid BPD and PTSD.: Data from two trials comparing Narrative Exposure The...

A Review of Cross-Species Transmission Mechanisms of Influenza Viruses.

The cross-species transmission of influenza viruses represents a critical link in the pandemic of zoonotic diseases. This mechanism involves multi-level interactions, including viral genetic adaptability, host-receptor compatibility, and ecological drivers. Recent studies have highlighted the essential role of mutations in hemagglutinin and neuraminidase in overcoming host barriers, while elucidat...

Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus erythematosus.

Evidence indicates a connection between periodontitis (PD) and systemic lupus erythematosus (SLE), though the underlying co-morbid mechanisms remain u...

Diagnosing migraine from genome-wide genotype data: a machine learning analysis.

Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the ...

Invited Article: Al guided Dual Antiplatelet Therapy and Anticoagulation.

Artificial intelligence (AI) has emerged as a transformative tool in healthcare through data analysis, pattern recognition and predictive modeling cap...

Novel approach for AI-based NO emission reduction in biological wastewater treatment relying on genetic algorithms and neural networks.

The potential of measurement-based control strategies for achieving lower NO emissions in biological wastewater treatment is limited due to strong tem...

AI-driven early diagnosis of specific mental disorders: a comprehensive study.

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...

Effective Autism Classification Through Grasping Kinematics.

Autism is a complex neurodevelopmental condition, where motor abnormalities play a central role alongside social and communication difficulties. These...

Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models.

OBJECTIVES: Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize p...

Assessing large language model performance related to aging in genetic conditions.

Most genetic conditions are described in pediatric populations, leaving a gap in understanding their clinical progression and management in adulthood....

Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associa...

Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.

BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and...

Advancement in early diagnosis of polycystic ovary syndrome: biomarker-driven innovative diagnostic sensor.

Polycystic ovary syndrome (PCOS) is a heterogeneous multifactorial endocrine disorder that affects one in five women around the globe. The pathology s...

Statistical algorithms for the analysis of deleterious genetic mutations.

We present algorithms for model selection and parameter estimation concerning deleterious genetic mutations. Three models are considered: single gene ...

Automated extraction of functional biomarkers of verbal and ambulatory ability from multi-institutional clinical notes using large language models.

BACKGROUND: Functional biomarkers in neurodevelopmental disorders, such as verbal and ambulatory abilities, are essential for clinical care and resear...

Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis.

BACKGROUND AND AIM: Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancer...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods eithe...

Brain circuits that regulate social behavior.

Social interactions are essential for the survival of individuals and the reproduction of populations. Social stressors, such as social defeat and iso...

Current and Emerging Precision Therapies for Developmental and Epileptic Encephalopathies.

Developmental and epileptic encephalopathies (DEEs) are severe neurological disorders characterized by childhood-onset seizures and significant develo...

Accelerated Design of Fenton-Like Copper Single-Atom Catalysts by Adaptive Learning with Genetic Programming.

Traditional trial-and-error methods for optimizing catalyst synthesis are time-consuming and costly, exploring only a small fraction of the vast combi...