Autism

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes.

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance ...

A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative ...

Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network.

Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could i...

CRISPRidentify: identification of CRISPR arrays using machine learning approach.

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-C...

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological u...

m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.

N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and...

AI, Virtual Reality, and Robots Advancing Autism Diagnosis and Therapy.

Autism spectrum disorder (ASD) is a challenge in multiple ways. Just getting diagnosed can take months of visits to doctors and specialists. After the...

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. W...

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. H...

A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.

Epistasis is a challenge in prediction, classification, and suspicion of human genetic diseases. Many technologies, methods, and tools have been devel...

Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study.

BACKGROUND: Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impac...

Oocyte and embryo preservation in wild animals: an update.

The reduction in population genetic diversity due to inbreeding depression and the negative impact of human activity on habitats ultimately generates ...

[Application of artificial intelligence technology in the diagnosis and treatment of colorectal cancer].

The combination of artificial intelligence (AI) technology and medicine is an important milestone in the development of modern medicine, which realize...

Effect of Genetic Polymorphisms on the Pharmacokinetics of Deferasirox in Healthy Chinese Subjects and an Artificial Neural Networks Model for Pharmacokinetic Prediction.

BACKGROUND AND OBJECTIVE: Deferasirox is an oral iron chelator used to reduce iron levels in iron-overloaded patients with transfusion-dependent anemi...

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathol...

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To s...

Precision medicine and artificial intelligence: overview and relevance to reproductive medicine.

Traditionally, new treatments have been developed for the population at large. Recently, large-scale genomic sequencing analyses have revealed tremend...

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - are heterogeneous, that is, they may have several ...

A Randomized Controlled Trial of an Intelligent Robotic Response to Joint Attention Intervention System.

Although there has been growing interest in utilizing robots for intervention in autism spectrum disorder (ASD), there have been very few controlled t...

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routin...