Autism

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.

The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We developed a machine learning model, called the Synthesis Success Calculator, to predict whether a long DNA fragment can be readily synthesized with a shor...

Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. ...

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell l...

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory fa...

Transthoracic echocardiography monitoring during ASD closure using an artificial hand system.

AIM: Continuous real-time echocardiographic monitoring is essential for guidance during ASD closure. However, transthoracic echocardiography (TTE) can...

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is mu...

Predicting geographic location from genetic variation with deep neural networks.

Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allow...

A preliminary evaluation of still face images by deep learning: A potential screening test for childhood developmental disabilities.

Most developmental disorders are defined by their clinical symptoms and many disorders share common features. The main objective of this research is t...

Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI.

Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a general...

Effects of CYP2C19*17 Genetic Polymorphisms on the Steady-State Concentration of Diazepam in Patients With Alcohol Withdrawal Syndrome.

Diazepam is one of the most widely prescribed tranquilizers for the therapy of alcohol withdrawal syndrome (AWS), which includes the symptoms of anxi...

Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks.

PURPOSE: Fetal brain abnormalities are some of the most common congenital malformations that may associated with syndromic and chromosomal malformatio...

Same father, same face: Deep learning reveals selection for signaling kinship in a wild primate.

Many animals rely on facial traits to recognize their kin; however, whether these traits have been selected specifically for this function remains unk...

Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing e...

Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis.

Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing...

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, th...

Artificial Intelligence Models Predict Operative Versus Nonoperative Management of Patients with Adult Spinal Deformity with 86% Accuracy.

OBJECTIVE: Patients with ASD show complex and highly variable disease. The decision to manage patients operatively is largely subjective and varies ba...

Adherence and acceptability of a robot-assisted Pivotal Response Treatment protocol for children with autism spectrum disorder.

The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on the current state-of-the-art in both ASD interven...

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including p...

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, econo...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognit...