Autism

Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine.

The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to ...

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain informatio...

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.

With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molec...

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA (A1C). It is a biomarker for diagnosis of diabetes and pred...

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subseq...

Genetics and voice production in childhood and adolescence - a review.

Adolescence is a challenging time of change in voicing, normally and in pathology. An increased focus on voice production in relation to genetics can ...

Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder.

Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-rea...

Leveraging innovative technology to generate drug response phenotypes for the advancement of biomarker-driven precision dosing.

Although traditional approaches to biomarker discovery have elucidated key molecular markers that have improved drug selection (precision medicine), t...

Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.

Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This ...

Creating artificial human genomes using generative neural networks.

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased compu...

Use of a tele-operated robot to increase sociability in individuals with autism spectrum disorder who display Hikikomori.

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.

Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic reson...

Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.

We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations...

Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve th...

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of t...

Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.

BACKGROUND: Next-generation sequencing provides comprehensive information about individuals' genetic makeup and is commonplace in oncology clinical pr...

Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network.

OBJECTIVE: To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations...

Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.

BACKGROUND: Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as researc...

A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.

Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular t...

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets a...