Allergy & Immunology

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor ...

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.

BACKGROUND: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target effects. However, most peptides predicted using MHC binding prediction algorithms do not induce a CD8 + T cell response, and there is a crucial need to refine the predictions to readily identify the best antigens that could mediate T-cell responses. Suc...

Do no harm: Natural language processing of social media supports safety of aseptic allergen immunotherapy procedures.

An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning.

Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significan...

Reporting and connecting cell type names and gating definitions through ontologies.

BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry a...

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

OBJECTIVES: Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that ass...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunother...

netDx: interpretable patient classification using integrated patient similarity networks.

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clini...

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes.

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have ...

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.

The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study do...

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of dr...

Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

A varying oxygen environment is known to affect cellular function in disease as well as activity of various therapeutics. For transient structures, wh...

Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of Emtricitabine and Tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug.

Spectrophotometric analysis method based on artificial neural network (ANN), partial least squares regression (PLS) and principal component regression...

ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm.

The major histocompatibility complex (MHC) is a term for all gene groups of a major histocompatibility antigen. It binds to peptide chains derived fro...

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.

PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportuni...

Application of adaptive-network-based fuzzy inference systems to the parameter optimization of a biochemical rule-based model.

In this study, the binding of allergens to antibody-receptor complexes was investigated. This process is important in understanding the allergic respo...

Skin Sensitization Testing-What's Next?

There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as ...

Sex Differences in Nociceptin/Orphanin FQ Peptide Receptor-Mediated Pain and Anxiety Symptoms in a Preclinical Model of Post-traumatic Stress Disorder.

Nociceptin/Orphanin FQ (N/OFQ) is a neuropeptide that modulates pain transmission, learning/memory, stress, anxiety, and fear responses via activation...

Deep learning for DNase I hypersensitive sites identification.

BACKGROUND: The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can e...

Ceftaroline Fosamil as an Alternative for a Severe Methicillin-resistant Staphylococcus aureus Infection: A Case Report.

Bacteremia secondary to methicillin-resistant Staphylococcus aureus (MRSA) is a dreaded medical condition that is not only associated with a significa...