Allergy

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test ...

Pediatric Acute-Onset Neuropsychiatric Syndrome: A Data Mining Approach to a Very Specific Constellation of Clinical Variables.

Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder presenting with: unusually abrupt onset of obsessive compulsive disorder (OCD) or severe eating restrictions, with at least two concomitant cognitive, behavioral, or affective symptoms such as anxiety, obsessive-compulsive behavior, and irritability/depression. This study describes the clinical and labor...

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.

Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disru...

Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections.

PURPOSE: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-ass...

Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis.

The diagnostic possibilities of multiphoton tomography (MPT) in dermatology have already been demonstrated. Nevertheless, the analysis of MPT data is ...

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning.

Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. U...

Autonomous Biohybrid Urchin-Like Microperforator for Intracellular Payload Delivery.

A magnetic urchin-like microswimmer based on sunflower pollen grain (SPG) that can pierce the cancer cell membrane and actively deliver therapeutic dr...

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microsc...

Radiomics and deep learning in lung cancer.

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the le...

iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation.

In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompt...

Neural negated entity recognition in Spanish electronic health records.

This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated event...

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.

BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goa...

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resu...

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy.

PURPOSE: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response t...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previou...

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how th...

Vitamin D and Serum Immunoglobulin E Levels in Allergic Rhinitis: A Case-control Study from Pakistan.

Introduction Allergic rhinitis (AR) is the most common non-infectious rhinitis and is associated with sneezing, cough, and flu-like symptoms. The exac...

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. ...

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient res...

Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue.

Catecholamine neurotransmitters, specifically, dopamine (DA), epinephrine (EP), and norepinephrine (NE), are known as substantial indicators of variou...