COVID-19

Learn to segment single cells with deep distance estimator and deep cell detector.

Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, deep convolutional neural networks (CNN) treat segmentation as a pixel-wise classification problem and have become a general and efficient method for im...

Prediction of Long Non-Coding RNAs Based on Deep Learning.

With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify long non-coding RNAs (lncRNAs) from transcripts is a challenging task. The identification and inclusion of lncRNAs not only can more clearly help us to understand life activities themselves, but can also help humans further explore and study the dise...

Acute renal failure in systemic sclerosis revealing Goodpasture syndrome: "All that glitters is not scleroderma renal crisis".

The most common cause of acute renal failure in systemic sclerosis patients is scleroderma renal crisis but other etiologies have to be considered suc...

Booster immunity - diagnosis of chronic hepatitis B viral infection.

INTRODUCTION: Diagnosis of chronic hepatitis B virus (HBV) infection particularly its occult form requires monitoring and repeat serological and molec...

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor deline...

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by ut...

Reliability and acceptability of using a social robot to carry out cognitive tests for community-dwelling older adults.

AIM: To improve access to cognitive testing for older adults, the reliability and acceptability of a speech-based cognitive test administered by a soc...

Human electrocortical dynamics while stepping over obstacles.

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroenc...

Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images.

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prere...

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing.

Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of ...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecu...

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...

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.

Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is hi...

ClinTAD: a tool for copy number variant interpretation in the context of topologically associated domains.

Standard clinical interpretation of DNA copy number variants (CNVs) identified by cytogenomic microarray involves examining protein-coding genes withi...

NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.

State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with ...

Ultrasensitive sandwich-type immunosensor for cardiac troponin I based on enhanced electrocatalytic reduction of HO using β-cyclodextrins functionalized 3D porous graphene-supported Pd@Au nanocubes.

In this study, Pd@Au nanocubes supported β-cyclodextrins functionalized three-dimensional porous graphene (CDs-3D-PG-Pd@Au NCs) was synthesized using ...

DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype...

Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2.

IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times a...

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating met...

Using deep-learning algorithms to derive basic characteristics of social media users: The Brexit campaign as a case study.

A recurrent criticism concerning the use of online social media data in political science research is the lack of demographic information about social...