Genetics

Detecting COVID-19 in Chest X-Ray Images via MCFF-Net.

COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has ...

Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection.

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecti...

Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation.

Credit risk evaluation is a crucial yet challenging problem in financial analysis. It can not only help institutions reduce risk and ensure profitabil...

Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression.

Advances in biotechnology and machine learning have created an enhanced environment for unearthing and exploiting previously unrecognized relationship...

Effectiveness of Tenofovir Alafenamide in Chronic Hepatitis B Patients with Normal Alanine Aminotransferase and Positive Hepatitis B Virus DNA.

BACKGROUND AND AIMS: With an increasing understanding of hepatitis B, the antiviral indications have been broadening gradually. To evaluate the effect...

Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.

Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or ...

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been i...

Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning.

The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-...

Using deep learning to identify recent positive selection in malaria parasite sequence data.

BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, incl...

Decreased neutrophil-mediated bacterial killing in COVID-19 patients.

The coronavirus disease COVID-19 was first described in December 2019. The peripheral blood of COVID-19 patients have increased numbers of neutrophils...

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures.

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. ...

Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images.

BACKGROUND: In recent years, the fibroblast growth factor receptor (FGFR) pathway has been proven to be an important therapeutic target in bladder can...

Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target o...

Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen.

Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the inductio...

Evaluating machine learning methodologies for identification of cancer driver genes.

Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological anal...

A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets.

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Suc...

DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning.

BACKGROUND: Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biolog...

The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study.

BACKGROUND: Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmo...

Deep learning connects DNA traces to transcription to reveal predictive features beyond enhancer-promoter contact.

Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chr...

A deep learning approach to identify gene targets of a therapeutic for human splicing disorders.

Pre-mRNA splicing is a key controller of human gene expression. Disturbances in splicing due to mutation lead to dysregulated protein expression and c...