COVID-19

Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be ful...

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studi...

Investigation of the potential molecular mechanisms of acupuncture in the treatment of long COVID: a bioinformatics approach.

Long COVID is a poorly understood condition characterized by persistent symptoms following the acute phase of COVID-19, including fatigue, cognitive i...

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging d...

Pretrainable geometric graph neural network for antibody affinity maturation.

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pre...

A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.

IMPORTANCE: During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists ...

A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures.

Enumeration of Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk....

A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.

Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challen...

A Physics-Informed Neural Network approach for compartmental epidemiological models.

Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forec...

Internal Medicine Year in Review 2023.

The year 2023 marked a significant change for Internal Medicine, as the number of submissions related to the novel coronavirus infection (COVID-19) de...

Brain tumor image segmentation method using hybrid attention module and improved mask RCNN.

To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation met...

Deep graph representation learning for influence maximization with accelerated inference.

Selecting a set of initial users from a social network in order to maximize the envisaged number of influenced users is known as influence maximizatio...

CNVDeep: deep association of copy number variants with neurocognitive disorders.

BACKGROUND: Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including ...

A deep convolutional neural network approach using medical image classification.

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to...

Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggeste...

Constraint based Bayesian optimization of bioink precursor: a machine learning framework.

Current research practice for optimizing bioink involves exhaustive experimentation with multi-material composition for determining the printability, ...

COVID-19 severity detection using chest X-ray segmentation and deep learning.

COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, wit...

The impacts of positive selection on genomic variation in Drosophila serrata: Insights from a deep learning approach.

This study explores the impact of positive selection on the genetic composition of a Drosophila serrata population in eastern Australia through a comp...

Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs.

The absence of a long COVID (LC) or post-acute sequelae of COVID-19 (PASC) diagnostic has profound implications for research and potential therapeutic...

Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.

Alzheimer's disease (AD) involves aggregation of amyloid β and tau, neuron loss, cognitive decline, and neuroinflammatory responses. Both resident mic...