COVID-19

Triple and quadruple optimization for feature selection in cancer biomarker discovery.

The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on prediction accuracy that neglects clinical utility and validation feasibility. We introduce three- and four-objective optimization strategies based on genetic algorithms to identify clinically actionable biomarkers in omics studies, addressing classi...

Improving tabular data extraction in scanned laboratory reports using deep learning models.

OBJECTIVE: Medical laboratory testing is essential in healthcare, providing crucial data for diagnosis and treatment. Nevertheless, patients' lab testing results are often transferred via fax across healthcare organizations and are not immediately available for timely clinical decision making. Thus, it is important to develop new technologies to accurately extract lab testing information from scan...

Particle formation in response to different protein formulations and containers: Insights from machine learning analysis of particle images.

Subvisible particle count is a biotherapeutics stability indicator widely used by pharmaceutical industries. A variety of stresses that biotherapeutic...

Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.

Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over t...

FluPMT: Prediction of Predominant Strains of Influenza A Viruses via Multi-Task Learning.

Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitat...

DeepCOVIDNet-CXR: deep learning strategies for identifying COVID-19 on enhanced chest X-rays.

OBJECTIVES: COVID-19 is one of the recent major epidemics, which accelerates its mortality and prevalence worldwide. Most literature on chest X-ray-ba...

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk...

Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning.

BACKGROUND: Precise evaluation of pathological complete response (pCR) is essential for determining the prognosis of patients with locally advanced re...

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, ...

Two-stage deep learning framework for occlusal crown depth image generation.

The generation of depth images of occlusal dental crowns is complicated by the need for customization in each case. To decrease the workload of skille...

Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic Approach.

Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fa...

Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks.

The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure th...

AI-driven antibody design with generative diffusion models: current insights and future directions.

Therapeutic antibodies are at the forefront of biotherapeutics, valued for their high target specificity and binding affinity. Despite their potential...

Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning.

INTRODUCTION: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in me...

Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study.

Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in...

Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

BACKGROUNDS: During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's h...

Integrating machine learning to advance epitope mapping.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and dia...

MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging.

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging....

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentat...

Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms.

Cognitive dysfunction caused by diabetes has become a serious global medical issue. Diabetic kidney disease (DKD) exacerbates cognitive dysfunction in...