COVID-19

Development and Validation of a Hierarchical Machine Learning Method using MALDI-TOF Mass Spectrometry for Rapid SCCmec Typing and PVL Detection in MRSA: A Multi-Center Study.

OBJECTIVES: Methicillin-resistant (MRSA) is a major public health concern because of its genotypic diversity and association with severe infections, particularly those caused by strains carrying Panton-Valentine leukocidin (PVL). This study aimed to develop an artificial intelligence-clinical decision support system (AI-CDSS) to streamline MRSA genotyping and PVL detection, providing a more effic...

Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer.

Deep neural networks often suffer from poor generalization due to complex and non-convex loss landscapes. Sharpness-Aware Minimization (SAM) is a popular solution that smooths the loss landscape by minimizing the maximized change of training loss when adding a perturbation to the weight. However, indiscriminate perturbation of SAM on all parameters is suboptimal and results in excessive computatio...

Harnessing computational technologies to facilitate antibody-drug conjugate development.

Antibody-drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known ch...

Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes.

The capability to profile the landscape of antigen-binding affinities of a vast number of antibodies (B cell receptors, BCRs) will provide a powerful ...

Leveraging machine learning for monitoring afforestation in mining areas: evaluating Tata Steel's restoration efforts in Noamundi, India.

Mining activities have long been associated with significant environmental impacts, including deforestation, habitat degradation, and biodiversity los...

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201.

Class Ι major histocompatibility complexes (MHC-Ι), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nu...

Revisiting Essential and Nonessential Settings of Evidential Deep Learning.

Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation that provides reliable predictive uncertainty in a single forward pass...

Deep learning-based contour propagation in magnetic resonance imaging-guided radiotherapy of lung cancer patients.

Fast and accurate organ-at-risk (OAR) and gross tumor volume (GTV) contour propagation methods are needed to improve the efficiency of magnetic resona...

Mapping breast cancer research on monoclonal antibodies: a data-driven approach using VOSviewer, Bibliometrix, and CiteSpace.

Monoclonal Antibodies and Breast Cancer Research (MABCR) has progressed substantially, particularly in the areas of HER2-positive and triple-negative ...

Structure-based screening of small-molecule interleukin-23 inhibitors inspired by monoclonal antibody interactions.

Interleukin-23 (IL-23) is a key driver of chronic inflammatory diseases, yet current therapies rely on costly monoclonal antibodies. This study aims t...

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant systemic therapy (NAST) is an established prognostic marker in breast cancer (BC). Mul...

Intelligent Virtual Dental Implant Placement via 3D Segmentation Strategy.

Virtual dental implant placement in cone-beam computed tomography (CBCT) is a prerequisite for digital implant surgery, carrying clinical significance...

Classification of COVID-19, Long COVID, and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near-Real-Time Monitoring Component.

BACKGROUND: Heart rate variability (HRV) is a validated biomarker of autonomic and inflammatory regulation, and has been associated with both acute CO...

Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.

The rapid advancement of Next-Generation Sequencing (NGS) technologies has revolutionized the field of genomics, producing large volumes of data that ...

A Case Study for Visual Detection of a Systemic Disease: Optimizing Identification of Phony Peach Disease Based on Statistical and Machine Learning Models.

Phony peach disease (PPD), caused by subsp. (Xfm), poses a significant threat to commercial peach orchards in Georgia. Early and accurate detection ...

Utilizing Machine Learning to Improve Neutralization Potency of an HIV-1 Antibody Targeting the gp41 N-Heptad Repeat.

The N-heptad repeat (NHR) of the HIV-1 gp41 prehairpin intermediate (PHI) is an attractive potential vaccine target with high sequence conservation ac...

Disease entity impacts muscle wasting in the ICU with COVID-19 patients losing muscle nearly twice as fast.

Muscle loss in critically ill patients, particularly during prolonged ICU stays, poses significant challenges to recovery and long-term outcomes. ICU-...

Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.

BACKGROUND: The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixe...

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data.

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic varian...

Functional Characterization of Variants of Unknown Significance of Fibroblast Growth Factor Receptors 1-4 and Comparison With AI Model-Based Prediction.

PURPOSE: Fibroblast growth factor receptors (FGFRs; FGFR1, FGFR2, FGFR3, FGFR4) are frequently mutated oncogenes in solid cancers. The oncogenic poten...