AIMC Topic: Humans

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A Network Enhancement Method to Identify Spurious Drug-Drug Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive res...

PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites.

IEEE/ACM transactions on computational biology and bioinformatics
Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of ...

TDFFM: Transformer and Deep Forest Fusion Model for Predicting Coronavirus 3C-Like Protease Cleavage Sites.

IEEE/ACM transactions on computational biology and bioinformatics
COVID-19, caused by the highly contagious SARS-CoV-2 virus, is distinguished by its positive-sense, single-stranded RNA genome. A thorough understanding of SARS-CoV-2 pathogenesis is crucial for halting its proliferation. Notably, the 3C-like proteas...

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

IEEE/ACM transactions on computational biology and bioinformatics
Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vacci...

Boundary-Aware Dual Biaffine Model for Sequential Sentence Classification in Biomedical Documents.

IEEE/ACM transactions on computational biology and bioinformatics
Assigning appropriate rhetorical roles, such as "background," "intervention," and "outcome," to sentences in biomedical documents can streamline the process for physicians to locate evidence and resources for medical treatment and decision-making. Wh...

SGLMDA: A Subgraph Learning-Based Method for miRNA-Disease Association Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNA) are endogenous non-coding RNAs, typically around 23 nucleotides in length. Many miRNAs have been founded to play crucial roles in gene regulation though post-transcriptional repression in animals. Existing studies suggest that the d...

Improving Clinical Decision Making With a Two-Stage Recommender System.

IEEE/ACM transactions on computational biology and bioinformatics
Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses ch...

Machine learning-based identification and validation of aging-related genes in cardiomyocytes from patients with atrial fibrillation.

Minerva cardiology and angiology
BACKGROUND: Aging is a key risk factor for atrial fibrillation (AF), a prevalent cardiac disorder among the elderly. This study aims to elucidate the genetic underpinnings of AF in the context of aging.

Evaluating the positive predictive value of code-based identification of cirrhosis and its complications utilizing GPT-4.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Diagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing using large language models (...

Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
PURPOSE: This study evaluated the accuracy of a commercial deep learning (DL)-based algorithm for segmenting the prostate, seminal vesicles (SV), and organs at risk (OAR) in patients with prostate cancer.