AIMC Topic: Software

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Requirement Analysis for Data-Driven Electroencephalography Seizure Monitoring Software to Enhance Quality and Decision Making in Digital Care Pathways for Epilepsy: A Feasibility Study from the Perspectives of Health Care Professionals.

JMIR human factors
BACKGROUND: Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients' quality of life. Therefore, it is critical to track epileptic seizures, diagnose them, and provide potential ther...

PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.

Human genetics
Reliable prediction of pathogenic variants plays a crucial role in personalized medicine, which aims to provide accurate diagnosis and individualized treatment using genomic medicine. This study introduces PRP, a pathogenic risk prediction for rare n...

scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling.

Biology direct
BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) technology reveals biological processes and molecular-level genomic information among individual cells. Numerous computational methods, including methods based on graph neural networks (GNNs), have b...

Reliable protein-protein docking with AlphaFold, Rosetta, and replica exchange.

eLife
Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformation...

MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data.

Journal of proteome research
MultiOmicsAgent (MOAgent) is an innovative, Python-based open-source tool for biomarker discovery, utilizing machine learning techniques, specifically extreme gradient-boosted decision trees, to process multiomics data. With its cross-platform compat...

Creating, anonymizing and evaluating the first medical language model pre-trained on Dutch Electronic Health Records: MedRoBERTa.nl.

Artificial intelligence in medicine
Electronic Health Records (EHRs) contain written notes by all kinds of medical professionals about all aspects of well-being of a patient. When adequately processed with a Large Language Model (LLM), this enormous source of information can be analyze...

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data.

Genome biology
Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from ...

RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study.

BMC medical genomics
BACKGROUND: Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression...

CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.

Genome biology
The large-scale application of the mammalian methylation array has substantially expanded the availability of DNA methylation data in mammalian species. However, this data captures only a small portion of species-tissue combinations. To address this,...

Flexible imputation toolkit for electronic health records.

Scientific reports
Missing data in electronic health records (EHRs) poses a significant challenge for analysis. This study introduces Pympute, a comprehensive Python package designed for efficient and robust missing value imputation for EHRs. Pympute's core algorithm, ...