AIMC Topic: Machine Learning

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Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.

BMC bioinformatics
BACKGROUND: Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB ...

Overlay databank unlocks data-driven analyses of biomolecules for all.

Nature communications
Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make...

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

BMC bioinformatics
BACKGROUND: The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This i...

AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: The review of clinical reports is an essential part of monitoring disease progression. Synthesizing multiple imaging reports is also important for clinical decisions. It is critical to aggregate information quickly and accurat...

Classification performance bias between training and test sets in a limited mammography dataset.

PloS one
OBJECTIVES: To assess the performance bias caused by sampling data into training and test sets in a mammography radiomics study.

Predicting T-Cell Lymphoma in Children From F-FDG PET-CT Imaging With Multiple Machine Learning Models.

Journal of imaging informatics in medicine
This study aimed to examine the feasibility of utilizing radiomics models derived from F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone F-FDG PET/CT scans. Lesions were extracted from PET/CT and...

Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions.

Computational biology and chemistry
Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when ...

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted...