AIMC Topic: Machine Learning

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Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

Journal of nephrology
BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastos...

Development of a standardized methodology for transfer learning with QSAR models: a purely data-driven approach for source task selection.

SAR and QSAR in environmental research
Transfer learning is a machine learning technique that works well with chemical endpoints, with several papers confirming its efficiency. Although effective, because the choice of source/assistant tasks is non-trivial, the application of this techniq...

Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.

BMC medical imaging
Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consuming and subjective. Due to the growing usage of machine learning techniques in applied sciences, res...

Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.

Scientific reports
DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer ...

Self-Supervised Learning for Electroencephalography.

IEEE transactions on neural networks and learning systems
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning ...

Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance.

IEEE/ACM transactions on computational biology and bioinformatics
Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether...

promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promote...

Predicting Homelessness Among Transitioning U.S. Army Soldiers.

American journal of preventive medicine
INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention.

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

Nature communications
A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of ...