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Cancer Cell
Efficient prediction of TCGA cancer subtypes using compact multi-omic signatures for personalized medicine
Most methods for discovering cancer subtypes cannot label new samples from other studies or trials. This work overcomes that limitation by using five machine learning approaches on multi-omic data from ...
Hindawi - CIN
Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework
This research work fuses the competence of AutoML and computational intelligence to discover highly predictive features for autism that would enable possible early detection of the disorder.
MDPI - cells
A Blood-Based Molecular Clock for Biological Age Estimation
Extensive efforts have been made to identify biomarkers of biological age. DNA methylation levels of ELOVL fatty acid elongase 2 (ELOVL2) and the signal joint T-cell receptor rearrangement excision circles ...
Crisis
Toward Automatic Risk Assessment to Support Suicide Prevention
Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Suicide risk has proven extremely difficult to assess for ...
NATURE
A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity
There is a need to identify at-risk individuals early that would benefit from timely medical interventions for COVID-19 disease. DNA methylation provides an opportunity to identify an epigenetic signature of ...
SPRINGER
Mass Spectrometry Proteomics analysis discovers biomarkers in serum months to years before non-small cell lung cancer: The HUNT study
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities. A novel algorithm for dimensionality reduction called Pathway Activity Score Learning (PASL) is presented. The major novelty ...
ACM
Just Add Data: automated predictive modeling for knowledge discovery & feature selection
The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. This paper ...
SPRINGER
Learning biologically-interpretable latent representations for gene expression data
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities. A novel algorithm for dimensionality reduction called Pathway Activity Score Learning (PASL) is presented. The major novelty ...
IJMS
Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach
Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. In this paper, an in silico pipeline was established to ...
IJMS
Outcome Prediction in Critically-ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms
ICU patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist ...
MDPI - metabolites
Short-Term Exercise Training Blunts Differences in Consecutive Daily Urine ¹H-NMR Metabolomic Signatures
Physical inactivity is a worldwide health problem, an important risk for global mortality and is associated with chronic noncommunicable diseases. The aim of this study was to explore the differences ...
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