CASE STUDIES

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.

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 (sjTRECs) represent the most promising candidates. In this study, an easy, cost-effective and reliable model to measure the individual rate and the quality of aging in human population has been proposed.

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 medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice.

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 individuals at increased risk. Machine learning was utilized to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus.

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 of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. PASL’s latent space has a fairly straightforward biological interpretation.

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 focuses on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM.

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 of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. PASL’s latent space has a fairly straightforward biological interpretation.

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 analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM).

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 but lack in specificity. The main goal of this study is to develop and validate interpretable ML models to predict early and late mortality, while exploiting all available data stored in medical record.

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 in systemic urine ¹H-NMR metabolomes between physically active and inactive healthy young males enrolled in the X-Adapt project in response to controlled exercise.

Exploring the differences between preterm and full-term male participants’ levels of urine and fecal ¹H-NMR metabolomes, during rest and exercise in normoxia and hypoxia and accessing general differences in human gut-microbiomes through metagenomics at the level of taxonomy, diversity, functional genes, enzymatic reactions, metabolic pathways and predicted gut metabolites.