An integrative approach to identifying NPC1 as a susceptibility gene for gestational diabetes mellitus

The Journal of Maternal-Fetal & Neonatal Medicine, Journal Year: 2025, Volume and Issue: 38(1)

Published: Jan. 2, 2025

Objective The objective of this study was to identify a novel gene and its potential mechanisms associated with susceptibility gestational diabetes mellitus (GDM) through an integrative approach.

Language: Английский

---

Polygenic enrichment analysis in multi-omics levels identifies cell/tissue specific associations with schizophrenia based on single-cell RNA sequencing data

Schizophrenia Research, Journal Year: 2025, Volume and Issue: 277, P. 93 - 101

Published: March 1, 2025

Language: Английский

---

Leveraging complementary multi-omics data integration methods for mechanistic insights in kidney diseases

JCI Insight, Journal Year: 2025, Volume and Issue: 10(5)

Published: March 9, 2025

Chronic kidney diseases (CKDs) are a global health concern, necessitating comprehensive understanding of their complex pathophysiology. This study explores the use 2 complementary multidimensional -omics data integration methods to elucidate mechanisms CKD progression as proof concept. Baseline biosamples from 37 participants with in Clinical Phenotyping and Resource Biobank Core (C-PROBE) cohort prospective longitudinal outcome ascertained over 5 years were used generate molecular profiles. Tissue transcriptomic, urine plasma proteomic, targeted metabolomic profiling integrated using orthogonal multi-omics approaches, one unsupervised other supervised. Both identified 8 urinary proteins significantly associated long-term outcomes, which replicated an adjusted survival model 94 samples independent validation group same cohort. The also 3 shared enriched pathways: complement coagulation cascades, cytokine-cytokine receptor interaction pathway, JAK/STAT signaling pathway. Use different multiscalar strategies on enabled identification prioritization disease progression. Approaches like this will be invaluable expansion high-dimension diseases.

Language: Английский

---

Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction

Briefings in Bioinformatics, Journal Year: 2025, Volume and Issue: 26(2)

Published: March 1, 2025

The complementary information found in different modalities of patient data can aid more accurate modelling a patient's disease state and better understanding the underlying biological processes disease. However, analysis multi-modal, multi-omics presents many challenges. In this work, we compare performance variety ensemble machine learning (ML) algorithms that are capable late integration multi-class from modalities. methods their variations tested were (i) voting ensemble, with hard soft vote, (ii) meta learner, (iii) multi-modal AdaBoost model using learner to integrate on each boosting round, PB-MVBoost novel application mixture expert's model. These compared simple concatenation. We examine these an in-house study hepatocellular carcinoma, plus validation datasets studies breast cancer irritable bowel develop models achieve area under receiver operating curve up 0.85 find two boosted methods, vote best performing models. also stability features selected size clinical signature. Our work shows integrating omics effective ML enhances accuracy outcome predictions produces stable predictive than individual or provide recommendations for data.

Language: Английский

---

Cerebrospinal Fluid Metabolomics and Proteomics Integration in Neurological Syndromes

Methods in molecular biology, Journal Year: 2025, Volume and Issue: unknown, P. 303 - 321

Published: Jan. 1, 2025

Language: Английский

---