An explainable graph neural network approach for integrating multi-omics data with prior knowledge to identify biomarkers from interacting biological domains DOI Creative Commons
Rohit Tripathy,

Zachary Frohock,

Hong Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract The rapid growth of multi-omics datasets, in addition to the wealth existing biological prior knowledge, necessitates development effective methods for their integration. Such are essential building predictive models and identifying disease-related molecular markers. We propose a framework supervised integration data with priors represented as knowledge graphs. Our leverages graph neural networks (GNNs) model relationships among features from high-dimensional ‘omics set transformers integrate low-dimensional representations features. Furthermore, our incorporates explainability elucidate important biomarkers extract interaction between quantities interest. demonstrate effectiveness approach by applying it Alzheimer’s disease (AD) ROSMAP cohort, showing that transcriptomics proteomics AD domain network improves prediction accuracy status highlights functional biomarkers.

Language: Английский

Neuroinflammatory Loop in Schizophrenia, Is There a Relationship with Symptoms or Cognition Decline? DOI Open Access
Claudio Carril Pardo, Karina Oyarce, América Vera-Montecinos

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(1), P. 310 - 310

Published: Jan. 1, 2025

Schizophrenia (SZ), a complex psychiatric disorder of neurodevelopment, is characterised by range symptoms, including hallucinations, delusions, social isolation and cognitive deterioration. One the hypotheses that underlie SZ related to inflammatory events which could be partly responsible for symptoms. However, it unknown how molecules can contribute decline in SZ. This review summarises exposes possible contribution imbalance between pro-inflammatory anti-inflammatory interleukins like IL-1beta, IL-4 TNFalfa among others on impairment. We discuss this affects microglia astrocytes inducing disruption blood–brain barrier (BBB) SZ, impact prefrontal cortex or associative areas involved executive functions such as planning working tasks. also highlight generated intestinal microbiota alterations, due dysfunctional microbial colonisers use some anti-psychotics, central nervous system. Finally, question arises whether modulate correct characterises if an immunomodulatory strategy incorporated into conventional clinical treatments, either alone complement, applied specific phases, prodromal first-episode psychosis.

Language: Английский

Citations

1
Revealing the Oxidative Stress-Related Molecular Characteristics and Potential Therapeutic Targets of Schizophrenia through Integrated Gene Expression Data Analysis DOI

Xiumei Zhu,

Xi Chen,

Huajie Ba

et al.

Molecular Neurobiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

Citations

0
A Multi-omics approach to identify and validate shared genetic architecture in rheumatoid arthritis, multiple sclerosis, and type 1 diabetes: integrating GWAS, GEO, MSigDB, and scRNA-seq data DOI Creative Commons

Tailin Wang,

Qian He, Kei Hang Katie Chan

et al.

Functional & Integrative Genomics, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 21, 2025

Language: Английский

Citations

0
Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks DOI Creative Commons
Rohit Tripathy,

Zachary Frohock,

Hong Wang

et al.

npj Systems Biology and Applications, Journal Year: 2025, Volume and Issue: 11(1)

Published: May 8, 2025

Abstract The rapid growth of multi-omics datasets and the wealth biological knowledge necessitates development effective methods for their integration. Such are essential building predictive models identifying drug targets based on a limited number samples. We propose framework called GNNRAI supervised integration data with priors represented as graphs. Our leverages graph neural networks (GNNs) to model correlation structures among features from high-dimensional ‘omics data, which reduces dimensions in enables us analyze thousands genes simultaneously using hundreds Furthermore, our incorporates explainability elucidate informative biomarkers. apply Alzheimer’s disease (AD) showing that transcriptomics proteomics prior AD is effective, improving prediction accuracy status over single-omics analyses highlighting both known novel AD-predictive

Language: Английский

Citations

0
Novel Insights into Psychosis and Antipsychotic Interventions: From Managing Symptoms to Improving Outcomes DOI Open Access
Adonis Sfera,

Hassan Imran,

Dan O. Sfera

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(11), P. 5904 - 5904

Published: May 28, 2024

For the past 70 years, dopamine hypothesis has been key working model in schizophrenia. This contributed to development of numerous inhibitors dopaminergic signaling and antipsychotic drugs, which led rapid symptom resolution but only marginal outcome improvement. Over decades, there limited research on quantifiable pathological changes schizophrenia, including premature cellular/neuronal senescence, brain volume loss, attenuation gamma oscillations electroencephalograms, oxidation lipids plasma mitochondrial membranes. We surmise that aberrant activation aryl hydrocarbon receptor by toxins derived from gut microbes or environment drives cellular neuronal a hallmark Early aging promotes secondary changes, impairment loss mitochondria, gray matter depletion, decreased oscillations, compensatory metabolic shift lactate lactylation. The aim this narrative review is twofold: (1) summarize what known about senescence schizophrenia schizophrenia-like disorders, (2) discuss novel strategies for improving long-term outcomes severe mental illness with natural senotherapeutics, membrane lipid replacement, transplantation, microbial phenazines, antioxidant phenothiazines, glycogen synthase kinase-3 beta, antagonists.

Language: Английский

Citations

3
The Future of Antipsychotic Interventions: From Managing Symptoms to Improving Outcomes DOI Open Access
Adonis Sfera,

Hassan Imran,

Dan O Sfera

et al.

Published: March 28, 2024

Abstract For the past 70 years, dopamine hypothesis has been key working model in schizophrenia. This contributed to development of numerous inhibitors dopaminergic signaling, antipsychotic drugs, which led rapid symptom resolution but only marginal outcome improvement. Over decades, there was limited research on quantifiable pathological changes schizophrenia, including premature cellular/neuronal, senescence, brain volume loss, attenuation gamma oscillations electroencephalogram and oxidation lipids plasma mitochondrial membranes. We surmise that aberrant activation aryl hydrocarbon receptor by toxins derived from gut microbes or environment drives cellular, neuronal, a hallmark Early aging promotes secondary changes, impairment loss mitochondria, gray matter depletion, decreased oscillations, compensatory metabolic shift lactate lactylation. The aim this narrative review is twofold: 1. To summarize what known about cellular/neuronal senescence schizophrenia schizophrenia-like disorders. 2. discuss novel strategies for improving long-term severe mental illness with natural senotherapeutics, membrane lipid replacement, transplantation, microbial phenazines, antioxidant phenothiazines, glycogen synthase kinase-3 beta, receptor.

Language: Английский

Citations

0
An explainable graph neural network approach for integrating multi-omics data with prior knowledge to identify biomarkers from interacting biological domains DOI Creative Commons
Rohit Tripathy,

Zachary Frohock,

Hong Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract The rapid growth of multi-omics datasets, in addition to the wealth existing biological prior knowledge, necessitates development effective methods for their integration. Such are essential building predictive models and identifying disease-related molecular markers. We propose a framework supervised integration data with priors represented as knowledge graphs. Our leverages graph neural networks (GNNs) model relationships among features from high-dimensional ‘omics set transformers integrate low-dimensional representations features. Furthermore, our incorporates explainability elucidate important biomarkers extract interaction between quantities interest. demonstrate effectiveness approach by applying it Alzheimer’s disease (AD) ROSMAP cohort, showing that transcriptomics proteomics AD domain network improves prediction accuracy status highlights functional biomarkers.

Language: Английский

Citations

0