A multi-context feature learning approach to identify disease-specific gene neighborhoods

Schizophrenia (SCZ) is a severe psychiatric disorder with several clinical manifestations that include cognitive dysfunction, decline in motivation, and psychosis. Current standards of care treatment with antipsychotic agents are often ineffective in controlling the disease, as only one-third of SCZ patients respond to medications. The mechanisms underlying the pathogenesis of SCZ remain elusive. It is believed that inflammatory processes may play a role as contributing factors to the etiology of SCZ. Galectins are a family of β-galactoside-binding lectins that contribute to the regulation of immune and inflammatory responses, and previous reports have shown their role in the maintenance of central nervous system (CNS) homeostasis and neuroinflammation. In the current study, we evaluated the expression levels of the galectin gene family in post-mortem samples of the hippocampus, associative striatum, and dorsolateral prefrontal cortex from SCZ patients. We found a significant downregulation of LGALS8 (Galectin-8) in the hippocampus of SCZ patients as compared to otherwise healthy donors. Interestingly, the reduction of LGALS8 was disease-specific, as no modulation was observed in the hippocampus from bipolar nor major depressive disorder (MDD) patients. Prediction analysis identified TBL1XR1, BRF2, and TAF7 as potential transcription factors controlling LGALS8 expression. In addition, MIR3681HG and MIR4296 were negatively correlated with LGALS8 expression, suggesting a role for epigenetics in the regulation of LGALS8 levels. On the other hand, no differences in the methylation levels of LGALS8 were observed between SCZ and matched control hippocampus. Finally, ontology analysis of the genes negatively correlated with LGALS8 expression identified an enrichment of the NGF-stimulated transcription pathway and of the oligodendrocyte differentiation pathway. Our study identified LGALS8 as a disease-specific gene, characterizing SCZ patients, that may in the future be exploited as a potential therapeutic target.

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Analysis of Significance Patterns Identifies Ubiquitous and Disease-Specific Gene-Expression Signatures in Patient Peripheral Blood Leukocytes

Annals of the New York Academy of Sciences ◽

10.1196/annals.1358.017 ◽

2005 ◽

Vol 1062 (1) ◽

pp. 146-154 ◽

Cited By ~ 38

Author(s):

D. CHAUSSABEL

Keyword(s):

Gene Expression ◽

Peripheral Blood ◽

Specific Gene ◽

Peripheral Blood Leukocytes ◽

Blood Leukocytes ◽

Specific Gene Expression ◽

Gene Expression Signatures ◽

Disease Specific

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A Spatio-temporal Feature Learning Approach for Dynamic Scene Recognition

Lecture Notes in Computer Science - Pattern Recognition and Machine Intelligence ◽

10.1007/978-3-319-69900-4_75 ◽

2017 ◽

pp. 591-598 ◽

Cited By ~ 1

Author(s):

Ihsan Ullah ◽

Alfredo Petrosino

Keyword(s):

Feature Learning ◽

Scene Recognition ◽

Learning Approach ◽

Dynamic Scene ◽

Spatio Temporal ◽

Temporal Feature

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NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

10.1101/2020.04.01.019901 ◽

2020 ◽

Author(s):

Zihu Guo ◽

Yingxue Fu ◽

Chao Huang ◽

Chunli Zheng ◽

Ziyin Wu ◽

...

Keyword(s):

Gene Networks ◽

Drug Repositioning ◽

High Reliability ◽

Rapid Development ◽

Small Neighborhood ◽

Specific Gene ◽

Master Genes ◽

Disease Initiation ◽

Disease Specific

AbstractRapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting new drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

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