Dataset Information

Inferring phenotypes from substance use via collaborative matrix completion.

ABSTRACT: BACKGROUND:Although substance use disorders (SUDs) are heritable, few genetic risk factors for them have been identified, in part due to the small sample sizes of study populations. To address this limitation, researchers have aggregated subjects from multiple existing genetic studies, but these subjects can have missing phenotypic information, including diagnostic criteria for certain substances that were not originally a focus of study. Recent advances in addiction neurobiology have shown that comorbid SUDs (e.g., the abuse of multiple substances) have similar genetic determinants, which makes it possible to infer missing SUD diagnostic criteria using criteria from another SUD and patient genotypes through statistical modeling. RESULTS:We propose a new approach based on matrix completion techniques to integrate features of comorbid health conditions and individual's genotypes to infer unreported diagnostic criteria for a disorder. This approach optimizes a bi-linear model that uses the interactions between known disease correlations and candidate genes to impute missing criteria. An efficient stochastic and parallel algorithm was developed to optimize the model with a speed 20 times greater than the classic sequential algorithm. It was tested on 3441 subjects who had both cocaine and opioid use disorders and successfully inferred missing diagnostic criteria with consistently better accuracy than other recent statistical methods. CONCLUSIONS:The proposed matrix completion imputation method is a promising tool to impute unreported or unobserved symptoms or criteria for disease diagnosis. Integrating data at multiple scales or from heterogeneous sources may help improve the accuracy of phenotype imputation.

SUBMITTER: Lu J

PROVIDER: S-EPMC6249733 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Inferring phenotypes from substance use via collaborative matrix completion.

Lu Jin J Sun Jiangwen J Wang Xinyu X Kranzler Henry H Gelernter Joel J Bi Jinbo J

BMC systems biology 20181122 Suppl 6

<h4>Background</h4>Although substance use disorders (SUDs) are heritable, few genetic risk factors for them have been identified, in part due to the small sample sizes of study populations. To address this limitation, researchers have aggregated subjects from multiple existing genetic studies, but these subjects can have missing phenotypic information, including diagnostic criteria for certain substances that were not originally a focus of study. Recent advances in addiction neurobiology have sh ...[more]

PMID: 30463556

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Project description:Current studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in a variety of fundamental biological processes related to complex human diseases. The prediction of latent disease-lncRNA associations can help to understand the pathogenesis of complex human diseases at the level of lncRNA, which also contributes to the detection of disease biomarkers, and the diagnosis, treatment, prognosis and prevention of disease. Nevertheless, it is still a challenging and urgent task to accurately identify latent disease-lncRNA association. Discovering latent links on the basis of biological experiments is time-consuming and wasteful, necessitating the development of computational prediction models. In this study, a computational prediction model has been remodeled as a matrix completion framework of the recommendation system by completing the unknown items in the rating matrix. A novel method named faster randomized matrix completion for latent disease-lncRNA association prediction (FRMCLDA) has been proposed by virtue of improved randomized partial SVD (rSVD-BKI) on a heterogeneous bilayer network. First, the correlated data source and experimentally validated information of diseases and lncRNAs are integrated to construct a heterogeneous bilayer network. Next, the integrated heterogeneous bilayer network can be formalized as a comprehensive adjacency matrix which includes lncRNA similarity matrix, disease similarity matrix, and disease-lncRNA association matrix where the uncertain disease-lncRNA associations are referred to as blank items. Then, a matrix approximate to the original adjacency matrix has been designed with predicted scores to retrieve the blank items. The construction of the approximate matrix could be equivalently resolved by the nuclear norm minimization. Finally, a faster singular value thresholding algorithm with a randomized partial SVD combing a new sub-space reuse technique has been utilized to complete the adjacency matrix. The results of leave-one-out cross-validation (LOOCV) experiments and 5-fold cross-validation (5-fold CV) experiments on three different benchmark databases have confirmed the availability and adaptability of FRMCLDA in inferring latent relationships of disease-lncRNA pairs, and in inferring lncRNAs correlated with novel diseases without any prior interaction information. Additionally, case studies have shown that FRMCLDA is able to effectively predict latent lncRNAs correlated with three widespread malignancies: prostate cancer, colon cancer, and gastric cancer.

Project description:BackgroundReddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research.ObjectiveThis review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention.MethodsA total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions.ResultsThe use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources.ConclusionsThis systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research.

Project description:Contextual triggers are significant factors contributing to relapse in substance use disorders (SUD). Emerging evidence points to a critical role of extracellular matrix (ECM) molecules as mediators of reward memories. Chondroitin sulfate proteoglycans (CSPGs) are a subset of ECM molecules that form perineuronal nets (PNN) around inhibitory neurons. PNNs restrict synaptic connections and help maintain synapses. Rodent models suggest that modulation of PNNs may strengthen contextual reward memories in SUD. However, there is currently a lack of information regarding PNNs in the hippocampus of people with SUD as well as how comorbidity with major depressive disorder (MDD) may affect PNNs. We used postmortem hippocampal tissues from cohorts of human and nonhuman primates with or without chronic alcohol use to test the hypothesis that PNNs are increased in subjects with SUD. We used histochemical labeling and quantitative microscopy to examine PNNs, and qRT-PCR to examine gene expression for ECM molecules, synaptic markers and related markers. We identified increased densities of PNNs and CSPG-labeled glial cells in SUD, coinciding with decreased expression of the ECM protease matrix metalloproteinase 9 (Mmp9), and increased expression for the excitatory synaptic marker vesicle associated membrane protein 2 (Vamp2). Similar increases in PNNs were observed in monkeys with chronic alcohol self-administration. Subjects with MDD displayed changes opposite to SUD, and subjects with SUD and comorbid MDD had minimal changes in any of the outcome measures examined. Our findings demonstrate that PNNs are increased in SUD, possibly contributing to stabilizing contextual reward memories as suggested by preclinical studies. Our results also point to a previously unsuspected role for CSPG expression in glial cells in SUD. Evidence for increased hippocampal PNNs in SUD suggests that targeting PNNs to weaken contextual reward memories is a promising therapeutic approach for SUD, however comorbidity with MDD is a significant consideration.

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Inferring phenotypes from substance use via collaborative matrix completion.

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Inferring phenotypes from substance use via collaborative matrix completion.

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