Researcher:
Mokhtaridoost, Milad

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PhD Student

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Milad

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Mokhtaridoost

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Mokhtaridoost, Milad

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2020 - 20223

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Researcher Of Publications: search.filters.isAuthorOfPublication.7b7d94f5-9dc5-4de1-adda-5268ca7473f0

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    Identifying key miRNA–mRNA regulatory modules in cancer using sparse multivariate factor regression
    (Springer Science and Business Media Deutschland GmbH, 2020) N/A; N/A; Department of Industrial Engineering; N/A; Mokhtaridoost, Milad; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; N/A; 237468
    The interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) are known to have a major effect on the formation and progression of cancer. In this study, we identified regulatory modules of 32 cancer types using a sparse multivariate factor regression model on matched miRNA and mRNA expression profiles of more than 9,000 primary tumors. We used an algorithm that decomposes the coefficient matrix into two low-rank matrices with separate sparsity-inducing penalty terms on each. The first matrix linearly transforms the predictors to a set of latent factors, and the second one regresses the responses using these factors. Our solution significantly outperformed another decomposition-based approach in terms of normalized root mean squared error in all 32 cohorts. We demonstrated the biological relevance of our results by performing survival and gene set enrichment analyses. The validation of overall results indicated that our solution is highly efficient for identifying key miRNA–mRNA regulatory modules.
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    PublicationOpen Access
    An efficient framework to identify key miRNA-mRNA regulatory modules in cancer
    (Oxford University Press (OUP), 2020) N/A; Department of Industrial Engineering; Mokhtaridoost, Milad; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine
    Motivation: micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA-mRNA regulatory modules. Results: we presented a two-step framework to model miRNA-mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supportedmiRTarBase database.
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    PublicationOpen Access
    Identifying tissue- and cohort-specific RNA regulatory modules in cancer cells using multitask learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2022) Maass, Philipp G.; N/A; Department of Industrial Engineering; Mokhtaridoost, Milad; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; N/A; 237468
    Understanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA-mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA-mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types (i.e., cohort-specific) and shared regulatory modules between cohorts (i.e., tissue-specific). MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA-mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA-mRNA regulatory modules separately. We tested the model's ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA-mRNA signatures.