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      PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

      research-article
      1 , 1 , 2 , 3 ,
      BMC Bioinformatics
      BioMed Central
      Deep learning, Patient clustering, Multi-omics

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          Abstract

          Background

          Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources.

          Results

          We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters.

          Conclusions

          Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.

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          Most cited references27

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Objective Criteria for the Evaluation of Clustering Methods

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              Is Open Access

              Circulating microRNAs in breast cancer: novel diagnostic and prognostic biomarkers

              Effective management of breast cancer depends on early diagnosis and proper monitoring of patients’ response to therapy. However, these goals are difficult to achieve because of the lack of sensitive and specific biomarkers for early detection and for disease monitoring. Accumulating evidence in the past several years has highlighted the potential use of peripheral blood circulating nucleic acids such as DNA, mRNA and micro (mi)RNA in breast cancer diagnosis, prognosis and for monitoring response to anticancer therapy. Among these, circulating miRNA is increasingly recognized as a promising biomarker, given the ease with which miRNAs can be isolated and their structural stability under different conditions of sample processing and isolation. In this review, we provide current state-of-the-art of miRNA biogenesis, function and discuss the advantages, limitations, as well as pitfalls of using circulating miRNAs as diagnostic, prognostic or predictive biomarkers in breast cancer management.
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                Author and article information

                Contributors
                frohlich@bit.uni-bonn.de
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                16 April 2020
                16 April 2020
                2020
                : 21
                : 146
                Affiliations
                [1 ]ISNI 0000 0004 4655 0235, GRID grid.473748.b, Computer Science Department, , University of Constantine 2, ; 25016 Constantine, Algeria
                [2 ]University of Bonn, Bonn-Aachen, International Center for IT, 53115 Bonn, Germany
                [3 ]ISNI 0000 0000 9730 7658, GRID grid.466709.a, Fraunhofer Institute for, Algorithms and Scientific, Computing (SCAI), ; 53754 Sankt, Augustin Germany
                Author information
                http://orcid.org/0000-0002-5328-1243
                Article
                3465
                10.1186/s12859-020-3465-2
                7161108
                32299344
                8045e9fd-5d7b-4387-a171-7574001797ab
                © The Author(s). 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 13 November 2019
                : 23 March 2020
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2020

                Bioinformatics & Computational biology
                deep learning,patient clustering,multi-omics
                Bioinformatics & Computational biology
                deep learning, patient clustering, multi-omics

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