A multi-omics data integration approach to identify a predictive molecular signature of CLAD

Pellet, Johann; Lefaudeux, Diane; Royer, Pierre-Joseph; Koutsokera, Angela; Bourgoin-Voillard, Sandrine; Schmitt, Markus; Trocmé, Candice; Claustre, Johanna; Fritz, Andreas; Sève, Michel; Magnan, A.; Pison, Christophe; Nicod, Laurent; Auffray, Charles

doi:10.1183/13993003.congress-2015.oa3271

Cited by 3 publications

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“…a Visualization of the topology of clinical data from the U-BIOPRED consortium adult severe asthma cohorts (courtesy of Ratko Djukanovic, University of Southampton, UK and Peter Sterk, Amsterdam Medical Center, The Netherlands) [ 126 ] using Topology Data Analysis from Ayasdi [ 127 , 128 ]. b Network obtained though integration of genome, transcriptome, and proteome data from the SysCLAD consortium lung transplantation cohorts (courtesy of Johann Pellet, EISBM, France) [ 129 , 130 ] using Ingenuity® Variant Analysis [ 131 ]. c Typical static representation of a molecular pathway in Thomson Reuters GeneGo MetaCore™ [ 132 ].…”

Section: Data Quality Acquisition Curation and Visualizationmentioning

confidence: 99%

Making sense of big data in health research: Towards an EU action plan

et al. 2016

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Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

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Section: Data Quality Acquisition Curation and Visualizationmentioning

confidence: 99%

Making sense of big data in health research: Towards an EU action plan

et al. 2016

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“…The diversity of these insults as well as the donor and recipient genetic burden assign each patient a unique clinical history. Hence, large-scale gene expression profiling 187,[196][197][198][199] or the powerful systems biology approach, [200][201][202][203] which integrates data sets of different nature, represents promising tools to decipher the complex network of factors involved in the development of CLAD.…”

Section: Discussionmentioning

confidence: 99%

Chronic Lung Allograft Dysfunction

et al. 2016

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Chronic lung allograft dysfunction (CLAD) is the major limitation of long-term survival after lung transplantation. Chronic lung allograft dysfunction manifests as bronchiolitis obliterans syndrome or the recently described restrictive allograft syndrome. Although numerous risk factors have been identified so far, the physiopathological mechanisms of CLAD remain poorly understood. We investigate here the immune mechanisms involved in the development of CLAD after lung transplantation. We explore the innate or adaptive immune reactions induced by the allograft itself or by the environment and how they lead to allograft dysfunction. Because current literature suggests bronchiolitis obliterans syndrome and restrictive allograft syndrome as 2 distinct entities, we focus on the specific factors behind one or the other syndromes. Chronic lung allograft dysfunction is a multifactorial disease that remains irreversible and unpredictable so far. We thus finally discuss the potential of systems-biology approach to predict its occurrence and to better understand its underlying mechanisms.

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“…The discrepancy among multi-omics data, therefore, provides an opportunity for detecting reliable and consistent signals for biological studies in a comprehensive manner. Multi-dimensional data integration and gene signature identification are among the most challenging tasks for bioinformaticians ( Li et al, 2019 ; Mallik and Zhao, 2020 ; Qiu et al, 2020 ; Pellet et al, 2015 ; Serra et al, 2015 ). Mallik et al (2017 ) proposed a scheme to recognize epigenetic biomarkers applying maximal relevance and minimal redundancy-based feature selection for multi-omics data.…”

Section: Introductionmentioning

confidence: 99%

“… Qiu et al (2020 ) suggested an approach regarding the revelation of 172 osteoporosis biomarkers by multi-omics data integration. A scheme of multi-omics data integration was presented by Pellet et al (2015 ) to determine predictive molecular signatures regarding CLAD. Because specific profiles contain different characteristics/phenomena, integration of multi-view data with significant feature reduction and gene signature detection is fundamentally important.…”

Section: Introductionmentioning

confidence: 99%

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection

et al. 2023

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In this current era, biomedical big data handling is a challenging task. Interestingly, the integration of multi-modal data, followed by significant feature mining (gene signature detection), becomes a daunting task. Remembering this, here, we proposed a novel framework, namely, three-factor penalized, non-negative matrix factorization-based multiple kernel learning with soft margin hinge loss (3PNMF-MKL) for multi-modal data integration, followed by gene signature detection. In brief, limma, employing the empirical Bayes statistics, was initially applied to each individual molecular profile, and the statistically significant features were extracted, which was followed by the three-factor penalized non-negative matrix factorization method used for data/matrix fusion using the reduced feature sets. Multiple kernel learning models with soft margin hinge loss had been deployed to estimate average accuracy scores and the area under the curve (AUC). Gene modules had been identified by the consecutive analysis of average linkage clustering and dynamic tree cut. The best module containing the highest correlation was considered the potential gene signature. We utilized an acute myeloid leukemia cancer dataset from The Cancer Genome Atlas (TCGA) repository containing five molecular profiles. Our algorithm generated a 50-gene signature that achieved a high classification AUC score (viz., 0.827). We explored the functions of signature genes using pathway and Gene Ontology (GO) databases. Our method outperformed the state-of-the-art methods in terms of computing AUC. Furthermore, we included some comparative studies with other related methods to enhance the acceptability of our method. Finally, it can be notified that our algorithm can be applied to any multi-modal dataset for data integration, followed by gene module discovery.

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A multi-omics data integration approach to identify a predictive molecular signature of CLAD

Cited by 3 publications

References 0 publications

Making sense of big data in health research: Towards an EU action plan

Making sense of big data in health research: Towards an EU action plan

Chronic Lung Allograft Dysfunction

3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection

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