DOP70 An integrated multi-omics biomarker predicting endoscopic response in ustekinumab treated patients with Crohn's disease

Verstockt, Bram; Sudahakar, P; Creyns, Brecht; Verstockt, Sare; Cremer, Jonathan; Wollants, W-J; Organe, Sophie; Korcsmáros, Tamás; Madgwick, Matthew; Assche, Gert Van; Breynaert, Christine; Vermeire, Séverine; Ferrante, Marc

doi:10.1093/ecco-jcc/jjy222.104

Cited by 9 publications

(6 citation statements)

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“…The results (factors) of MOFA are then used in a Cox-PH regression model and could predict the time to the next treatment with a reasonably high prediction accuracy (C-index∼75%). In the second and the third applications, MOFA is used to analyze Ustekinumab (UST) drug-response ( Verstockt et al, 2019 ) and mESCs (mouse embryonic stem cells) multi-omics data ( Argelaguet et al, 2018 ) to identify predictive factors (a combination of different Omics data).…”

Section: Unsupervised Multi-omics Data Integration Methodsmentioning

confidence: 99%

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Vahabi

Michailidis

2022

Front. Genet.

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Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

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Section: Unsupervised Multi-omics Data Integration Methodsmentioning

confidence: 99%

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Vahabi

Michailidis

2022

Front. Genet.

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“…10,43 A machine-learning model predicted endoscopic response to ustekinumab in patients with CD by integrating genomics and transcriptomics data. 44 The study identified 10-and 15-feature transcriptomic and genomic panels, respectively, that can predict endoscopic response to therapy. Additionally, multiomics profiling can identify proteomic, metabolomic, and microbial biomarkers associated with relapse in patients with quiescent IBD.…”

Section: Opportunities For Ai To Support Clinical Managementmentioning

confidence: 99%

Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions

Ahmad¹,

East²,

Panaccione³

et al. 2023

Intest Res

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biochemical investigations. [3][4][5] Endoscopic evaluation defines the location and pattern of inflammation (e.g., segmental or continuous, mild or severe) and can also identify noninflammatory pathology (e.g., dysplasia). 3 Endoscopic and histologic improve ments correlate with better outcomes and are treatment targets in the management of IBD. 6,7 Using an inflammatory pathway-based approach to inform treatment decisions may be more useful than classifying disease by organ involvement alone. 8 The complexity of IBD management lends itself to artificial intelligence (AI) as a means of improving clinical practice (Fig. 1). 3 Comprised of machine learning, deep learning, and neural networks, AI can help optimize IBD diagnosis, refine assessment of macroscopic and microscopic disease severity, and improve disease monitoring. [9][10][11]

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“…Multiomics data integration could prove useful in biomarker discovery for treatment response. Recently, our group identified 10-feature transcriptomic (accuracy of 98%) and 15-feature genomic (accuracy 96.6%) panels predicting endoscopic response to ustekinumab by incorporating genomics and transcriptomics data into a matrix factorisation-based machine learning model in patients with CD 112…”

Section: Current Paradigm Of Ibd Disease Management and Its Limitationsmentioning

confidence: 99%

Big data in IBD: big progress for clinical practice

Tabib

Madgwick

Sudhakar³

et al. 2020

Gut

131

108

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IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

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DOP70 An integrated multi-omics biomarker predicting endoscopic response in ustekinumab treated patients with Crohn's disease

Cited by 9 publications

References 0 publications

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions

Big data in IBD: big progress for clinical practice

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