Integrative omics for health and disease

Karczewski, Konrad J.; Snyder, M

doi:10.1038/nrg.2018.4

Cited by 792 publications

(539 citation statements)

References 131 publications

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“…Third, we will review an important clinical application of proteogenomics -- cancer biology -- to demonstrate how this field in particular is poised to move towards truly integrative analysis of the massive data. We will focus on aspects that expand on other excellent reviews published lately that have discussed computational methods and human diseases in more detail [18][19][20][21][22][23][24][25]. …”

Section: Integrating Protein Knowledge Into Omics Analysesmentioning

confidence: 99%

Integration of large-scale multi-omic datasets: A protein-centric view

Rendleman

Choi

Vogel

2018

Current Opinion in Systems Biology

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Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

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Section: Integrating Protein Knowledge Into Omics Analysesmentioning

confidence: 99%

Integration of large-scale multi-omic datasets: A protein-centric view

Rendleman

Choi

Vogel

2018

Current Opinion in Systems Biology

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“…Hence, integrating and simultaneously analysing different data types offers better understanding of the mechanisms of a biological process and its intrinsic structure. Many studies have addressed and highlighted the importance of data integration at different scales (Žitnik et al, 2019;López de Maturana et al, 2019;Karczewski and Snyder, 2018;Huang et al, 2017;Gomez-Cabrero et al, 2014). In the context of analysing cancer data, it has been shown that such integrative approaches yield improved performance for accurate diagnosis, survival analysis and treatment planning (Vial et al, 2018;Gevaert et al, 2006;Thomas et al, 2014;Kristensen et al, 2014;Shen et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Variational autoencoders for cancer data integration: design principles and computational practice

Simidjievski

Bodnar

Tariq

et al. 2019

Preprint

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International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyse such data, several machine learning, bioinformatics and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyse multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.

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“…Additionally, it is now evident that complex phenotypes, such as disease, are not represented by the variability of a single data type (e.g. expression in tissue or GWAS results) but rather require the combination of a multitude of data types and resources that describe multiple aspects of the phenotype at different dimensions 1,2,3 . mantis-ml seeks to uncover any feature patterns among a collection of known positive-labelled disease-associated genes to then prioritise novel genes that share a highly similar feature profile with the known disease genes.…”

Section: Introductionmentioning

confidence: 99%

Stochastic semi-supervised learning to prioritise genes from high-throughput genomic screens

Vitsios

Petrovski

2019

Preprint

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Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses that result in candidate lists of genes. Often these analyses highlight several gene signals that might contribute to pathogenesis but are insufficiently powered to reach experiment-wide significance. This often triggers a process of laborious evaluation of highly-ranked genes through manual inspection of various public knowledge resources to triage those considered sufficiently interesting for deeper investigation. Here, we introduce a novel multi-dimensional, multi-step machine learning framework to objectively and more holistically assess biological relevance of genes to disease studies, by relying on a plethora of gene-associated annotations. We developed mantis-ml to serve as an automated machine learning (AutoML) framework, following a stochastic semisupervised learning approach to rank known and novel disease-associated genes through iterative training and prediction sessions of random balanced datasets across the protein-coding exome (n=18,626 genes). We applied this framework on a range of disease-specific areas and as a generic disease likelihood estimator, achieving an average Area Under Curve (AUC) prediction performance of 0.85. Critically, to demonstrate applied utility on exome-wide association studies, we overlapped mantis-ml disease-specific predictions with data from published cohort-level association studies. We retrieved statistically significant enrichment of high mantis-ml predictions among the top-ranked genes from hypothesis-free cohort-level statistics (p<0.05), suggesting the capture of true prioritisation signals. We believe that mantis-ml is a novel easy-to-use tool to support objectively triaging gene discovery and overall enhancing our understanding of complex genotype-phenotype associations.

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Integrative omics for health and disease

Cited by 792 publications

References 131 publications

Integration of large-scale multi-omic datasets: A protein-centric view

Integration of large-scale multi-omic datasets: A protein-centric view

Variational autoencoders for cancer data integration: design principles and computational practice

Stochastic semi-supervised learning to prioritise genes from high-throughput genomic screens

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