An integrated approach to uncover drivers of cancer.
Uri David Akavia,Oren Litvin,Jessica J. Kim,Jessica J. Kim,Felix Sanchez-Garcia,Dylan Kotliar,Helen C. Causton,Panisa Pochanard,Panisa Pochanard,Eyal Mozes,Levi A. Garraway,Levi A. Garraway,Dana Pe'er +12 more
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TLDR
A computational framework is developed that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression and correctly identified known drivers of melanoma and predicted multiple tumor dependencies.About:
This article is published in Cell.The article was published on 2010-12-10 and is currently open access. It has received 491 citations till now.read more
Citations
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Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET
Héctor Peinado,Maša Alečković,Simon Lavotshkin,Irina Matei,Bruno Costa-Silva,Gema Moreno-Bueno,Marta Hergueta-Redondo,Caitlin Williams,Guillermo García-Santos,Cyrus M. Ghajar,Ayuko Nitadori-Hoshino,Caitlin Hoffman,Karen Badal,Benjamin A. Garcia,Margaret K. Callahan,Jianda Yuan,Vilma R. Martins,Johan Skog,Rosandra N. Kaplan,Mary S. Brady,Jedd D. Wolchok,Jedd D. Wolchok,Paul B. Chapman,Paul B. Chapman,Yibin Kang,Yibin Kang,Jacqueline Bromberg,Jacqueline Bromberg,David Lyden +28 more
TL;DR: The data show that exosome production, transfer and education of bone marrow cells supports tumor growth and metastasis, has prognostic value and offers promise for new therapeutic directions in the metastatic process.
Journal ArticleDOI
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.
Jacob H. Levine,Erin F. Simonds,Sean C. Bendall,Kara L. Davis,El-ad David Amir,Michelle D. Tadmor,Oren Litvin,Harris G. Fienberg,Astraea Jager,Eli R. Zunder,Rachel Finck,Amanda Larson Gedman,Ina Radtke,James R. Downing,Dana Pe'er,Garry P. Nolan +15 more
TL;DR: Using hematopoietic progenitors, a signaling-based measure of cellular phenotype was defined, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts, yielding insights into AML pathophysiology.
Journal ArticleDOI
Methods of integrating data to uncover genotype–phenotype interactions
TL;DR: The emerging approaches for data integration — including meta-dimensional and multi-staged analyses — which aim to deepen the understanding of the role of genetics and genomics in complex outcomes are explored.
Journal ArticleDOI
Next-Generation Machine Learning for Biological Networks
Diogo M. Camacho,Katherine M. Collins,Rani K. Powers,James C. Costello,James J. Collins,James J. Collins,James J. Collins +6 more
TL;DR: A primer on machine learning for life scientists is provided, including an introduction to deep learning, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
Journal ArticleDOI
Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Ricard Argelaguet,Britta Velten,Damien Arnol,Sascha Dietrich,Thorsten Zenz,Thorsten Zenz,Thorsten Zenz,John C. Marioni,John C. Marioni,John C. Marioni,Florian Buettner,Wolfgang Huber,Oliver Stegle +12 more
TL;DR: Multi‐Omics Factor Analysis (MOFA) infers a set of (hidden) factors that capture biological and technical sources of variability that disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities.
References
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
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Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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Significance analysis of microarrays applied to the ionizing radiation response
TL;DR: A method that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements is described, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
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