Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

Summary: High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the difference...

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Bibliographic Details
Main Authors: Xin Bing, Tyler Lovelace, Florentina Bunea, Marten Wegkamp, Sudhir Pai Kasturi, Harinder Singh, Panayiotis V. Benos, Jishnu Das
Format: Article
Language:English
Published: Elsevier 2022-05-01
Series:Patterns
Subjects:
DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
Online Access:http://www.sciencedirect.com/science/article/pii/S2666389922000538

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http://www.sciencedirect.com/science/article/pii/S2666389922000538

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