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CAUSALPATH

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Next Generation Causal Analysis Inspired by the Induction of Biological Pathway...

Next Generation Causal Analysis Inspired by the Induction of Biological Pathways from Cytometry Data Discovering the causal mechanisms of a complex system of interacting components is necessary in order to control it. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations under certa... ver más

31/12/2019

UNIVERSITY OF CRET...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

PANEPISTIMIO KRITIS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2019-12-31 No tenemos la información de la convocatoria

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2 Participantes

UNIVERSITY OF CRETE

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Líder del proyecto

PANEPISTIMIO KRITIS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Discovering the causal mechanisms of a complex system of interacting components is necessary in order to control it. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations under certain conditions from observational data alone or with a limited number of interventions/manipulations. An important, challenging biological problem that may take decades of experimental work is the induction of biological cellular pathways; pathways are informal causal models indispensable in biological research and drug design. Recent exciting advances in flow/mass cytometry biotechnology allow the generation of large-sample datasets containing measurements on single cells, thus setting the problem of pathway learning suitable for CD methods. CAUSALPATH builds upon and further advances recent breakthrough developments in CD methods to enable the induction of biological pathways from cytometry and other omics data. As a testbed problem we focus on the differentiation of human T-cells; these are involved in autoimmune and inflammatory diseases, as well as cancer and thus, are targets of new drug development for a range of chronic diseases. The biological problem acts as our campus for general novel formalisms, practical algorithms, and useful tools development, pointing to fundamental CD problems: presence of feedback cycles, presence of latent confounding variables, CD from time-course data, Integrative Causal Analysis (INCA) of heterogeneous datasets and others. Three features complement CAUSALPATH’s approach: (A) methods development will co-evolve with biological wet-lab experiments periodically testing the algorithmic postulates, (B) Open-source tools will be developed for the non-expert, and (C) Commercial exploitation of the results will be sought out. CAUSALPATH brings together an interdisciplinary team, committed to this vision. It builds upon the PI’s group recent important results on INCA algorithms.

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