Haematopoiesis is the process of blood cells’ formation, with progenitor stem cells differentiating into mature forms such as white and red blood cells or platelets. While progenitor cells share regulatory pathways involving common nuclear factors, specific networks shape their fate towards particular lineages. This paper analyses the complex regulatory network that drives the formation of mature red blood cells and platelets from their common precursors. Using the latest reverse transcription quantitative real-time PCR genomic data, we develop a dedicated graphical model that incorporates the effect of external genomic data and allows inference of regulatory networks from the high-dimensional and partially observed data.

Gianluca Sottile, L.A. (2024). Sparse inference of the human haematopoietic system from heterogeneous and partially observed genomic data. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 0, 1-25 [10.1093/jrsssc/qlae052].

Sparse inference of the human haematopoietic system from heterogeneous and partially observed genomic data

Gianluca Sottile
;
Luigi Augugliaro;
2024-10-14

Abstract

Haematopoiesis is the process of blood cells’ formation, with progenitor stem cells differentiating into mature forms such as white and red blood cells or platelets. While progenitor cells share regulatory pathways involving common nuclear factors, specific networks shape their fate towards particular lineages. This paper analyses the complex regulatory network that drives the formation of mature red blood cells and platelets from their common precursors. Using the latest reverse transcription quantitative real-time PCR genomic data, we develop a dedicated graphical model that incorporates the effect of external genomic data and allows inference of regulatory networks from the high-dimensional and partially observed data.
14-ott-2024
Settore STAT-01/A - Statistica
Gianluca Sottile, L.A. (2024). Sparse inference of the human haematopoietic system from heterogeneous and partially observed genomic data. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 0, 1-25 [10.1093/jrsssc/qlae052].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/661735
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