Classification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic data

Autor: Caroline Bazzoli, Sophie Lambert-Lacroix

Publikováno v: BMC Bioinformatics, Vol 19, Iss 1, Pp 1-13 (2018)

Abstract Background To address high-dimensional genomic data, most of the proposed prediction methods make use of genomic data alone without considering clinical data, which are often available and known to have predictive value. Recent studies sugge

Externí odkaz: https://doaj.org/article/9a84dced69eb4386acb4e201a27294a1

On Fractional Gaussian Random Fields Simulations

Autor: Alexandre Brouste, Jacques Istas, Sophie Lambert-Lacroix

Publikováno v: Journal of Statistical Software, Vol 23, Iss 1 (2007)

To simulate Gaussian fields poses serious numerical problems: storage and computing time. The midpoint displacement method is often used for simulating the fractional Brownian fields because it is fast. We propose an effective and fast method, valid

Externí odkaz: https://doaj.org/article/bf1ca8b72d494c228c538df1926474d4

Development of Indirect Health Data Linkage on Health Product Use and Care Trajectories in France: Systematic Review (Preprint)

Autor: Florence Ranchon, Sébastien Chanoine, Sophie Lambert-Lacroix, Jean-Luc Bosson, Alexandre Moreau-Gaudry, Pierrick Bedouch

BACKGROUND European national disparities in the integration of data linkage (ie, being able to match patient data between databases) into routine public health activities were recently highlighted. In France, the claims database covers almost the who

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::945deac2887e0bd2b2925a4b1790d338
https://doi.org/10.2196/preprints.41048

Random forest of perfect trees: concept, performance, applications and perspectives

Autor: Pascal Jézéquel, Sophie Lambert-Lacroix, Aurélie Gaultier, Faouda Ben Azzouz, Pierre Gillois, Jean-Michel Nguyen, Luisa Silva, Alexandre Moreau-Gaudry, Philippe Juin, Mario Campone, Daniel Antonioli

Publikováno v: Bioinformatics
Bioinformatics, 2021, 37 (15), pp.2165-2174. ⟨10.1093/bioinformatics/btab074⟩

Motivation The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman’s principles and involves b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad6667b006ec70861e990481c176117c
https://www.hal.inserm.fr/inserm-03546813

Classification based on extensions of LS-PLS using logistic regression: application toclinical and multiple genomic data

Autor: Caroline Bazzoli, Sophie Lambert-Lacroix

Publikováno v: BMC Bioinformatics
BMC Bioinformatics, 2018, 19 (1), ⟨10.1186/s12859-018-2311-2⟩
BMC Bioinformatics, BioMed Central, 2018, 19 (1), ⟨10.1186/s12859-018-2311-2⟩
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-13 (2018)

International audience; Prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical data that often are

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1671723e1614f7a0d0800c192bd823b
https://hal.science/hal-01405101v3/file/Bazzoli_et_al-2018-BMC_Bioinformatics.pdf