Analyse comparative des méthodes de classifications

Creusier, Jordane; Biétry, Franck

doi:10.3917/rimhe.010.0105

Cited by 11 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most widely used method is clustering, which aimed to group data into clusters. We will focus on two main clustering methods in the context of creating PPI networks ( Malouche, 2013 ; Creusier and Biétry, 2014 ).…”

Section: Methods Based On the Machine Learning Algorithmmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

show abstract

Section: Methods Based On the Machine Learning Algorithmmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…The first indices to be observed were the information criteria, more specifically the sample-size-adjusted Bayesian Information Criterion (BICssa), which indicates approximate fit indices, where lower values are indicative of superior fit (Nylund-Gibson & Choi, 2018). The adjusted Lo-Mendell-Rubin likelihood ratio test (LMR-LRT) was also used to select the optimal number of profiles, with a non-significant LMR-LRT indicating that a model with one fewer class is preferred (Creusier & Biétry, 2014). In cases where the BICssa and the LMR-LRT statistics did not agree, priority was given to the LMR-LRT.…”

Section: Data Analysesmentioning

confidence: 99%