Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

"Object Categorization: Reversals and Explanations of the Basic-Level Advantage" (Rogers & Patterson, 2007): A simplicity account

T. T. Rogers and K. Patterson (2007), in their article “Object Categorization: Reversals and Explanations of the Basic-Level Advantage” (Journal of Experimental Psychology: General, 136, 451–469), reported an impressive set of results demonstrating a reversal of the highly robust basic-level advantage both in patients with semantic dementia and in healthy individuals engaged in a speeded categorization task. To explain their results, as well as the usual basic-level advantage seen in healthy individuals, the authors employed a parallel distributed processing theory of conceptual knowledge. In this paper, we introduce an alternative way of explaining the results of Rogers and Patterson, which is premised on a more restricted set of assumptions born from standard categorization theory. Specifically, we provide evidence that their results can be accounted for based on the predictions of the simplicity model of unsupervised categorization

A Memetic Algorithm for Community Detection in Complex Networks

Community detection is an important issue in the field of complex networks. Modularity is the most popular partition-based measure for community detection of networks represented as graphs. We present a hybrid algorithm mixing a dedicated crossover operator and a multi-level local optimization procedure. Experimental evaluations on a set of 11 well-known benchmark graphs show that the proposed algorithm attains easily all the current best solutions and even improves 6 of them in terms of maximum modularity

Knowledge discovery with CRF-based clustering of named entities without a priori classes

International audienceKnowledge discovery aims at bringing out coherent groups of entities. It is usually based on clustering which necessitates defining a notion of similarity between the relevant entities. In this paper, we propose to divert a supervised machine learning technique (namely Conditional Random Fields, widely used for supervised labeling tasks) in order to calculate, indirectly and without supervision, similarities among text sequences. Our approach consists in generating artificial labeling problems on the data to reveal regularities between entities through their labeling. We describe how this framework can be implemented and experiment it on two information extraction/discovery tasks. The results demonstrate the usefulness of this unsupervised approach, and open many avenues for defining similarities for complex representations of textual data

Information Theoretic Criteria for Community Detection

Many algorithms for finding community structure in graphs search for a partition that maximizes modularity. However, recent work has identified two important limitations of modularity as a community quality criterion: are solution limit; and a bias towards finding equal-sized communities. Information-theoretic approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two information-theoretic algorithms are themselves subject to a resolution limit, identifies the component of each approach that is responsible for the resolution limit, proposes a variant, SGE (Sparse Graph Encoding), that addresses this limitation, and demonstrates on three artificial data sets that (1) SGE does not exhibit a resolution limit on sparse graphs in which other approaches do, and that (2) modularity and the compression-based algorithms, including SGE, behave similarly on graphs not subject to the resolution limit

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Effect of aspiration of the preovulatory follicle on luteinization, corpus luteum function, and peripheral plasma gonadotropin concentrations in the mare

International audienc

Effect of aspiration of the preovulatory follicle on luteinization, corpus luteum function, and peripheral plasma gonadotropin concentrations in the mare

International audienc