Improved comprehensibility and reliability of explanations via restricted halfspace discretization

Abstract. A number of two-class classification methods first discretize each attribute of two given training sets and then construct a propositional DNF formula that evaluates to True for one of the two discretized training sets and to False for the other one. The formula is not just a classification tool but constitutes a useful explanation for the differences between the two underlying populations if it can be comprehended by humans and is reliable. This paper shows that comprehensibility as well as reliability of the formulas can sometimes be improved using a discretization scheme where linear combinations of a small number of attributes are discretized

Viral Apoptosis Evasion via the MAPK Pathway by Use of a Host Long Noncoding RNA

An emerging realization of infectious disease is that pathogens can cause a high incidence of genetic instability within the host as a result of infection-induced DNA lesions. These often lead to classical hallmarks of cancer, one of which is the ability to evade apoptosis despite the presence of numerous genetic mutations that should be otherwise lethal. The Human Immunodeficiency Virus type 1 (HIV-1) is one such pathogen as it induces apoptosis in CD4+ T cells but is largely non-cytopathic in macrophages. As a consequence there is long-term dissemination of the pathogen specifically by these infected yet surviving host cells. Apoptosis is triggered by double-strand breaks (DSBs), such as those induced by integrating retroviruses like HIV-1, and is coordinated by the p53-regulated long noncoding RNA lincRNA-p21. As is typical for a long noncoding RNA, lincRNA-p21 mediates its activities in a complex with one of its two protein binding partners, namely HuR and hnRNP-K. In this work, we monitor the cellular response to infection to determine how HIV-1 induces DSBs in macrophages yet evades apoptosis in these cells. We show that the virus does so by securing the pro-survival MAP2K1/ERK2 cascade early upon entry, in a gp120-dependent manner, to orchestrate a complex dysregulation of lincRNA-p21. By sequestering the lincRNA-p21 partner HuR in the nucleus, HIV-1 enables lincRNA-p21 degradation. Simultaneously, the virus permits transcription of pro-survival genes by sequestering lincRNA-p21's other protein partner hnRNP-K in the cytoplasm via the MAP2K1/ERK2 pathway. Of particular note, this MAP2K1/ERK2 pro-survival cascade is switched off during T cell maturation and is thus unavailable for similar viral manipulation in mature CD4+ T cells. We show that the introduction of MAP2K1, ERK2, or HDM2 inhibitors in HIV-infected macrophages results in apoptosis, providing strong evidence that the viral-mediated apoptotic block can be released, specifically by restoring the nuclear interaction of lincRNA-p21 and its apoptosis protein partner hnRNP-K. Together, these results reveal a unique example of pathogenic control over mammalian apoptosis and DNA damage via a host long noncoding RNA, and present MAP2K1/ERK2 inhibitors as a novel therapeutic intervention strategy for HIV-1 infection in macrophages

Internet of Things for Sustainable Mining

The sustainable mining Internet of Things deals with the applications of IoT technology to the coupled needs of sustainable recovery of metals and a healthy environment for a thriving planet. In this chapter, the IoT architecture and technology is presented to support development of a digital mining platform emphasizing the exploration of rock–fluid–environment interactions to develop extraction methods with maximum economic benefit, while maintaining and preserving both water quantity and quality, soil, and, ultimately, human health. New perspectives are provided for IoT applications in developing new mineral resources, improved management of tailings, monitoring and mitigating contamination from mining. Moreover, tools to assess the environmental and social impacts of mining including the demands on dwindling freshwater resources. The cutting-edge technologies that could be leveraged to develop the state-of-the-art sustainable mining IoT paradigm are also discussed

Illustration d'une méthode d'évaluation supervisée par un problème de classification de courbes

@inproceedings{CN-FERRANDIZ-2006-2, author = {S. Ferrandiz and M. Boullé}, title = {Illustration d'une méthode d'évaluation supervisée par un problème de classification de courbes}, booktitle = {Rencontres de la société francophone de classification}, address = {Metz, France}, year = {2006} }National audienc

Predictive K-means with local models

Supervised classification can be effective for prediction but sometimes weak on interpretability or explainability (XAI). Clustering, on the other hand, tends to isolate categories or profiles that can be meaningful but there is no guarantee that they are useful for labels prediction. Predictive clustering seeks to obtain the best of the two worlds. Starting from labeled data, it looks for clusters that are as pure as possible with regards to the class labels. One technique consists in tweaking a clustering algorithm so that data points sharing the same label tend to aggregate together. With distance-based algorithms, such as k-means, a solution is to modify the distance used by the algorithm so that it incorporates information about the labels of the data points. In this paper, we propose another method which relies on a change of representation guided by class densities and then carries out clustering in this new representation space. We present two new algorithms using this technique and show on a variety of data sets that they are competitive for prediction performance with pure supervised classifiers while offering interpretability of the clusters discovered

Application d'une méthode d'évaluation d'une métrique à la préparation de données séquentielles

@inproceedings{CN-FERRANDIZ-2007, author = {S. Ferrandiz and M. Boullé}, title = {Application d'une méthode d'évaluation d'une métrique à la préparation de données séquentielles}, booktitle = {Actes des 7èmes journées francophones extraction et gestion des connaissances ({EGC}'07)}, publisher = {Cépaduès-Editions}, address = {Namur, Belgique}, year = {2007}, month = {janvier}, pages = {319-330} }National audienc

Supervised selection of dynamic features, with an application to telecommunication data preparation

@inproceedings{CI-FERRANDIZ-2006, author = {S. Ferrandiz and M. Boullé}, title = {Supervised selection of dynamic features, with an application to telecommunication data preparation}, booktitle = {Proceedings of the 6th industrial conference on data mining}, series = {Lecture notes in artificial intelligence}, publisher = {Springer Verlag}, volume = {4065}, pages = {239-249}, address = {Leipzig, Allemagne}, year = {2006}, month = {July} }International audienc

Sélection non paramétrique et régularisée d'instances et de variables

@inproceedings{CN-FERRANDIZ-2006-3, author = {S. Ferrandiz and M. Boullé}, title = {Sélection non paramétrique et régularisée d'instances et de variables}, booktitle = {Actes de la conférence francophone sur l'apprentissage automatique (CAp'06)}, address = {Trégastel, France}, publisher = {Presses Universitaires de Grenoble}, pages = {385-386}, year = {2006} }National audienc