Apprentissage de co-similarités pour la classification automatique de données monovues et multivues

L'apprentissage automatique consiste à concevoir des programmes informatiques capables d'apprendre à partir de leurs environnement, ou bien à partir de données. Il existe différents types d'apprentissage, selon que l'on cherche à faire apprendre au programme, et également selon le cadre dans lequel il doit apprendre, ce qui constitue différentes tâches. Les mesures de similarité jouent un rôle prépondérant dans la plupart de ces tâches, c'est pourquoi les travaux de cette thèse se concentrent sur leur étude. Plus particulièrement, nous nous intéressons à la classification de données, qui est une tâche d'apprentissage dit non supervisé, dans lequel le programme doit organiser un ensemble d'objets en plusieurs classes distinctes, de façon à regrouper les objets similaires ensemble. Dans de nombreuses applications, ces objets (des documents par exemple) sont décrits à l'aide de leurs liens à d'autres types d'objets (des mots par exemple), qui peuvent eux-même être classifiés. On parle alors de co-classification, et nous étudions et proposons dans cette thèse des améliorations de l'algorithme de calcul de co-similarités XSim. Nous montrons que ces améliorations permettent d'obtenir de meilleurs résultats que les méthodes de l'état de l'art. De plus, il est fréquent que ces objets soient liés à plus d'un autre type d'objets, les données qui décrivent ces multiples relations entre différents types d'objets sont dites multivues. Les méthodes classiques ne sont généralement pas capables de prendre en compte toutes les informations contenues dans ces données. C'est pourquoi nous présentons dans cette thèse l'algorithme de calcul multivue de similarités MVSim, qui peut être vu comme une extension aux données multivues de l'algorithme XSim. Nous montrons que cette méthode obtient de meilleures performances que les méthodes multivues de l'état de l'art, ainsi que les méthodes monovues, validant ainsi l'apport de l'aspect multivue. Finalement, nous proposons également d'utiliser l'algorithme MVSim pour classifier des données classiques monovues de grandes tailles, en les découpant en différents ensembles. Nous montrons que cette approche permet de gagner en temps de calcul ainsi qu'en taille mémoire nécessaire, tout en dégradant relativement peu la classification par rapport à une approche directe sans découpage.Machine learning consists in conceiving computer programs capable of learning from their environment, or from data. Different kind of learning exist, depending on what the program is learning, or in which context it learns, which naturally forms different tasks. Similarity measures play a predominant role in most of these tasks, which is the reason why this thesis focus on their study. More specifically, we are focusing on data clustering, a so called non supervised learning task, in which the goal of the program is to organize a set of objects into several clusters, in such a way that similar objects are grouped together. In many applications, these objects (documents for instance) are described by their links to other types of objects (words for instance), that can be clustered as well. This case is referred to as co-clustering, and in this thesis we study and improve the co-similarity algorithm XSim. We demonstrate that these improvements enable the algorithm to outperform the state of the art methods. Additionally, it is frequent that these objects are linked to more than one other type of objects, the data that describe these multiple relations between these various types of objects are called multiview. Classical methods are generally not able to consider and use all the information contained in these data. For this reason, we present in this thesis a new multiview similarity algorithm called MVSim, that can be considered as a multiview extension of the XSim algorithm. We demonstrate that this method outperforms state of the art multiview methods, as well as classical approaches, thus validating the interest of the multiview aspect. Finally, we also describe how to use the MVSim algorithm to cluster large-scale single-view data, by first splitting it in multiple subsets. We demonstrate that this approach allows to significantly reduce the running time and the memory footprint of the method, while slightly lowering the quality of the obtained clustering compared to a straightforward approach with no splitting.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Clustering Libraries of Compounds into Families: Asymmetry-Based Similarity Measure to Categorize Small Molecules

International audienceClustering Libraries of Compounds into Families: Asymmetry-Based Similarity Measure to Categorize Small Molecule

Seven-year experience of a primary care antiretroviral treatment programme in Khayelitsha, South Africa.

OBJECTIVES: We report on outcomes after 7 years of a community-based antiretroviral therapy (ART) programme in Khayelitsha, South Africa, with death registry linkages to correct for mortality under-ascertainment. DESIGN: This is an observational cohort study. METHODS: Since inception, patient-level clinical data have been prospectively captured on-site into an electronic patient information system. Patients with available civil identification numbers who were lost to follow-up were matched with the national death registry to ascertain their vital status. Corrected mortality estimates weighted these patients to represent all patients lost to follow-up. CD4 cell count outcomes were reported conditioned on continuous virological suppression. RESULTS: Seven thousand, three hundred and twenty-three treatment-naive adults (68% women) started ART between 2001 and 2007, with annual enrolment increasing from 80 in 2001 to 2087 in 2006. Of 9.8% of patients lost to follow-up for at least 6 months, 32.8% had died. Corrected mortality was 20.9% at 5 years (95% confidence interval 17.9-24.3). Mortality fell over time as patients accessed care earlier (median CD4 cell count at enrolment increased from 43 cells/microl in 2001 to 131 cells/microl in 2006). Patients who remained virologically suppressed continued to gain CD4 cells at 5 years (median 22 cells/microl per 6 months). By 5 years, 14.0% of patients had failed virologically and 12.2% had been switched to second-line therapy. CONCLUSION: At a time of considerable debate about future global funding of ART programmes in resource-poor settings, this study has demonstrated substantial and durable clinical benefits for those able to access ART throughout this period, in spite of increasing loss to follow-up

Correcting for Mortality Among Patients Lost to Follow Up on Antiretroviral Therapy in South Africa: A Cohort Analysis

Loss to follow-up (LTF) challenges the reporting of antiretroviral treatment (ART) programmes, since it encompasses patients alive but lost to programme and deaths misclassified as LTF. We describe LTF before and after correction for mortality in a primary care ART programme with linkages to the national vital registration system

In vitro and in vivo MMP gene expression localisation by In Situ-RT-PCR in cell culture and paraffin embedded human breast cancer cell line xenografts

BACKGROUND: Members of the matrix metalloproteinase (MMP) family of proteases are required for the degradation of the basement membrane and extracellular matrix in both normal and pathological conditions. In vitro, MT1-MMP (MMP-14, membrane type-1-MMP) expression is higher in more invasive human breast cancer (HBC) cell lines, whilst in vivo its expression has been associated with the stroma surrounding breast tumours. MMP-1 (interstitial collagenase) has been associated with MDA-MB-231 invasion in vitro, while MMP-3 (stromelysin-1) has been localised around invasive cells of breast tumours in vivo. As MMPs are not stored intracellularly, the ability to localise their expression to their cells of origin is difficult. METHODS: We utilised the unique in situ-reverse transcription-polymerase chain reaction (IS-RT-PCR) methodology to localise the in vitro and in vivo gene expression of MT1-MMP, MMP-1 and MMP-3 in human breast cancer. In vitro, MMP induction was examined in the MDA-MB-231 and MCF-7 HBC cell lines following exposure to Concanavalin A (Con A). In vivo, we examined their expression in archival paraffin embedded xenografts derived from a range of HBC cell lines of varied invasive and metastatic potential. Mouse xenografts are heterogenous, containing neoplastic human parenchyma with mouse stroma and vasculature and provide a reproducible in vivo model system correlated to the human disease state. RESULTS: In vitro, exposure to Con A increased MT1-MMP gene expression in MDA-MB-231 cells and decreased MT1-MMP gene expression in MCF-7 cells. MMP-1 and MMP-3 gene expression remained unchanged in both cell lines. In vivo, stromal cells recruited into each xenograft demonstrated differences in localised levels of MMP gene expression. Specifically, MDA-MB-231, MDA-MB-435 and Hs578T HBC cell lines are able to influence MMP gene expression in the surrounding stroma. CONCLUSION: We have demonstrated the applicability and sensitivity of IS-RT-PCR for the examination of MMP gene expression both in vitro and in vivo. Induction of MMP gene expression in both the epithelial tumour cells and surrounding stromal cells is associated with increased metastatic potential. Our data demonstrate the contribution of the stroma to epithelial MMP gene expression, and highlight the complexity of the role of MMPs in the stromal-epithelial interactions within breast carcinoma

Correcting Mortality for Loss to Follow-Up: A Nomogram Applied to Antiretroviral Treatment Programmes in Sub-Saharan Africa

Matthias Egger and colleagues present a nomogram and a web-based calculator to correct estimates of program-level mortality for loss to follow-up, for use in antiretroviral treatment programs

Superior virologic and treatment outcomes when viral load is measured at 3 months compared to 6 months on antiretroviral therapy.

INTRODUCTION: Routine viral load (VL) monitoring is utilized to assess antiretroviral therapy (ART) adherence and virologic failure, and it is currently scaled-up in many resource-constrained settings. The first routine VL is recommended as late as six months after ART initiation for early detection of sub-optimal adherence. We aimed to assess the optimal timing of first VL measurement after initiation of ART. METHODS: This was a retrospective, cohort analysis of routine monitoring data of adults enrolled at three primary care clinics in Khayelitsha, Cape Town, between January 2002 and March 2009. Primary outcomes were virologic failure and switch to second-line ART comparing patients in whom first VL done was at three months (VL3M) and six months (VL6M) after ART initiation. Adjusted hazard ratios (aHR) were estimated using Cox proportional hazard models. RESULTS: In total, 6264 patients were included for the time to virologic failure and 6269 for the time to switch to second-line ART analysis. Patients in the VL3M group had a 22% risk reduction of virologic failure (aHR 0.78, 95% CI 0.64-0.95; p=0.016) and a 27% risk reduction of switch to second-line ART (aHR 0.73, 95% CI 0.58-0.92; p=0.008) when compared to patients in the VL6M group. For each additional month of delay of the first VL measurement (up to nine months), the risk of virologic failure increased by 9% (aHR 1.09, 95% CI 1.02-1.15; p=0.008) and switch to second-line ART by 13% (aHR 1.13, 95% CI 1.05-1.21; p<0.001). CONCLUSIONS: A first VL at three months rather than six months with targeted adherence interventions for patients with high VL may improve long-term virologic suppression and reduce switches to costly second-line ART. ART programmes should consider the first VL measurement at three months after ART initiation