Thomas PapastergiouUniversité de Montpellier | UM1 · Laboratory of Informatics, Robotics, and Microelectronics
Post-doc researcher on de-novo with Artificial Intelligence
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September 2017 - November 2018
- Research Associate
- University Research Associate within the European Project 54940000: Η2020-PHC-2014-2015 (690140): "FrailSafe: Sensing and predictive treatment of frailty and associated co-morbidities using advanced personalized patient models and advanced interventions", Principal Investigator: Prof. Vasileios Megalooikonomou.
October 2014 - July 2017
- Adjunct Computer Science Lecturer
- Teaching: 1. Introduction in Databases (Lectures) 2. Business Resource Management Systems (Lectures) 3. Financial and Administrative Applications with Spreadsheets (Lab.) 4. Introduction in Databases (Lab.) 5. Introduction to Accounting Information Systems (Lab.) 6. Business Resource Management Systems (Lab.)
NDM-1 (New-Delhi-Metallo-β-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classif...
In this paper, we first present a new dataset of NDM-1 biological activities that is compiled by a cleaned version of the NMDI database. A literature review enriched the former database by 741 new compounds, comprising activities against NDM-1 classified in three classes (inactive, weakly and strongly active compounds) by specifying a unifying proc...
Protein arginine methylation is an understudied epigenetic mechanism catalyzed by enzymes known as Protein Methyltransferases of Arginine (PRMTs), while the opposite reaction is performed by Jumonji domain- containing protein 6 (JMJD6). There is increasing evidence that PRMTs are deregulated in prostate cancer (PCa). In this study, the expression o...
Background Recently, the Patras Immunotherapy Score (PIOS) has been developed to estimate the survival benefit of patients with advanced non-small-cell lung cancer (aNSCLC) treated with nivolumab or pembrolizumab. The aim of this study was to validate the clinical value of PIOS in an external cohort of aNSCLC patients. Methods PIOS is a baseline f...
Lung cancer is the leading cause of cancer deaths nowadays and its early detection and treatment plays an important role in survival of patients. The main challenge is to acquire an accurate diagnosis in a limited time and without the need of massive computing power. Here, we propose SqueezeNodule-Net, a light and accurate convolutional neural netw...
e21164 Background: The treatment of advanced non-small cell lung cancer (aNSCLC) has tremendously changed during the last few years, especially, since immune checkpoint inhibitors (ICIs) were incorporated in the daily clinical practice. However, clinical useful biomarkers remain an unmet need. Recently, our group established and proposed a new scor...
In this dissertation, we propose a basic algorithm (GenProxSGD) for calculating a CANDECOMP/PARAFAC (CP) decomposition from partially observed data, which is based on the proximal operator. This algorithm deals with the decomposition’s optimization problem by solving local optimization problems, in the sense that in each iteration a proximal to the...
Multiple instance learning (MIL) has shown great potential in addressing weakly supervised problems in which class labels are provided for sets (bags) of instances. The main challenge in MIL comes from the lack of knowledge on the pertinence of each individual instance in class discrimination. In this paper we propose TensMIL2, a generic unsupervis...
As the amount of data increases, fully supervised learning methods relying on dense annotations often become impractical, and are substituted by weakly supervised methods , that exploit data with a variable content in respect to size and semantics. In such schemes the volume of irrelevant information might be critically high impacting negatively th...
Multidimensional data that occur in a variety of applications in clinical diagnostics and health care can naturally be represented by multidimensional arrays (i.e., tensors). Tensor decompositions offer valuable and powerful tools for latent concept discovery that can handle effectively missing values and noise. We propose a seamless, application-i...
Clustering consists in partitioning a set of objects into disjoint and homogeneous clusters. For many years, clustering methods have been applied in a wide variety of disciplines and they also have been utilized in many scientific areas. Traditionally, clustering methods deal with numerical data, i.e. objects represented by a conjunction of numeric...