WorldCat Identities

Malod-Dognin, Noël

Overview
Works: 8 works in 11 publications in 1 language and 12 library holdings
Roles: Author
Publication Timeline
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Most widely held works by Noël Malod-Dognin
Towards a data-integrated cell by Noël Malod-Dognin( )

2 editions published in 2019 in English and held by 3 WorldCat member libraries worldwide

We are increasingly accumulating molecular data about a cell. The challenge is how to integrate them within a unified conceptual and computational framework enabling new discoveries. Hence, we propose a novel, data-driven concept of an integrated cell, iCell. Also, we introduce a computational prototype of an iCell, which integrates three omics, tissue-specific molecular interaction network types. We construct iCells of four cancers and the corresponding tissue controls and identify the most rewired genes in cancer. Many of them are of unknown function and cannot be identified as different in cancer in any specific molecular network. We biologically validate that they have a role in cancer by knockdown experiments followed by cell viability assays. We find additional support through Kaplan-Meier survival curves of thousands of patients. Finally, we extend this analysis to uncover pan-cancer genes. Our methodology is universal and enables integrative comparisons of diverse omics data over cells and tissues
Precision medicine - A promising, yet challenging road lies ahead by Noël Malod-Dognin( )

2 editions published in 2018 in English and held by 2 WorldCat member libraries worldwide

Precision medicine proposes to individualize the practice of medicine based on patients' genetic backgrounds, their biomarker characteristics and other omics datasets. After outlining the key challenges in precision medicine, namely patient stratification, biomarker discovery and drug repurposing, we survey recent developments in high-throughput technologies and big biological datasets that shape the future of precision medicine. Furthermore, we provide an overview of recent data-integrative approaches that have been successfully used in precision medicine for mining medical knowledge from big-biological data, and we highlight modeling and computing issues that such integrative approaches will face due to the ever-growing nature of big-biological data. Finally, we raise attention to the challenges in translational medicine when moving from research findings to approved medical practices
Author Correction: Towards a data-integrated cell by Noël Malod-Dognin( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Towards Structural Classification of Proteins based on Contact Map Overlap by Rumen Andonov( Book )

1 edition published in 2007 in English and held by 1 WorldCat member library worldwide

Protein structure comparison : from contact map overlap maximisation to distance-based alignment search tool by Noël Malod-Dognin( Book )

2 editions published in 2010 in English and held by 1 WorldCat member library worldwide

In molecular biology, a fruitful assumption is that proteins sharing close three dimensional structures may share a common function and in most cases derive from a same ancestor. Computing the similarity between two protein structures is therefore a crucial task and has been extensively investigated. Among all the proposed methods, we focus on the similarity measure called Contact Map Overlap maximisation (CMO), mainly because it provides scores which can be used for obtaining good automatic classifications of the protein structures. In this thesis, comparing two protein structures is modelled as finding specific sub-graphs in specific k-partite graphs called alignment graphs. Then, we model CMO as a kind of maximum edge induced sub-graph problem in alignment graphs, for which we conceive an exact solver which outperforms the other CMO algorithms from the literature. Even though we succeeded to accelerate CMO, the procedure still stays too much time consuming for large database comparisons. To further accelerate CMO, we propose a hierarchical approach for CMO which is based on the secondary structure of the proteins. Finally, although CMO is a very good scoring scheme, the alignments it provides frequently posses big root mean square deviation values. To overcome this weakness, we propose a new comparison method based on internal distances which we call DAST (for Distance-based Alignment Search Tool). It is modelled as a maximum clique problem in alignment graphs, for which we design a dedicated solver with very good performances
Unveiling new disease, pathway, and gene associations via multi-scale neural network( )

1 edition published in 2020 in English and held by 1 WorldCat member library worldwide

Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these profiles, improving our ability to diagnose and assess disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we consider differential gene expressions obtained by microarray technology for patients diagnosed with various diseases. Based on these data and cellular multi-scale organization, we aim at uncovering disease-disease, disease-gene and disease-pathway associations. We propose a neural network with structure based on the multi-scale organization of proteins in a cell into biological pathways. We show that this model is able to correctly predict the diagnosis for the majority of patients. Through the analysis of the trained model, we predict disease-disease, disease-pathway, and disease-gene associations and validate the predictions by comparisons to known interactions and literature search, proposing putative explanations for the predictions
GR-Align : fast and flexible alignment of protein 3D structures using graphlet degree similarity by Noël Malod-Dognin( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Motivation: Protein structure alignment is key for transferring information from well-studied proteins to less studied ones. Structural alignment identifies the most precise mapping of equivalent residues, as structures are more conserved during evolution than sequences. Among the methods for aligning protein structures, maximum Contact Map Overlap (CMO) has received sustained attention during the past decade. Yet, known algorithms exhibit modest performance and are not applicable for large-scale comparison. Results: Graphlets are small induced subgraphs that are used to design sensitive topological similarity measures between nodes and networks. By generalizing graphlets to ordered graphs, we introduce GR-Align, a CMO heuristic that is suited for database searches. On the Proteus_300 set (44 850 protein domain pairs), GR-Align is several orders of magnitude faster than the state-of-the-art CMO solvers Apurva, MSVNS and AlEigen7, and its similarity score is in better agreement with the structural classification of proteins. On a large-scale experiment on the Gold-standard benchmark dataset (3 207 270 protein domain pairs), GR-Align is several orders of magnitude faster than the state-of-the-art protein structure comparison tools TM-Align, DaliLite, MATT and Yakusa, while achieving similar classification performances. Finally, we illustrate the difference between GR-Align%s flexible alignments and the traditional ones by querying a flexible protein in the Astral-40 database (11 154 protein domains). In this experiment, GR-Align%s top scoring alignments are not only in better agreement with structural classification of proteins, but also that they allow transferring more information across proteins
Omics data complementarity underlines functional cross-communication in yeast by Noël Malod-Dognin( )

1 edition published in 2017 in English and held by 1 WorldCat member library worldwide

Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker's yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein-protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data
 
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Alternative Names
Dognin, Noël Malod-

Languages
English (11)