Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise
Searched keyword : Graphics and Image Processing
Related people (5)
I have a joint MSc degree in Mathematical Modelling from three European universities: University of L’Aquila (Italy), University of Nice-Sophia Antipolis (France) and Autonomous University of Barcelona (Spain). I also hold a PhD degree in Applied Mathematics and Scientific Computing from University of Bordeaux, France. I have done my PhD and one year of post-doc at INRIA Bordeaux Sud-Ouest, and partially at IHU-Liryc. During this time I studied how electrical signals propagate through the cardiac tissue under certain diseased conditions. My model of interest was the bidomain model, which is a system of partial differential equations that takes into account physiological properties of the cardiac cells and the spatial organization of the cardiac tissue. I worked on the mathematical multiscale analysis and numerical simulations of the problem to understand how structural changes of the tissue affect the propagation of the signal on the heart level. I collaborated with biologists and engineers of the IHU-Liryc to apply my model on a rat heart using high-resolution MRI data. For this I also worked on image analysis and image processing. I’ve joined the Institute Pasteur in February 2018 as a member of the HUB in Bioinformatics and Biostatistics. Currently I am working on stochastic mathematical modeling and inference for systems biology, gene expression, RNA transcription, etc.
ModelingScientific computingApplication of mathematics in sciencesGraphics and Image Processing
BacteriaFungiInsect or arthropodEscherichia coliSaccharomyces cerevisiaeFly
BiophysicsGraphics and Image Processing
Bernd Jagla received his PhD in bioinformatics (department of Biology, Chemistry, and Parmacy) from the Free University in Berlin, Germany in 1999. Before joining the Institut Pasteur, he worked for almost ten years in New York City, including as an associate research scientist in the Joint Centers for System Biology (Columbia University) and at the Columbia University Screening Center led by Dr J.E. Rothman. He joined the Institut Pasteur in 2009 to take charge of the bioinformatic needs at the Transcriptome et Epigenome platform, focusing on Next Generation Sequencing. As of 2016 he is member of the C3BI – HUB Team detached to the Human immunology center (CIH) and provides support for cytometry, next generation sequencing, and microarray data analysis. His areas of interest include the quality assurance and data analysis and visualization at the facility. He also has strong expertise in developing algorithms for function prediction from sequence data, image analysis, analysis of mass spectrometry data, workflow management systems. While at Pasteur he developed: KNIME extensions for Next Generation Sequencing (Link) Post Alignment Visualization and Characterization of High-Throughput Sequencing Experiments (Link) Post Alignment statistics of Illumina reads (Link)
AlgorithmicsChIP-seqData managementData VisualizationImage analysisMachine learningSequence analysisDatabaseGenome analysisBiostatisticsProgram developmentScientific computingData and text miningIllumina HiSeqGraphics and Image ProcessingIllumina MiSeqHigh Throughput ScreeningFlow cytometry/cell sortingPac Bio
Image reconstruction, processing and analysis. Dingle molecule localization microscopy. Development of ImageJ and Fiji plugins.
Image analysisGraphics and Image Processing
Dr. Natalia Pietrosemoli is an Engineer with a M. Sc. in Modeling and Simulation of Complex Realities from the International Center for Theoretical Physics, ICTP and the International School of Advanced Studies, SISSA (Triest, Italy). During her M. Sc. internships she mostly worked in modeling, optimization, combinatorics and information theory applied to medical imaging. In 2012 she got a Ph. D in Computational Biology from the School of Bioengineering of Rice University (Houston, TX, US), where she specialized in computational structural biology and functional genomics. Her doctoral thesis “Protein functional features extracted with from primary sequences : a focus on disordered regions”, contributed to a better understanding of the functional and evolutionary role of intrinsic disorder in protein plasticity, complexity and adaptation to stress conditions. As part of her Ph. D., Natalia was a visiting scholar in two labs in Madrid: the Structural Computational Biology Group at the Spanish National Cancer Research Centre (CNIO), where she mainly worked in sequence analysis and the functional-structural relationships of proteins, and the Computational Systems Biology Group at the Spanish National Centre for Biotechnology (CNB-CSIC ), where she studied the functional implications of intrinsically disordered proteins at the genomic level for several organisms, collaborating with different experimental and theoretical groups. In 2013, she joined the Swiss Institute of Bioinformatics as a postdoctoral fellow in the Bioinformactics Core Facility. Her main project consisted in the molecular classification of a rare type of lymphoma, which involved the integration of transcriptomic, clinical and mutational data for the identification of molecular markers for classification, diagnosis and prognosis. This work was performed in collaboration with the Pathology Institute at the University Hospital of Lausanne (CHUV). In November of 2015 Natalia joined the Hub Team @ Pasteur C3BI as a Senior Bioinformatician. Natalia is especially interested in the integrative analysis of different omics data, both at large-scale and for small datasets, and loves collaborating in interdisciplinary environments and having feedback from her fellow experimental colleagues. Currently, she’s coordinating several projects performing functional and pathway analysis at the genomic level. By grouping genes, proteins and other biological molecules into the pathways they are involved in, the complexity of the analyses is significantly reduced, while the explanatory power increases with respect to having a list of differentially expressed genes or proteins.
AlgorithmicsData managementGenomicsImage analysisMachine learningModelingProteomicsSequence analysisStructural bioinformaticsTranscriptomicsDatabaseGenome analysisBiostatisticsScientific computingDatabases and ontologiesApplication of mathematics in sciencesData and text miningGeneticsGraphics and Image ProcessingBiosensors and biomarkersClinical researchCell biology and developmental biologyInteractomicsBioimage analysis
- Determination of the transcriptome controlled by the two-component system BvrR/BvrS using dominant positive and negative BvrR mutants(Javier PIZARRO-CERDA - Yersinia) - Pending
- Analyse transcriptionnelle du cellules cancéreuse intestinal vs normales après co-culture avec la bactérie associée au cancer Streptococcus gallolyticus(Ewa PASQUEREAU - Biology of Gram-Positive Pathogens) - Pending
- Functional interactomics of SKAP2(Jean-François BUREAU - Functional Genetics of Infectious Diseases) - Pending