He is a Member of the editorial boards of IEEE/ACM Transaction on Computational Biology and Bioinformatics, Briefings in Bioinformatics, High-Throughput, Encyclopaedia of Bioinformatics and Computational Biology, Encyclopaedia of Systems Biology. His current research interests include bioinformatics, medical informatics, data analytics, parallel and distributed computing. He is the director of the Data Analytics research center, the chair of the Bioinformatics Laboratory, and the chair of the Bachelor Degree in Informatics and Biomedical Engineering, at University “Magna Graecia” of Catanzaro. Some real cases regarding the statistical and data mining analysis of DMET SNPs datasets for pharmacogenomics studies in cancer research are also presented.īios: Mario Cannataro is a Full Professor of Computer Engineering and Bioinformatics at University “Magna Graecia” of Catanzaro, Italy. gene expression and SNPs, mass spectra, protein-protein interactions), and describes some parallel and distributed bioinformatics tools for the preprocessing and statistical and data mining analysis of omics data, including those developed at the Bioinformatics Laboratory of the University Magna Graecia of Catanzaro (micro-CS, DMET-Analyzer, DMET-Miner, OSAnalyzer, coreSNP, GenotypeAnalytics, etc.). The talk introduces main omics data (e.g. The resulting scenario comprises a set of methodologies and bioinformatics tools for the management and analysis of omics data stored locally or in geographically distributed biological databases. Thus, managing omics data requires both support and spaces for data storing as well as algorithms and software pipelines for data preprocessing, analysis, and sharing. Moreover, both raw experimental data and derived information extracted by raw data are more and more stored in various databases spread all over the Internet, not fully integrated. On the other hand, the large volumes of omics data poses new challenges both for the efficient storage and integration of the data and for their efficient preprocessing and analysis. Such omics disciplines are gaining an increasing interest in the scientific community due to the availability of novel, high throughput platforms for the investigation of the cell machinery, such as mass spectrometry, microarray, next generation sequencing, that are producing an overwhelming amount of experimental omics data. Genomics, proteomics, and interactomics refer to the study of the genome, proteome and interactome of an organism. To apply for this position, or for any additional information, email to deadline for applications is March 10th.Title: High Performance Data Mining Analysis of Omics and Clinical Data:Įxperiences at University Magna Graecia of Catanzaro No tenure-track academic position is linked to this job offer. We offer flexible working time and a combination of local/remote working schemes. Payscale at postdoc level depending on the candidate’s experience. Proved publication track record (minimum Scopus H-index equal to 7).PhD in Biomedical Engineering, Computer Science, Computer Vision, Data Science, Medical Physics, or related field, with emphasis on medical image analysis and artificial intelligence (both machine learning and deep learning).The project, in collaboration with the European Institute of Oncology (Milan) and Azienda Ospedaliero-Universitaria Cagliari, aims at using CT scans and artificial intelligence to predict the therapeutic outcomes (both pharmacological and surgical) for gynecological cancer patients. We are looking for a motivated person to work as a post-doc on a medical image analysis project at Magna Graecia University of Catanzaro, Italy.
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