His primary scientific interests are in Systems biology, Bioinformatics, Computational biology, Signal transduction and Theoretical computer science. His Systems biology research is multidisciplinary, incorporating perspectives in Software, Fuzzy logic, Artificial intelligence and Machine learning, Profiling. His Bioinformatics study integrates concerns from other disciplines, such as Myeloid, Cancer research, Gateway and Crowdsourcing, World Wide Web.
His Computational biology research includes themes of Benchmarking, Genomics, In silico, Gene and Reverse engineering. His Cell signaling and Protein Interaction Map study, which is part of a larger body of work in Signal transduction, is frequently linked to Protein network and Network topology, bridging the gap between disciplines. His work carried out in the field of Theoretical computer science brings together such families of science as State and Mammalian cell.
Julio Saez-Rodriguez mostly deals with Computational biology, Cancer, Signal transduction, Systems biology and Bioinformatics. In Computational biology, Julio Saez-Rodriguez works on issues like Pharmacogenomics, which are connected to Drug resistance. Julio Saez-Rodriguez combines topics linked to CRISPR with his work on Cancer.
In his work, Metabolomics is strongly intertwined with Phosphoproteomics, which is a subfield of Signal transduction. His biological study spans a wide range of topics, including Proteome, Software and Theoretical computer science. He has included themes like Carcinogenesis, Receptor and Proteomics in his Cell biology study.
His main research concerns Computational biology, Transcriptome, Cancer research, Kidney disease and Metabolomics. His Computational biology research incorporates themes from Cell signaling, Proteomics, Gene and Drug. The Drug study combines topics in areas such as Clinical Oncology, Pharmacogenomics and Mechanism of action.
His study in Transcriptome is interdisciplinary in nature, drawing from both Chromatin, Fibrosis, Myofibroblast and Cellular differentiation. His Cancer research study combines topics in areas such as Carcinogenesis, Gene knockdown, In vivo and Single-cell analysis. His studies deal with areas such as Microbiome, Bioinformatics and Glomerulonephritis as well as Kidney disease.
His scientific interests lie mostly in Computational biology, Kidney disease, Metabolomics, Proteome and Proteomics. His Computational biology study frequently draws connections between adjacent fields such as Gene regulatory network. His studies in Kidney disease integrate themes in fields like Cancer research, Fibrosis, Myofibroblast, RNA-Seq and Cellular differentiation.
His work deals with themes such as Cosmos, Multi omics and Phosphoproteomics, which intersect with Metabolomics. The concepts of his Proteome study are interwoven with issues in Internal medicine, Nephrology, Omics and Kidney biopsy sample. His Proteomics research integrates issues from Structural biology, Yeast, Protein aggregation and Phosphorylation.
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Systematic identification of genomic markers of drug sensitivity in cancer cells
Mathew J. Garnett;Elena J. Edelman;Sonja J. Heidorn;Christopher Greenman;Christopher Greenman.
Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients
Marc van de Wetering;Hayley E. Francies;Joshua M. Francis;Joshua M. Francis;Gergana Bounova.
A Landscape of Pharmacogenomic Interactions in Cancer
Francesco Iorio;Francesco Iorio;Theo A. Knijnenburg;Theo A. Knijnenburg;Daniel J. Vis;Graham R. Bignell.
Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens
Fiona M. Behan;Francesco Iorio;Francesco Iorio;Gabriele Picco;Emanuel Gonçalves.
Structural and functional analysis of cellular networks with CellNetAnalyzer
Steffen Klamt;Julio Saez-Rodriguez;Ernst Dieter Gilles.
BMC Systems Biology (2007)
Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges
Robert J. Prill;Daniel Marbach;Julio Saez-Rodriguez;Julio Saez-Rodriguez;Peter Karl Sorger;Peter Karl Sorger.
PLOS ONE (2010)
A methodology for the structural and functional analysis of signaling and regulatory networks
Steffen Klamt;Julio Saez-Rodriguez;Jonathan A. Lindquist;Luca Simeoni.
BMC Bioinformatics (2006)
A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia
Konstantinos Tzelepis;Hiroko Koike-Yusa;Etienne De Braekeleer;Yilong Li.
Cell Reports (2016)
Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
Michael P. Menden;Francesco Iorio;Francesco Iorio;Mathew Garnett;Ultan McDermott.
PLOS ONE (2013)
Pharmacogenomic agreement between two cancer cell line data sets
Nicolas Stransky;Mahmoud Ghandi.
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