What is he best known for?
The fields of study he is best known for:
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.
His most cited work include:
- Systematic identification of genomic markers of drug sensitivity in cancer cells (1650 citations)
- Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients (957 citations)
- A Landscape of Pharmacogenomic Interactions in Cancer (823 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Computational biology (75.11%)
- Cancer (25.53%)
- Signal transduction (23.40%)
What were the highlights of his more recent work (between 2020-2021)?
- Computational biology (75.11%)
- Transcriptome (16.60%)
- Cancer research (8.51%)
In recent papers he was focusing on the following fields of 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.
Between 2020 and 2021, his most popular works were:
- Decoding myofibroblast origins in human kidney fibrosis (19 citations)
- Decoding myofibroblast origins in human kidney fibrosis (19 citations)
- Decoding myofibroblast origins in human kidney fibrosis (19 citations)
In his most recent research, the most cited papers focused on:
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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