Marcel J. T. Reinders mainly focuses on Artificial intelligence, Gene, Data mining, Genetics and Machine learning. His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition. His Data mining study combines topics from a wide range of disciplines, such as Annotation and Code.
His works in Genome, Sequence analysis, Genomic organization, Somatic cell and Germline mutation are all subjects of inquiry into Genetics. His Machine learning study incorporates themes from Probabilistic logic and Genetic network. The concepts of his Gene expression study are interwoven with issues in Molecular biology and Gene rearrangement.
Marcel J. T. Reinders mostly deals with Computational biology, Artificial intelligence, Genetics, Gene and Data mining. His Computational biology research includes themes of Cancer, Cell and Bioinformatics. Marcel J. T. Reinders combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
His Genome, DNA microarray, Gene expression profiling, Genomics and Sequence analysis investigations are all subjects of Genetics research. His research on Gene often connects related areas such as Human brain. His primary area of study in Data mining is in the field of Identification.
His primary scientific interests are in Computational biology, Gene, Artificial intelligence, Disease and Transcriptome. His Computational biology research includes elements of Text mining, Cell and Saccharomyces cerevisiae, Yeast. His study in the field of Genome is also linked to topics like Expression.
Marcel J. T. Reinders interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence. The Disease study combines topics in areas such as Genetics, Immune system and Bioinformatics. His work on Endocytosis is typically connected to TREM2 as part of general Genetics study, connecting several disciplines of science.
Marcel J. T. Reinders focuses on Computational biology, Disease, Artificial intelligence, Data mining and Mass cytometry. The study incorporates disciplines such as Patient response and Cancer in addition to Computational biology. Marcel J. T. Reinders has researched Disease in several fields, including Genetics and Genetic association.
His Genetics study incorporates themes from Percentile and Meta-analysis. His research integrates issues of Functional annotation, Machine learning and Gene ontology in his study of Artificial intelligence. His work on Identification as part of his general Data mining study is frequently connected to Set, thereby bridging the divide between different branches of science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Molecular Maps of the Reorganization of Genome-Nuclear Lamina Interactions during Differentiation
Daan Peric-Hupkes;Wouter Meuleman;Wouter Meuleman;Ludo Pagie;Sophia W.M. Bruggeman.
Molecular Cell (2010)
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
Jun Wang;Arjen P. de Vries;Marcel J. T. Reinders.
international acm sigir conference on research and development in information retrieval (2006)
Resolving motion correspondence for densely moving points
C.J. Veenman;M.J.T. Reinders;E. Backer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas.
Paul Roepman;Lodewyk F A Wessels;Lodewyk F A Wessels;Nienke Kettelarij;Patrick Kemmeren.
Nature Genetics (2005)
Eleven grand challenges in single-cell data science
David Lähnemann;David Lähnemann;Johannes Köster;Johannes Köster;Ewa Szczurek;Davis J. McCarthy;Davis J. McCarthy.
Genome Biology (2020)
A maximum variance cluster algorithm
C.J. Veenman;M.J.T. Reinders;E. Backer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
New insights on human T cell development by quantitative T cell receptor gene rearrangement studies and gene expression profiling
Willem A. Dik;Karin Pike-Overzet;Floor Weerkamp;Dick de Ridder;Dick de Ridder.
Journal of Experimental Medicine (2005)
Constitutive nuclear lamina–genome interactions are highly conserved and associated with A/T-rich sequence
Wouter Meuleman;Daan Peric-Hupkes;Jop Kind;Jean-Bernard Beaudry.
Genome Research (2013)
An algorithm-based topographical biomaterials library to instruct cell fate
Hemant V. Unadkat;Marc Hulsman;Kamiel Cornelissen;Bernke J. Papenburg.
Proceedings of the National Academy of Sciences of the United States of America (2011)
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Tamim Abdelaal;Tamim Abdelaal;Lieke C.M. Michielsen;Lieke C.M. Michielsen;Davy Cats;Dylan Hoogduin.
Genome Biology (2019)
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