Gunnar Rätsch mostly deals with Artificial intelligence, Genetics, Support vector machine, Machine learning and Pattern recognition. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Translation initiation sites. His study connects Computational biology and Genetics.
His Support vector machine research is multidisciplinary, incorporating elements of Margin, Algorithm, Site specificity and Identification. His study in the field of Unsupervised learning, Boosting and Multiple kernel learning also crosses realms of Set and Engineering support. His Genomics study, which is part of a larger body of work in Genome, is frequently linked to Chromothripsis, bridging the gap between disciplines.
His primary areas of investigation include Artificial intelligence, Machine learning, Genetics, Computational biology and Support vector machine. Many of his studies on Artificial intelligence apply to Pattern recognition as well. Many of his studies on Machine learning involve topics that are commonly interrelated, such as Variation.
His study in Gene, Genome, RNA splicing, Transcriptome and Genomics is carried out as part of his studies in Genetics. His Genome research incorporates themes from Annotation and Data mining. His Computational biology research is multidisciplinary, incorporating perspectives in RNA, DNA microarray and RNA-Seq.
His primary scientific interests are in Artificial intelligence, Machine learning, Set, Search engine indexing and Algorithm. His Deep learning study in the realm of Machine learning connects with subjects such as Field. His Search engine indexing research also works with subjects such as
His biological study focuses on Genomics. His work in the fields of Algorithm, such as Edit distance and Tree, intersects with other areas such as Clone and Scale. His work in Artificial neural network tackles topics such as Pattern recognition which are related to areas like Contextual image classification.
His primary areas of study are Artificial intelligence, Machine learning, Variation, Cancer research and Messenger RNA. His is doing research in Feature learning and Model selection, both of which are found in Artificial intelligence. His Machine learning study frequently draws parallels with other fields, such as Intensive care unit.
His research in Variation intersects with topics in Unsupervised learning and Data set. His study in Messenger RNA is interdisciplinary in nature, drawing from both Cell, CD19, Transcription factor, Protein biosynthesis and Cell biology. His studies examine the connections between Gene expression and genetics, as well as such issues in Pan cancer, with regards to Genomics.
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An introduction to kernel-based learning algorithms
K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda.
IEEE Transactions on Neural Networks (2001)
The cancer genome atlas pan-cancer analysis project
John N Weinstein;John N Weinstein;Eric A. Collisson;Gordon B Mills;Kenna R Mills Shaw;Kenna R Mills Shaw.
Nature Genetics (2013)
Fisher discriminant analysis with kernels
S. Mika;G. Ratsch;J. Weston;B. Scholkopf.
ieee workshop on neural networks for signal processing (1999)
Large Scale Multiple Kernel Learning
Sören Sonnenburg;Gunnar Rätsch;Christin Schäfer;Bernhard Schölkopf.
Journal of Machine Learning Research (2006)
Input space versus feature space in kernel-based methods
B. Scholkopf;S. Mika;C.J.C. Burges;P. Knirsch.
IEEE Transactions on Neural Networks (1999)
Soft Margins for AdaBoost
G. Rätsch;T. Onoda;K.-R. Müller.
Machine Learning (2001)
The Molecular Taxonomy of Primary Prostate Cancer
Adam Abeshouse;Jaeil Ahn;Rehan Akbani;Adrian Ally.
Predicting Time Series with Support Vector Machines
Klaus-Robert Müller;Alex J. Smola;Gunnar Rätsch;Bernhard Schölkopf.
international conference on artificial neural networks (1997)
Kernel PCA and De-Noising in Feature Spaces
Sebastian Mika;Bernhard Schölkopf;Alex J. Smola;Klaus-Robert Müller.
neural information processing systems (1998)
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Matthew T. Weirauch;Matthew T. Weirauch;Ally Yang;Mihai Albu;Atina G. Cote.
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