D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 60 Citations 26,944 149 World Ranking 1505 National Ranking 45

Overview

What is he best known for?

The fields of study he is best known for:

  • Gene
  • Artificial intelligence
  • Machine learning

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 most cited work include:

  • The cancer genome atlas pan-cancer analysis project (3506 citations)
  • An introduction to kernel-based learning algorithms (3027 citations)
  • Fisher discriminant analysis with kernels (2282 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (33.83%)
  • Machine learning (25.15%)
  • Genetics (17.37%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (33.83%)
  • Machine learning (25.15%)
  • Set (8.38%)

In recent papers he was focusing on the following fields of study:

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

  • De Bruijn sequence which intersects with area such as Genome and Binary relation,
  • Theoretical computer science which connect with Graph and Time complexity.

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.

Between 2018 and 2021, his most popular works were:

  • Pan-cancer analysis of whole genomes (538 citations)
  • Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (339 citations)
  • Genomic basis for RNA alterations in cancer (92 citations)

In his most recent research, the most cited papers focused on:

  • Gene
  • Artificial intelligence
  • Machine learning

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.

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.

Best Publications

An introduction to kernel-based learning algorithms

K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda.
IEEE Transactions on Neural Networks (2001)

4404 Citations

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)

3386 Citations

Fisher discriminant analysis with kernels

S. Mika;G. Ratsch;J. Weston;B. Scholkopf.
ieee workshop on neural networks for signal processing (1999)

3328 Citations

Large Scale Multiple Kernel Learning

Sören Sonnenburg;Gunnar Rätsch;Christin Schäfer;Bernhard Schölkopf.
Journal of Machine Learning Research (2006)

1616 Citations

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)

1542 Citations

Soft Margins for AdaBoost

G. Rätsch;T. Onoda;K.-R. Müller.
Machine Learning (2001)

1448 Citations

The Molecular Taxonomy of Primary Prostate Cancer

Adam Abeshouse;Jaeil Ahn;Rehan Akbani;Adrian Ally.
Cell (2015)

1243 Citations

Predicting Time Series with Support Vector Machines

Klaus-Robert Müller;Alex J. Smola;Gunnar Rätsch;Bernhard Schölkopf.
international conference on artificial neural networks (1997)

1196 Citations

Kernel PCA and De-Noising in Feature Spaces

Sebastian Mika;Bernhard Schölkopf;Alex J. Smola;Klaus-Robert Müller.
neural information processing systems (1998)

1189 Citations

Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

Matthew T. Weirauch;Matthew T. Weirauch;Ally Yang;Mihai Albu;Atina G. Cote.
Cell (2014)

993 Citations

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Best Scientists Citing Gunnar Rätsch

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Mark A. Rubin

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Yale University

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Paul C. Boutros

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Francesca Demichelis

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Johan A. K. Suykens

Johan A. K. Suykens

KU Leuven

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Andrea Sboner

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David Zhang

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Chinese University of Hong Kong, Shenzhen

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Robert J. Schmitz

Robert J. Schmitz

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