David Haussler

243.5k citations

294 papers · 41.6k indexed · 21 hit papers · h-index 89

Impact in

Molecular Biology top 0.05%
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- RNA Research and Splicing
- RNA modifications and cancer
- Genomics and Chromatin Dynamics
- Machine Learning in Bioinformatics
Artificial Intelligence top 0.05%
- Machine Learning and Algorithms

Papers in

Molecular Biology 192
- Genomics and Phylogenetic Studies 86
- RNA and protein synthesis mechanisms 63
- Machine Learning in Bioinformatics 39
- Genomics and Chromatin Dynamics 22
Artificial Intelligence 83
- Machine Learning and Algorithms 43
- Algorithms and Data Compression 40

Co-authors: Mark Diekhans David Kulp Robert Baertsch Frank H. Eeckman Manfred K. Warmuth Andrzej Ehrenfeucht Tommi Jaakkola W. James Kent
Journals: Genome Research (20 papers)Bioinformatics (15 papers)Nucleic Acids Research (12 papers)Cancer Research (12 papers)Machine Learning (10 papers)
Partner nations: United States United Kingdom Germany

In The Last Decade

David Haussler

288 papers receiving 39.8k citations

Hit Papers

15 papers align trajectories log scale

Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic 2021 · 193 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2021 Nature Genetics
2018 Nucleic Acids Research
2016 Genome Research
2010 Bioinformatics
2008 Bioinformatics
2006 Nature
2005 Genome Research
2004 Genome Research
2004 Science
2003 Proceedings of the National Academy of Sciences
2002 Genome Research
2000 Proceedings of the National Academy of Sciences
2000 Bioinformatics
1999 Medical Entomology and Zoology
1998 Neural Information Processing Systems
1997 Journal of Computational Biology
1997 PubMed
1994 Journal of Molecular Biology
1989 Journal of the ACM
1989 Neural Computation
1987 Information Processing Letters

Peers

Countries citing papers authored by David Haussler

Since Specialization

Citations

This map shows the geographic impact of David Haussler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Haussler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Haussler more than expected).

Fields of papers citing papers by David Haussler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Haussler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Haussler. The network helps show where David Haussler may publish in the future.

Co-authors

The 25 scholars most cited alongside David Haussler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David Haussler Line = papers co-authored together David Haussler links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	Protocol for deep-learning-driven cell type label transfer in single-cell RNA sequencing data STAR Protocols ·Zoe Zabetian, Jesus Gonzalez-Ferrer, Julian Lehrer, Vanessa D. Jönsson, Mircea Teodorescu, David Haussler, Mohammed A. Mostajo-Radji	2025	0
2	A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring Internet of Things ·Kateryna Voitiuk, Spencer T. Seiler, Mirella Pessoa de Melo, Jinghui Geng, Tjitse van der Molen, Sebastian Hernandez, H. Schweiger, Jessica Sevetson, David F. Parks, Ash Robbins, Sebastián Torres, Drew Ehrlich, Matthew A.T. Elliott, Tal Sharf, David Haussler, Mohammed A. Mostajo-Radji, Sofie R. Salama, Mircea Teodorescu	2025	4
3	Model-agnostic neural mean field with a data-driven transfer function SHILAP Revista de lepidopterología ·Alex Spaeth, David Haussler, Mircea Teodorescu	2024	0
4	A Daily-Updated Database and Tools for Comprehensive SARS-CoV-2 Mutation-Annotated Trees Molecular Biology and Evolution ·Jakob McBroome, Bryan Thornlow, Angie S. Hinrichs, Alexander Krämer, Nicola De Maio, Nick Goldman, David Haussler, Russell Corbett‐Detig, Yatish Turakhia	2021	56
5	The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets GigaScience ·Holly C. Beale, Jacquelyn Roger, Matthew A. Cattle, Liam T. McKay, Drew Thompson, Katrina Learned, A. Geoffrey Lyle, Ellen Kephart, Robert Currie, Du Linh Lam, Lauren Sanders, Jacob Pfeil, John Vivian, Isabel Bjork, Sofie R. Salama, David Haussler, Olena M. Vaske	2021	4
6	TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal Cancer Research ·Yulia Newton, Adam M. Novak, Teresa Swatloski, Duncan C. McColl, Sahil Chopra, Kiley Graim, Alana S. Weinstein, Robert Baertsch, Sofie R. Salama, Kyle Ellrott, M. R. Chopra, Theodore C. Goldstein, David Haussler, Olena Morozova, Joshua M. Stuart	2017	41
7	Long-read sequence assembly of the gorilla genome Science ·David Gordon, John Huddleston, Mark Chaisson, C. Hill, Zev Kronenberg, Katherine M. Munson, Maika Malig, Archana N. Raja, Ian T. Fiddes, LaDeana Hillier, Christopher Dunn, Carl Baker, Joel Armstrong, Mark Diekhans, Benedict Paten, Jay Shendure, Richard K. Wilson, David Haussler, Chen-Shan Chin, Evan E. Eichler	2016	218
8	Chromosome-scale shotgun assembly using an in vitro method for long-range linkage Genome Research ·Nicholas H. Putnam, Brendan L. O’Connell, Jonathan C. Stites, Brandon Rice, Marco Blanchette, Robert Calef, Christopher J. Troll, Andrew J. Fields, Paul D. Hartley, Charles W. Sugnet, David Haussler, Daniel S. Rokhsar, Richard E. Green Hit paper breakdown →	2016	468
9	A unifying model of genome evolution under parsimony BMC Bioinformatics ·Benedict Paten, Daniel R. Zerbino, Glenn Hickey, David Haussler	2014	4
10	Double Minute Chromosomes in Glioblastoma Multiforme Are Revealed by Precise Reconstruction of Oncogenic Amplicons Cancer Research ·Zack Sanborn, Sofie R. Salama, Mia Grifford, Cameron Brennan, Tom Mikkelsen, Suresh C. Jhanwar, Sol Katzman, Lynda Chin, David Haussler	2013	73
11	Genome wide screens in yeast to identify potential binding sites and target genes of DNA-binding proteins Nucleic Acids Research ·Jue Zeng, Jizhou Yan, Ting Wang, Deborah Mosbrook-Davis, Kyle Dolan, Ryan Christensen, Gary D. Stormo, David Haussler, Richard H. Lathrop, Rainer K. Brachmann, Shawn M. Burgess	2007	21
12	Ultraconserved Elements in the Human Genome Science ·Gill Bejerano, Michael Pheasant, Igor V. Makunin, Stuart Stephen, W. James Kent, John S. Mattick, David Haussler Hit paper breakdown →	2004	1262
13	Knowledge-based analysis of microarray gene expression data by using support vector machines University of North Texas Digital Library (University of North Texas) ·William Noble Grundy, Manuel Ares, David Haussler	2001	2
14	Probabilistic Kernel Regression Models International Conference on Artificial Intelligence and Statistics ·Tommi Jaakkola, David Haussler	1999	130
15	Exploiting Generative Models in Discriminative Classifiers Neural Information Processing Systems ·Tommi Jaakkola, David Haussler Hit paper breakdown →	1998	795
16	Proceedings of the fifth annual workshop on Computational learning theory Conference on Learning Theory ·David Haussler	1992	34
17	Calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise Conference on Learning Theory ·Manfred Opper, David Haussler	1991	32
18	Learnability and the Vapnik-Chervonenkis dimension Journal of the ACM ·Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth Hit paper breakdown →	1989	1058
19	Equivalence of models for polynomial learnability Conference on Learning Theory ·David Haussler, Michael Kearns, Nick Littlestone, Manfred K. Warmuth	1988	53
20	Learning decision trees from random examples Conference on Learning Theory ·Andrzej Ehrenfeucht, David Haussler	1988	18

About David Haussler

David Haussler is a scholar working on Molecular Biology, Artificial Intelligence, Cancer Research, Computational Theory and Mathematics and Genetics, having authored 294 papers that have together received 41.6k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (86 papers), RNA and protein synthesis mechanisms (63 papers), Machine Learning and Algorithms (43 papers), Chromosomal and Genetic Variations (42 papers), Algorithms and Data Compression (40 papers), Machine Learning in Bioinformatics (39 papers), Cancer Genomics and Diagnostics (26 papers) and Genomics and Chromatin Dynamics (22 papers). The work is most often cited by research in Molecular Biology (25.1k citations), Artificial Intelligence (9.1k citations), Genetics (7.1k citations), Cancer Research (3.8k citations) and Computational Theory and Mathematics (2.8k citations). David Haussler has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Mark Diekhans, David Kulp, Robert Baertsch, Frank H. Eeckman, Manfred K. Warmuth, Andrzej Ehrenfeucht, Tommi Jaakkola, W. James Kent, Martin G. Reese and Webb Miller. Their work appears in journals such as Genome Research, Bioinformatics, Nucleic Acids Research, Cancer Research and Machine Learning.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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