Nir Friedman

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Nir Friedman
Alma mater Tel Aviv University, Weizmann Institute of Science, Stanford University
Awards Michael Bruno Memorial Award (2010), [1] European Research Council Research Award (2009–2014), Juludan Prize (2007), Sir Zelman Cowen Universities Fund Prize (2007) [2]
Scientific career
Fields Machine Learning and Computational Biology
Institutions Hebrew University of Jerusalem
Doctoral advisor Joseph Halpern
Other academic advisors David Harel, Stuart Russell
Notable students Dana Pe'er

Nir Friedman (born 1967) is an Israeli Professor of Computer Science and Biology at the Hebrew University of Jerusalem. [3] [4]

His research combines Machine Learning and Statistical Learning with Systems Biology, specifically in the fields of Gene Regulation, Transcription and Chromatin.

Education and research

Friedman earned his B.Sc degree from Tel Aviv University (1987) and his M.Sc from the Weizmann Institute of Science (1992). [5] In 1997, he completed his Ph.D. at Stanford under the supervision of Joseph Halpern, in the field of Artificial Intelligence. [6]

After some postdoctoral work at the University of California, Berkeley, he accepted a faculty position at the School of Computer Science, [7] the Hebrew University of Jerusalem.

His highly cited research [8] [9] includes work on Bayesian network classifiers [10] (with Danny Geiger and Moises Goldszmidt), Bayesian Structural EM, [11] and the use of Bayesian methods to analyzing gene expression data [12] [13] [14] [15] (with Aviv Regev, Dana Pe'er, Eran Segal, Daphne Koller and David Botstein). More recent works focus on Probabilistic Graphical Models, reconstructing Regulatory Networks, Genetic Interactions, and the role of Chromatin in Transcriptional Regulation (with Oliver Rando) [16]

In 2009, Friedman and Koller published a textbook on Probabilistic Graphical Models. [17] Later that year, he joined the Institute of Life Sciences, [18] and opened an experimental lab where he uses advanced robotic tools to study transcriptional regulation in the yeast Saccharomyces cerevisiae. [19]

Related Research Articles

A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.

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An influence diagram (ID) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network, in which not only probabilistic inference problems but also decision making problems can be modeled and solved.

<span class="mw-page-title-main">Dynamic Bayesian network</span> Probabilistic graphical model

A dynamic Bayesian network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps.

<span class="mw-page-title-main">Daphne Koller</span> Israeli-American computer scientist

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Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models. VOBN models are used in machine learning in general and have shown great potential in bioinformatics applications. These models extend the widely used position weight matrix (PWM) models, Markov models, and Bayesian network (BN) models.

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Krueppel-like factor 6 is a protein that in humans is encoded by the KLF6 gene.

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The Immunological Genome Project (ImmGen) is a collaborative scientific research project that is currently building a gene-expression database for all characterized immune cells in the mouse. The overarching goal of the project is to computationally reconstruct the gene regulatory network in immune cells

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Eran Segal is a computational biologist professor at the Weizmann Institute of Science. He works on developing quantitative models for all levels of gene regulation, including transcription, chromatin, and translation. Segal also works as an epidemiologist.

<span class="mw-page-title-main">Aviv Regev</span> Bioinformatician

Aviv Regev is a computational biologist and systems biologist and Executive Vice President and Head of Genentech Research and Early Development in Genentech/Roche. She is a core member at the Broad Institute of MIT and Harvard and professor at the Department of Biology of the Massachusetts Institute of Technology. Regev is a pioneer of single cell genomics and of computational and systems biology of gene regulatory circuits. She founded and leads the Human Cell Atlas project, together with Sarah Teichmann.

Variable elimination (VE) is a simple and general exact inference algorithm in probabilistic graphical models, such as Bayesian networks and Markov random fields. It can be used for inference of maximum a posteriori (MAP) state or estimation of conditional or marginal distributions over a subset of variables. The algorithm has exponential time complexity, but could be efficient in practice for low-treewidth graphs, if the proper elimination order is used.

<span class="mw-page-title-main">Dana Pe'er</span> Bioinformatician

Dana Pe'er, Chair and Professor in Computational and Systems Biology Program at Sloan Kettering Institute is a researcher in computational systems biology. A Howard Hughes Medical Institute (HHMI) Investigator since 2021, she was previously a professor at Columbia Department of Biological Sciences. Pe'er's research focuses on understanding the organization, function and evolution of molecular networks, particularly how genetic variations alter the regulatory network and how these genetic variations can cause cancer.

<span class="mw-page-title-main">Fred Winston</span> American geneticist

Fred Marshall Winston is the John Emory Andrus Professor of Genetics in the Harvard Medical School Genetics Department, where he has been a member of the faculty since 1983. Research in his laboratory has focused on mechanisms of transcription and the regulation of chromatin structure in the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. Dr. Winston served as the President of the Genetics Society of America in 2009 and has been elected to both the American Academy of Arts and Sciences (2009) and the National Academy of Sciences (2013).

Radford M. Neal is a professor emeritus at the Department of Statistics and Department of Computer Science at the University of Toronto, where he holds a research chair in statistics and machine learning.

<span class="mw-page-title-main">Hanah Margalit</span>

Hanah Margalit is a Professor in the faculty of medicine at the Hebrew University of Jerusalem. Her research combines bioinformatics, computational biology and systems biology, specifically in the fields of gene regulation in bacteria and eukaryotes.

<span class="mw-page-title-main">Michal Linial</span> Israeli computational biologist

Michal Linial is a Professor of Biochemistry and Bioinformatics at the Hebrew University of Jerusalem (HUJI). Linial is the Director of The Sudarsky Center for Computational Biology at HUJI. Since 2015, she is head of the ELIXIR-Israel node.

References

  1. ↑ "Nir Friedman - Yad Hanadiv". Yadhanadiv.org.il. Archived from the original on 25 June 2016. Retrieved 25 May 2016.
  2. ↑ "Young HU biologist wins Australian award". The Jerusalem Post - JPost.com. Retrieved 25 May 2016.
  3. ↑ "Nir Friedman". Cs.huji.ac.il. Retrieved 25 May 2016.
  4. ↑ "Nir Friedman's Lab". Systemsbio.cs.huji.ac.il. Retrieved 25 May 2016.
  5. ↑ "Curriculum Vitae—Nir Friedman" (PDF). Cs.huji.ac.il. Retrieved 25 May 2016.
  6. ↑ "Archived copy". Archived from the original on April 25, 2012. Retrieved October 22, 2011.{{cite web}}: CS1 maint: archived copy as title (link)
  7. ↑ "The Rachel and Selim Benin School of Computer Science and Engineering". Cs.huji.ac.il. Retrieved 25 May 2016.
  8. ↑ "nir friedman - Google Scholar". Scholar.google.com. Retrieved 25 May 2016.
  9. ↑ Search Results for author Friedman N on PubMed .
  10. ↑ "Bayesian network classifiers". Cs.huji.ac.il. Retrieved 25 May 2016.
  11. ↑ "The Bayesian structural EM algorithm". Cs.huji.ac.il. Retrieved 25 May 2016.
  12. ↑ Friedman, N.; Linial, M.; Nachman, I.; Pe'er, D. (2000). "Using Bayesian Networks to Analyze Expression Data". Journal of Computational Biology. 7 (3–4): 601–620. CiteSeerX   10.1.1.191.139 . doi:10.1089/106652700750050961. PMID   11108481.
  13. ↑ Segal, E.; Shapira, M.; Regev, A.; Pe'er, D.; Botstein, D.; Koller, D.; Friedman, N. (2003). "Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data". Nature Genetics. 34 (2): 166–176. doi:10.1038/ng1165. PMID   12740579. S2CID   6146032.
  14. ↑ Friedman, N. (2004). "Inferring Cellular Networks Using Probabilistic Graphical Models". Science. 303 (5659): 799–805. Bibcode:2004Sci...303..799F. doi:10.1126/science.1094068. PMID   14764868. S2CID   6819971.
  15. ↑ Segal, E.; Friedman, N.; Koller, D.; Regev, A. (2004). "A module map showing conditional activity of expression modules in cancer". Nature Genetics. 36 (10): 1090–1098. doi: 10.1038/ng1434 . PMC   2271138 . PMID   15448693.
  16. ↑ "Nir Friedman". Cs.huji.ac.il. Retrieved 25 May 2016.
  17. ↑ Daphne Koller and Nir Friedman (2009). Probabilistic Graphical Models. MIT Press. ISBN   978-0-262-01319-2.
  18. ↑ "The Alexander Silberman Institute of Life Sciences". Bio.huji.ac.il. Archived from the original on 2016-04-26. Retrieved 2016-05-25.
  19. ↑ "Robotic Facility". Systemsbio.cs.huji.ac.il. Retrieved 25 May 2016.
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