Nature 453 (7197) xi:

…people don’t behave like statisticians.

Making the paper: Arnon Lotem : Article : Nature

Perhaps Arnon Lotem meant to say statisticians don’t behave like people.

Ottawa-Carleton MSc/MCI Program in Bioinformatics

David Bickel is currently accepting new students.

For more information on the field of bioinformatics, see the slides from the First Canadian Workshop on Statistical Genomics, the links provided by Georgia Tech, and the jobs posted at the Canadian Bioinformatics Workshops.

M. Guo, S. Yang, M. Rupe, B. Hu, D. R. Bickel, L. Arthur, and O. Smith, “Genome-wide allele-specific expression analysis using Massively Parallel Signature Sequencing (MPSS) reveals cis- and trans-effects on gene expression in maize hybrid meristem tissue,” Plant Molecular Biology 66, 551-563 (2008).

Related statistical genomics papers

D. R. Bickel, “Correcting the estimated level of differential expression for gene selection bias: Application to a microarray study,” Statistical Applications in Genetics and Molecular Biology 7 (1) 10, http://www.bepress.com/sagmb/vol7/iss1/art10 (2008).

Related statistical genomics papers

Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the new Statistical Inference and Computation in Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology seeks a postdoctoral researcher who will collaboratively develop and apply statistical methods to solve current problems in analyzing and integrating gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or clinical data. The lab is presently targeting inference in genome-wide association studies, bias reduction in estimated levels of gene expression, and validation of microarray predictions and will attack similar statistical and computational challenges of importance to genetics and functional genomics. The researcher’s background will complement that of any students and any postdoctoral researcher to be recruited to the Statomics Lab from the biomedical and computer science communities, creating an interdisciplinary environment for high impact on the biological sciences as well as on statistics.

Scientific creativity and a thorough knowledge of either Bayesian statistics or another likelihood-based inferential framework are essential, as is the demonstrated ability to quickly and accurately implement likelihood-based methods in software. Strong initiative, excellent communication skills, and reception of a PhD in statistics or a closely related field within the four years prior to the start date are also absolutely necessary. The following qualities are desirable but not required: exposure to the law of likelihood; knowledge of biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience in a UNIX or Linux environment.

To apply, send a PDF CV that has contact information of three references to dbickel0@uottawa.ca (without the zero), with “likelihood postdoc” and the year of your graduation or anticipated graduation in the Subject line of the message; in the plaintext message body, concisely include evidence that you meet each requirement for the position and a description of your most significant papers and software packages with an explanation of your own contributions to them. Only those applicants selected for further consideration will receive a response.

Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the new Statistical Inference and Computation in Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology seeks MSc and PhD students who will develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or phenotypic data. The lab is presently targeting inference in genome-wide association studies, estimation of levels of gene expression, and improvements in the repeatability of microarray results and will attack similar statistical and computational challenges of importance to genetics and functional genomics.

The OISB provides a highly collaborative research environment with ample opportunities to interact with leading experimental and computational biologists. In addition, each student’s background will complement that of any students and any postdoctoral researchers to be recruited to the Statomics Lab from the statistics, bioinformatics, and computer science communities, creating interdisciplinary synergism for making unique contributions to science.

Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Canadian citizenship or permanent resident status, strong initiative, good communication skills, and a degree in bioinformatics, computer science, mathematics, physics, statistics, any field of engineering, or an equally quantitative field are also absolutely necessary. The following qualities are desirable but not required: coursework in computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience with UNIX or Linux.

Send a PDF CV that has your GPA and contact information of two references to dbickel0@uottawa.ca (without the zero) with “statistical bioinformatics graduate student” in the Subject line of the message and with your preferred graduate program (Biochemistry, Mathematics & Statistics, or Computer Science) and the degree sought (MSc or PhD) in the message body. Only those students selected for further consideration will receive a response.

Paul Poncet’s modeest package implements the half-range mode, the half-sample mode, and the mode-based skewness of D. R. Bickel, “Robust estimators of the mode and skewness of continuous data,” Computational Statistics and Data Analysis 39, 153-163 (2002).

More mode estimation software

Ideal for a fourth-year project or summer internship

THE EDGE. Acquire a statistical bioinformatics skill set by developing novel scientific software in the frontiers of post-genomic biology for high impact on medical science.
THE LAB. Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the new Statistical Machine Learning in Functional Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology aims to develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or clinical data. The lab is presently targeting the inference of degrees of differential gene expression and improvements in the repeatability of microarray results and will attack similar statistics and machine learning challenges of importance to functional genomics.
THE STUDENT. Learn to analyze genomics data with newly created statistical methods. Make breakthrough bioinformatics software accessible worldwide by improving the usability and functionality of the lab’s data analysis code and by adding documentation. Providing scientists with these reliable biostatistical tools can advance medical research by improving the accuracy of conclusions drawn from genomics and clinical data.

Send your cover letter and CV or resume, including your GPA, to dbickel0@uottawa.ca (without the zero).

PDF version

Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the new Statistical Machine Learning in Functional Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology seeks MSc and PhD students who will develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or phenotypic data. The lab is presently targeting the inference of regulatory networks from multiple sources of information and improvements in the repeatability of microarray results and will attack similar statistics and machine learning challenges of importance to functional genomics.

The OISB provides a highly collaborative research environment with ample opportunities to interact with leading experimental and computational biologists; www.oisb.ca gives details. In addition, each student’s background will complement that of any students and any postdoctoral researchers to be recruited to the Statomics Lab from the Bayesian and machine learning communities, creating interdisciplinary synergism for making unique contributions to science. Students will have top-priority access to high-performance computing that enables parallelization of computationally complex methods.

Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Canadian citizenship or permanent resident status, strong initiative, good communication skills, and a degree in bioinformatics, computer science, mathematics, physics, statistics, any field of engineering, or an equally quantitative field are also absolutely necessary. The following qualities are desirable but not required: coursework in computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience with UNIX or Linux.

To apply, send a PDF CV that has contact information of two references to dbickel0@uottawa.ca (without the zero) with “statistical bioinformatics graduate student” in the Subject line of the message. Only those applicants selected for further consideration will receive a response.

PDF version

The new Statistical Machine Learning in Functional Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology seeks a postdoctoral researcher who will, in collaboration with University of Ottawa faculty, develop and apply algorithms to solve current problems in analyzing and integrating gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or clinical data. At present, the lab is targeting the inference of regulatory networks from multiple sources of information and improvements in the repeatability of microarray results and will attack similar machine learning challenges of importance to functional genomics. The researcher’s background will complement that of any students and any postdoctoral researcher to be recruited to the Statomics Lab from the statistics community, creating an interdisciplinary environment for high impact on the biological sciences as well as on machine learning.

Scientific creativity and a thorough knowledge of validation techniques and machine learning methods of data analysis (such as those of random forests, support vector machines, and Bayesian belief networks) are essential, as is the demonstrated ability to quickly and accurately implement their algorithms in software. Strong initiative, excellent communication skills, and reception of a PhD in bioinformatics, computer science, mathematics, physics, or a closely related field within the four years prior to the start date are also absolutely necessary. The following qualities are desirable but not required: knowledge of biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience in a UNIX or Linux environment.

To apply, send a PDF CV that has contact information of three references to dbickel0@uottawa.ca (without the zero), with “machine learning postdoc” and the year of your graduation or anticipated graduation in the Subject line of the message; in the plaintext message body, concisely include evidence that you meet each requirement for the position and a description of your most significant papers and software packages with an explanation of your own contributions to them. Only those applicants selected for further consideration will receive a response.