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| From the News Desk |
| Bioinformatic news from UHM. To post here, contact us. |
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Pacific Rim Researchers to Collaborate on Distributed Bioinformatics Analysis of Avian Flu Using Global Computational Data Grid
Available here : Download DOC
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Genomics and Bioinformatics infrastructure at ASGPB
A presentation by Alexandre Dionne-Laporte held at the Bioinformatics Colloquium at the Kaka'ako Medical Education Building, November 28, 2006.
Available here : Download PDF
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SOCIETY FOR ADVANCEMENT OF CHICANOS AND NATIVE AMERICANS IN SCIENCE
Genome Scholarship
$25,000 Genome Scholarship Opportunity
SACNAS Genome Scholars will receive one year of support for graduate school in genomics/bioinformatics in the amount of $25,000. In addition, SACNAS will support Genome Scholars to attend the 2006 SACNAS National Conference in Tampa, Florida and one additional genomics/bioinformatics conference in the 2006-2007 academic year. Scholars will present their research at the 2007 SACNAS National Conference.
Qualifications
Underrepresented minority graduating seniors who have been accepted to a graduate program and/or current graduate students in genomics or bioinformatics are eligible for the SACNAS Genome Scholarship. In genomics we include: ethical, legal, and/or social implications of genomic research; computational biology as it relates to genomics and bioengineering as it relates to genomics.
Deadline: June 1
Download an application and view detailed requirements here.
Please contact info@sacnas.org or call 877-SACNAS-1 with any questions.
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CGPBRI FY04 Annual Report with survey
Available here : Download PDF
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Faculty Senate Research Committee Presentation
Available here : Download PDF
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ICS 491 Fall 2005
INTRODUCTION TO BIOINFORMATICS: Genome and sequence analysis
Download PDF
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Class hours: Tuesday-Thursday 13:30 pm to 14:45 pm
Location: KUY 304 Professor Guylaine Poisson, guylaine@hawaii.edu
Office Hours: Tuesday-Thursday 15:00 to 16:30 or by appointment.
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Textbook: Online resources will be posted on the course website
A list of reference books, research publications and recommended reading will also be posted on the course website.
This is an introductory course, no knowledge of biology or bioinformatics is assumed. Basic knowledge of programming is desirable. This class will focus on the basic knowledge of bioinformatics in the sequence analysis problem. Next semester another class will focus on the structure analysis and other problems related to the analysis of biological sequences.
Auditors and students from other departments are welcome.
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The course is divided in five sections. In each one we will combine the molecular biology knowledge needed to understand the problems with looking at some algorithms and learning how to use them.
Section 1: Biological sequences: Molecular biology for computer scientist, Types of sequences, biological database on internet. Informatics problems related to sequencing, assembly techniques etc.
Section 2: Sequences comparison: Sequences comparison techniques. Sequences alignment (pair and multiple). Global and local comparison.
Section 3: Patterns in sequences : Techniques of prediction and classification for pattern recognition in sequences.
Section 4: Genome structure : Procarya and eukarya genomes. Gene prediction and annotation.
Section 5: Microarrays Data: Basic knowledge of the microarrays technology and their applications to biology. Analysis: clustering, classification, time-series etc
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Tentative: 5 assignments (for 40%), 2 exams (60%).
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Computational Biology
Tuesday, May 31, 2005
12:00pm-1:00pm
Manoa Campus, POST 126
Small Feedback Sets in Computational Biology: Applications and Algorithmic Advances
Mike Fellows and Frances Rosamond Australian Centre for Bioinformatics Abstract.
A feedback set in a directed or undirected graph is a set of vertices that covers all the cycles in the graph. The talk will survey a number of applications of feedback sets in computational biology, and describe a new algorithm for finding small feedback sets in undirected graphs.
(Joint work with Mike Langston of the Univ. of Tennessee, and Frank Dehne of Carleton University, Canada.) |
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ICS 691 Spring 2005 Bioinformatics / Computational Biology
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Class hours: Tuesday and Wednesday 3:00 - 4:15 PM
Location: POST 126
Professor Susanna Still, sstill@hawaii.edu
Office Hours: Wednesday and Thursday 1:30 - 3:00 PM
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Textbook: Information Theory, Inference and Learning Algorithms.
David J. C. MacKay. Cambridge University Press, 2003.
A list of research publications and recommended reading will
be posted on the course website.
No knowledge of biology is assumed, elementary knowledge of
calculus, linear algebra, probability, statistics and scientific
programming is desirable.
Auditors and students from other departments (especially Physics,
Math and Biology) are welcome.
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This class will focus on quantitative models of biological systems.
The goals are twofold. On one hand, we will learn theoretical
methods and computational tools necessary for a quantitative
understanding of biology. On the other hand, students are
encouraged to develop an understanding of what kinds of questions
in biology are relevant and can be solved using currently available
data.
To that end, we will discuss some recent papers. I will give a list
of papers, but students are also encouraged to find research they
wish to discuss. Instead of focusing on one area, I want to work out
that very similar problems occur in many different biological
problems, ranging in scale from the very small (molecules, genes,
cells) to the very large (neural networks, organisms, behavior).
These difficulties often have to do with poor knowledge of the
parameters in many-parameter models which leads to bad
generalization and poor predictive power of the models. The
parameters have to be estimates from often huge data sets, too
complicated to analyze ``by hand'', and machine learning tools are
inevitable.
The goal of this class is that students acquire a profound
theoretical understanding of inference, learning and quantitative
modeling, together with the ability to use the mathematical
formalisms and algorithms that are relevant to problems in
bioinformatics / computational biology.
You will be expected to read original research literature and
present these research papers in class.
In a project, you will have a chance to use the quantitative
methods you are learning to work on a question in biology that
is of particular interest to you.
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Tentative: 2 assignments, 1 exam, 1 project. Guest lectures will
be announced on the course website. |
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Interdisciplinary Seminar Series
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You are cordially invited to join our new interdisciplinary seminar series
in Machine Learning, Computational Biology, and Bioinformatics.
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Problems that appear in very different disciplines can often be tackled in
similar ways, and an exchange across disciplines can greatly benefit
individual research projects. In particular, there are similar quantitative
needs for data analysis in many fields, ranging in scale from Microbiology
to Economics to Astronomy.
In biology, quantitative methods from theoretical physics and applied
mathematics have met with great success. In recent years, huge and complex data
sets have become available and machine learning methods are now essential.
The goal of this seminar series is to build a strong interdisciplinary
community here at UH for scientists working in the area of Machine Learning
and the area of Theoretical, or "Computational" Biology. These two areas
have overlaps that some of us are particularly interested in. However, we
would like to include relevant related work from other areas, and anyone
who is interested is welcome to join.
We will have both, talks given by UH scientists on their research, and
invited talks by speakers outside of UH. I would like to strongly encourage
you to present your work. Please send abstracts and convenient dates to
guylaine@hawaii.edu (bioinformatics) sstill@hawaii.edu (everything else).
Please feel free to forward this email to other scientists on campus, who
you think might be interested. There will be a mailing list to announce
future seminars, so please send an email with a request to be put on the
mailing list to sstill@hawaii.edu
We are starting the series with an invited talk by Dr. Chris Wiggins from
Columbia University, New York City, on "An information theoretic approach
to Spectral Graph Partitioning and Graph Modularity".
Chris has agreed to give a tutorial prior to his talk, to cover some
background. Please find the abstracts for both the tutorial and the talk
attached below.
The tutorial will be on MARCH 1, 2.30pm - 3.30pm in POST 302; the talk will
follow 3.45pm - 4.45pm (March 1, POST 302).
There will be coffee and cookies provided in the break, so come early if
you are coming for the talk only!!
Looking forward to seeing you there.
Aloha,
Susanna
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