Dennis F. Kibler
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Position:
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Professor Emeritus
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Area:
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Artificial
Intelligence
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Office:
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Bren Hall 4044
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Email:
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dfkibler70@gmail.com
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Office Hours:
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By appointment
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Genomic Analysis via Machine Learning Methods
These research projects are being done in collaboration with a number of
faculty from the School of Biological Science and the College of Medicine.
For validation of the computational approach each project is focussed on a
particular genome.
- Promoter modelling.
Collaborator: Ming Tan. Students: Hilda Yu and Johnny Ackers. We have
developed a bimotif variable gap model that has been successful at
predicting the sigma-28 promoter site in Chlamydia and E. coli
Extensions of this model will be applied to search for dimer binding
sites.
- Hydra database Collaborators: Hans
Bode, Robert Steele, and Steve Hampson. Analysis and data from Hydra ests,
as they are being sequenced. Currently we have about 140,000 ests
available and new sequences are added monthly. The main goals are to
identify genes, their functions, and their evolutionary histories.
- Discovery of Chlamydial
Transcription factor Binding Sites Collaborator: Ming Tan. Student:
Bob Chan. Chlamydia is an unusual bacterial that lives entirely within
human cells. We are developing algorithms that combine various forms of
evidence to identify the transcription binding sites that control the
early and late genes.
In general our goal is to develop programs that can
use the available data to help determine biological significant substructures
or patterns in genomes.
Current Graduate Advisees and Projects
·
Johnny Akers (Bio phd student): Advisor Ming
Tan. Identifying regulatory elements in Chlamydia arising from dimerization.
·
Martin Brandon
Advisor Pierre Baldi MitoMap
Graduate students completing
with doctorate
- Bob Chan (2007)
Discovery of Local Patterns in DNA that Predict CRMs and Protein
Structural Similarity
- Hilda Yu (2006)
co-advisor with Ming Tan
- Catherine Blake
(2003), Assistant Professor, University
of North Carolina, Chapel Hill
- Yuh-Jyh Hu (1999),
Assistant Professor in Computer Science and Engineering Department, Tatung University,
Taipei.
- Piew Datta (1997),
Researcher at GTE Research Laboratory.
- Pedro Domingos (1997),
Associate Professor, University
of Washington.
- Pat Murphy (1996),
Reseacher Scientist at JPL.
- David Ruby (1993),
Independent Consultant.
- David Aha (1990),
Researcher at Naval Research Laboratory.
- Etienne Wenger (1990),
Research scientist at the Institute for Research on Learning, Xerox Park
- Rogers Hall (1990),
Associate Professor at Vanderbilt, Department of Education.
- Douglas Fisher (1987),
Associate Professor at Vanderbilt
University.
- Paul Morris (1984),
Researcher at Intellicorp, Menlo
Park, California.
- Bruce W. Porter
(1984), Professor at University
of Texas.
- Steven Hampson (1983),
Research Scientist at UCI.
- John Conery (1983),
Professor at University
of Oregon.
- Stephen Fickas (1983),
Professor at University
of Oregon.
Notable Undergraduates
- Kimberly
Ferguson(2003) Computing Research Award (honorable mention)
Undergraduate Advisees and Projects
- Frank Chen: Building a
monad and dyad motif detector.
- Timothy Uy: (High
school student) Evaluating Genetic Algorithm on BSAT
- Bach Ho: Extending
Weka with improved Nearest Neighbor and k-means algorithms
- Gary Suh: Diagnose Colon cancer using
Protein expression data (Ciphergen)
- Vishahk: Creating a
web-interface and mysql database for HYDRA data.
- David Hu: Building a
dimer binding site detector for chlamydia trachomatis.
Course Offerings:
Cosmos: Data Mining
- Course for Gifted High
School Students
- Geometric and
intuitive approach to three problems in Data Mining
- Classification
- Regression
- Clustering
- Course will use the
freely available Weka software
- Prerequisites:
Comfortable with algebra, geometry and computers
- No programming
experience required
Cosmos
(2005) BioInformatics: Understanding our Genome
The goal of this course is to introduce students to
the bioinformatics research paradigm by studying three significant problems in
understanding the genome. The paradigm consists of starting with a biological
problem, biological data and biological knowledge and then building an
approximate computational model. The model is then applied to real data and its
predictions are then validated by additional biological experiments. Ideally
this cycle repeats. The course will begin with the problem of identifying genes
from sequence data using the GENSCAN program. After genes are identified, their
function will be hypothesized using the BLAST program. How genes are regulated
will be answered using programs for finding surprising subsequences. In all of
these studies students will work with real data using programs freely available
on the web. The ideas underlying the algorithms, their limitations, and their
connection to biology will be stressed.
Student Projects with draft abstracts
- Homosexuality:
Determined by genetics or social environment by Mansi Shah and Wendy
Kim
- Applying Microarray
Technology to predicting Adverse drug reaction in children with acute
lymphoblastic leukemia by Ruwani Ekanayake, Amy Henry, and Brittany
Horth
Abstract: Pharmacogenomics, the study of how an individual's
genetic inheritance affects the body's response to drugs, has expanded exponentially
since the completion of the Human Genome Project in 2003. Now, thanks to
emerging microarray technology, pharmacogenomics is being applied with
great success to cancer research. Our project will explore the benefits
of microarray technology in pharmacogenomics, specifically as applied to
cancers.
- Stem Cells by Micheal
Jenkins, Uchechukwu Nnadi, and Tom Garrett
- Predicting SNPs and
Their Effects on Proteins using SIFT and Polyphen by Eve Shih and Kuo
- Predicting Leukemia
Classes Based on gene expression data using WEKA by Alex Doo,
Stehpanie Lang . and George Quinonez
- Similarity Sequencing
and its use for Generation of Phylogenetic Trees by Ivan Cvitkovic and
Daniel Kaufman
Abstract: We plan to explore the concept of similarity sequencing.
Through our research, we will discuss the different algorithms for
similarity sequencing and a basic description of how they work.
Additionally we will discuss the application of similarity sequencing for
the generation of phylogenetic trees. During our project we will generate
two separate phylogenetic trees, based on different proteins.
- Pharmacogenomics and
Bioinformatcis of Long QT Syndrome by Daryl Serrano and Carlos
Palacios .
Abstract: Long QT syndrome is a genetic disease that affects the
heart. One's heart has electrical activity that is made by a flow of
ions. This causes the heart to beat normally. People with LQTS have a
slower QT interval (heartbeat) than normal. People can acquire LQTS
through prescription/over the counter medications or inherit the disease
from their parents at birth. LQTS can cause many symptoms in a person
that can greatly affect their everyday lives. However, there are
treatments for LQTS that help people live normal lives. Acquired LQTS is
caused by medications that include antihistamines, antidepressants,
mental illness medications, heart medications, etc. LQTS is also
congenital, or caused by a mutation in the gene that forms ion channels.
There are different classifications of LQTS, such as LQT1, LQT2, LQT3,
LQT4, and LQT5. These different forms of LQTS affect different ion
channels in the human heart. When LQTS slows one's heart beat, one is
likely to faint, have a seizure, or die. One will most likely experience
these symptoms when exercising or becomes emotionally excited. Although
LQTS may be fatal, doctors have found treatments that help shorted the QT
interval. and allow patients to live normal lives.
- How Breast Cancer
response to chemotherapy by Amanda Farrar and Rosalinda Ruiz
Abstract:< Chemotherapy is very important in metastasis Breast
Cancer. Doxorubicin, epirubicin, Paclitaxel, and docetaxle are the most
active chemical drugs used in Breast Cancer treatment. Activating
tumor-suppressor genes are emerging as important targets for therapy.
Researchers test various models to see which combinations of drugs
improve Breast Cancer treatment. By understanding the molecular processes
underlying Breast Cancer, researchers hope to create specific drugs that
will prevent the growth of Breast Cancer. The malfunction of genes allows
cancer to spread to various parts of the bodies. Understanding these
processes will help find a cure for this disease.
- Title: What is DEB and
how is it treated by Maritza Navarro and Alexandria Magallan
Abstract: Dystrophic epidermolysis bullosa (DEB) is caused by a
mutation in the collagen, type VII alpha 1 protein, which is found in
chromosome 3. Type VII collagen, is a protein that helps keep your skin
intact. Those who have dystrophic epidermolysis bullosa are usually
diagnosed at birth. Patients with the disease have "butterfly
skin" which is so thin that any minor trauma to the skin causes it
to blister and scar. The disease is a rare genetic disease that can be
both recessive and dominant. If a person has dominant DEB then he or she
has no real cure except for the nurture from those who care for them. But
those who have recessive DEB, may have a chance since there has recently
been 4 cases of skin graphs, or cutaneous pinch grafts, that helps to
heal the wounds of the patients.
- Similarity Sequencing
and its use for phylogenetic trees by Ivan Cvitkovic and Daniel Kaufman.
- The genetic causes and
physiological effect of sickle cell anemia by Alda Caan and Blanca
Trujillo
- How gene therapy may
be applied to the curing genetic diseases by Michael Jenkins and
Uchechukwu (Uli)Nnadi.
Freshman Seminar: Artificial Intelligence: Is it
for Real?
- Discussion of
historical and significant papers in Artificial Intelligence dealing with
the creating and evaluation of computational artifacts that do or do not
exhibit intelligence. Students will be encouraged to suggest their own
approaches to the problems. Brainstorming and constructive evaluation
will be encouraged.
- Prerequisite: ICS21
with a grade of B.
- Readings: Significant papers will be
chosen, corresponding to student interest. If you miss class, then you
can pick up the paper outside my office. Also if you miss class, you are
required to write a one paragraph comment on the reading which you should
send to me via email.
- First Paper: the Turing test (1950)
- Syllabus
- Meetings Wednesdays:
3-3:50, CSE 310.
- First Class: April 2;
Last Class: June 4
- Grading: Grades are
based on class participation and written assignments. Attendance is not
sufficient. Each week you are expected to hand in a one paragraph comment
on the reading.
H22 Honors Introduction to Computer Science II
- Introduction to basic
abstract data structures and associated algorithms, including their
implementation, selection, and complexity. Data structures include lists,
stacks, queues, tables, and trees.
- Prerequisite: H21 or
consent of instructor
- Required Texts:
- Data Structures and
Algorithms with Object-Oriented Design Patterns in Java by Bruno Priess.
- Core Java (2nd ed) by
Horstmann and Cornell
- Recommended: Any book
on Java that works for you. Many students like Core Java 2. Java Texts .
- Syllabus
- Ethics
Honors 23 Problem Solving and Data Structures
- Further analysis of
basic and non-basic data structures and associated algorithms. With
respect to representation, covers arrays, lists, trees and graphs. With
respect to algorithms covers recursion, divide-and-conquer, backtracking,
and dynamic programming.
- Prerequisite: H22 or
consent of instructor
- Required Text: Data
Structures and Problem Solving in Java
- Author: Mark Allen
Weiss
- Recommended for Swing:
Up to Speed with Swing by Steven Gutz.
- Syllabus
- Homework Details
- Ethics
ICS 23 Problem Solving and Data Structures
- Further analysis of
basic and non-basic data structures and associated algorithms. With
respect to representation, covers arrays, lists, trees and graphs. With
respect to algorithms covers recursion, divide-and-conquer, and
separate-and-conquer,
- Prerequisite: ICS22 or
consent of instructor
- Required Text: Data
Structures and Problem Solving in Java
- Author: Mark Allen
Weiss
- Suggested for Gui's: Up
to Speed with Swing by Steven Gutz.
- Syllabus
- Homework Details
- Ethics
171 Introduction to Artificial
Intelligence (4)
- The course is divide
into four major topics and we will spend about two weeks on each topic.
The major topics are: Problem Solving via search, Logical reasoning in
propositional and first-order logic, Probabilistic Reasoning and
Learning.
- Prerequisites: ICS 52
and and Mathematics 2A-B and 67.
- Text: Artificial
Intelligence: A Modern Approach
- Authors: Stuart
Russell and Peter Norvig
- Grading. There will be
4 quizzes,2 coding assignments, and 3 written homeworks. The quizzes will
each count 15% of your grade. The lowest homework/coding score will be
dropped. Scores on late homework will be reduced by 20% per day.
- Class Lectures: MWF
2:00-2:50 CS174
- Time: June 28 - Sept
3.
- Teaching Assistant:
Rajyashree Mukherjee mukherjr@uci.edu
- Course email:
36490-M04@classes.uci.edu
- Discussion Section:
Wed 3:00-3:50 CS174
- Syllabus and Lectures
- Ethics
- Homework Details Future homeworks will be
collected in class and returned via the distribution center.
172 Programming Techniques in Artificial
Intelligence (4) W.
- The study of the
object-oriented design as applied to AI algorithms and representations.
The goal is to create an in-depth understanding of some of the important
AI approaches by coding various algorithms in an object-oriented
language. On the coding side, we will examine graphical displays,
user-interfaces, and code libraries. On the AI side, we will implement
algorithms for problem-solving, optimization, decision-making, and
learning. There will be three to five coding/design assigments. The code
will be in Java or Python.
- Prerequisites: ICS
171, knowledge of object-oriented programming
- Texts: Any Java text
you like plus any AI text you like.
- Syllabus plus fuller description of course.
- Programming Language:
Java
- Ethics
CS 174 BioInformatics (4) S.
- Meetings:
M-W-F: 11-11:50 ELH 110
- Office Hours:
M-W: 9-10 and by appointment. Room 414D CS
- First Class:
Monday, April 3
- Last Class:
Friday, June 9
- Final: Friday,
June 16, 8am -10am
- Questions: Do
not hesitate to ask questions in class, in my office hours or by email. Do
hesitate to ask questions on the weekends - that's family time for me.
Untimely questions may not be answered.
- Text:
Fundamental Concepts of BioInformatics by Dan Krane and Michael Raymer
- Course Mailing List:
TBD
- Teaching Assistant:
Daniel Sanchez (valencid@ics.uci.edu)
- Teaching Assistant
Office hours: Wed- Fri, 10-11, TA office in the ICS Trailers
- Grading: There
will be eight assignments plus one quiz and a final. The lowest score on
a assignment will be dropped. The final will count 20% of your grade, the
quiz 10% and the best seven of your assignment scores will each count
10%. Homeworks can be turned in one day late for 1/2 credit. All
homeworks are due a week after the assignment on Monday by 10 am. If it
is turned in at 10:01, then you can only get at most 1/2 credit.
Homeworks will be deposited using the checkmate program.
- Course Goals:
Bioinformatics is the study of biological problems via computational
tools. The goal of the course is to provide students with sufficient
biological knowledge and computational methods that they can use,
understand, and possibly generate algorithms that are appropriate for
available biological data. This course will concentrate on approaches
that deal with the most voluminous and accurate data, namely DNA data, protein
data, and gene-expression data. As part of the course students will use
current tools on existing databases to a) locate genes and determine
their function, b) build phylogenic trees, c) locate regulatory elements
and d) predict protein structure.
- Overview of Course:
Problems in Biology and Computational Approaches
Week 1: Basic structure of DNA, RNA, genes and proteins.
Week 2&3: Methods: Dot Matrices, Local, Global and Multiple
Alignment algorithms.
Problems: Gene identification and gene function
Week 4&5. Methods: Tree building: UPGMA, clustering, maximum
likelihood
Problems: Tree of Life: fitting all organism into an evolutionary history
Week 6&7: Methods: Pattern discover by search (exhaustive and
heuristic)
Problems: Gene regulation in Prokaryotes and Eukaryotes
Week 8&9: Methods: Dynamic Programming, machine learning
Problems: Determine Secondary and Tertiary structure of RNA and Proteins.
Week 10: Methods: Machine Learning
Problem: Determining proteomic disease diagnoses.
- Course Workload:
Expect weekly assignments. You will have four assignments where you
implement basic algorithms for a particular biological problem. These
assignments will alternate with using existing, more sophisticated
algorithms for related tasks. In particular you will write algorithms for
finding genes, determining similarity between DNA and protein sequences,
building evolutionary trees, and locating regulatory elements. I will
provide a sketch or design for these algorithms so that they are all
implementable in one afternoon plus, at least for me, another afternoon
for debugging. Only the next homework assignment is guarantee to be
correct - other assignments may change as the class progresses.
- Quiz: Multiple
choice and fill in the blank. Monday of the second week of classes. This
will be based on chapter 1 of the text (pages 1-20) plus the lectures.
For computer scientist learning the basic vocabulary of molecular biology
is somewhat difficult. I recommend reading the chapter at least twice,
making note of the important concepts.
- First Assignment
to hand in: Due the beginning of the 3rd week of classes. The assignment
has two parts for submission: a coding part and question part. The
assignment is due by 10am on Monday of the third week of classes. In
general assignment are due by 10am on Monday of week after the assignment
is made. Assignments are to be deposited in the appropriate folder using checkmate.
Most of you have already used this software, but just in case: To set up
for electronic submission, go to checkmate.ics.uci.edu, log in with your
UCInet ID, choose "Course Listing" for "Spring 2006,"
click "Go" next to ICS 174, and then click "List me for
this course." For the answers to questions, you will submit a word
file hwkn.doc file. For the coding part, when they occur, submit a single
hwkn.java. To do this you will need collect your java files into a single
file. You may also do the homework in Python. I reserve the right to
change assignments as the course progresses.
- Assignment Details
CS 175A Projects in
Artificial Intelligence (4) S.
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Meetings: MWF 3:00-3:50 SE2 1304
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Office Hours: MW before and after class and by
appointment.
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Course Mailing List: 36485-F05@classes.uci.edu
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Course Project web site TBD
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Final paper and code due TBD.
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Teaching Assistants: James Worcester
(jworcest@uci.edu) Office hours: TBD
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Poster Presentations (Power-point) with demonstration;
Date to be decided.
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First Class: September 23 Last Class: December 2
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Work Load: Several presentations, interim
documents, design documents, website, power-point presentation, Java code, and
a final paper. The final paper should include at least two references, either
to papers or texts. The final presentation will be done in PowerPoint and
should include a demonstration and evaluation of the program. Each project will
have an associated WebSite
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Grading: All students in a group will assign
credit to one another. The individual grade will be based on the total project
grade (decided by me) distributed as group members decided.
§
In this course students will build Java programs
that demonstrate AI techniques. The potential topics include Expert Systems,
Natural Language Processing, Problem Solving, Search, Game Playing, Learning,
Reasoning, Perception, etc. A more specific but still incomplete list of
potential projects is listed under AI projects. One common approach would be to
implement some AI method and apply it to some domain. The AI text by Russell
and Norvig provides many examples of possible projects.
Each student will be involved in one project done
in a group of three people. In the project each object will be identified with
its author. All students are responsible for some of the code. The programs
generated will be made public so that all can view, use, and evaluate. Students
will be responsible for maintaining their programs. There will be an emphasis
on design so every project will go through a design review in which the entire
class participates. Each group will be responsible for several presentations:
the basic goal, the history on the problem, the design, and the final
demonstration with evaluation. Each group is also responsible for a report that
corresponds to each presentation.
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Prerequisites: ICS 171, knowledge of Java
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AI Projects.
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Work Schedule.
§
Ethics
202 Seminar in Research in ICS (2) F.
Graduate orientation program and colloquium series.
Includes talks by ICS faculty in all areas about their current research.
Satisfactory/Unsatisfactory Only. Formerly ICS 295.
209 Seminar in Bioinformatics(2)
Graduate seminar in which recent papers and
research are discussed and analyzed. Papers will concentrate on analysis of
genomic and gene expression data. Supporting papers on biology, machine
learning, and statistics may also be included. It is likely that each enrolled
person will present two papers. The first presentation will be informal and
will likely include necessary background material. The second presentation will
be a formal one, done either via PowerPoint or overheads. During the second
presentation background material can be assumed.
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ICS 249 Mondays12-2pm (Note new place and
time)
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Bioinformatic Papers
CS 270A: Introduction to Artificial Intelligence
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Introduction to basic AI representations and
algorithms, problem solving, planning, logical and probabilistic reasoning,
natural language processing and learning.
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Text: Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig 2nd Edition. I suggest ordering this
from Amazon.
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Lecture Time: Tues/Thurs 9:30 - 10:50 Room:
cs253
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Office hours: 11-12 tues/thurs and by
appointment.
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First class: Sept 30 2003. Last
class: Dec. 4 2003.
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Grading: Three coding assignments and a final:
all weighted equally.
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Final: Thursday Dec 11 8am-10am
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Click here for
lectures and assignments.
CS273: Machine Learning (4).
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Computational approaches to learning,
concentrating on classification and regression. Covers standard learning
representations (rules, decision trees, instances, linear threshold units,
neural nets, etc), their representation, limitations, and evaluation.
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Prerequisite: ICS 270A. Formerly ICS 275.
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Required Text: Introduction to Data Mining
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Authors: Pang-Ning Tan, Michael Steinbach, Vipin
Kumar
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Publisher: Addison-Wesley ISBN 0-321-32136-7
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Recommended Text: Data Mining: Practical Machine
Learning Tools and Techniques with Java Implementations. 2nd Edition.
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Authors: Ian H. Witten and Eibe Frank.
Publisher: Morgan Kaufman
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Recommended Text: Machine Learning by Tom
Mitchell. Publisher: McGraw-Hill
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First Class: Jan 10. Last class: Thursday March
16
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Final: Thursday March 23 8am - 10am
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Lectures: tues-thurs 9:30-10:50
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Room: CS-213
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Office Hours: tuesday/thursday after class and
by appointment.
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Syllabus
§
Ethics
277A Representations and
Algorithms for Molecular Biology(4)
o
The primary goal of this class is to introduce
molecular biologists to computer science and computer scientist to molecular
biology. It is not expected that biologist will become programmers, but they
will learn what might be accomplished with computational analysis. Nor is it
expected that computer scientists will conduct biological experiments, but they
will learn enough biology to understand the important problems in biology that
are addressable by computational means.
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Required Text: Bioinformatics: Sequence and
Genome Analysis by David W. Mount 2nd Edition
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Recommended Text: Introduction to Computation
Molecular Biology by Sebutal and Meidanis
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Recommended (undergraduate text): Fundamental
Concepts of Bioinformatics by Krane and Kramer
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First Class: Thursday Jan 8, 2006. Last class:
Thursday March 16
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Final: Thurs Dec 12, 1:30-3:30pm
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Meetings: Tuesday/Thursday 9:30- 10:50 CS253
§
Course Work: Weekly readings from the text and
papers, a few homeworks, and a final project.
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Course Mailing List: 36774-F05@classes.uci.edu
§
Project: A joint project and presentation
teaming a biologist with a computer scientist.
§
Syllabus
280 Seminar in Computational Biology
This is an advanced course that concentrates on recent computational methods
that aid in the functional analysis of genomes. Papers will be drawn primarily
from Bioinformatics, Proceedings of the Conference on Intelligent Systems for
Molecular Biology, Journal of Molecular Biology, Proceedings of the National
Academy of Science, Science, and the Journal of Computational Biology. The
course will focus on algorithms that find patterns in DNA, particularly those
that relate to gene regulation, such as finding regulatory elements, finding
promoters, and clustering gene expression data. There is no text. We will all
be reading, discussing, and presenting papers as well as any
research-in-progress.
Java Demos:
o
Traveling Salesman. Not available now.
This
Java (JDK1.0) program illustrates the use of hill-climbing to solve the
traveling saleman program. The program is simple so that the general ideas can
be understood. Many valuable extensions are possible. The program only uses one
operator, that of uncrossing edges that intersect. Additional operators are
useful. The general problem of defining useful operators for hill-climbing is
unsolved. Simulated annealing, multiple restarts, better initialization would
all be useful. If your browser executes JDK1.1, then you might prefer the
following similar demo: Not available now.
o
Dynamic Programming. Dynamic
Programming
This program illustrate the use of dynamic programming to find the minimum edit
distance between two strings. This distance depends on the cost one associates
with various edit operations. It can be used for spell checking but major
applications are in comparing amino acid and nucleotide sequences. If you look
at the source,the main action is in the routine doSimMatch. This was one of my
first Java programs, so the program can be greatly improved.
o
Kmean clustering. Not available now.
You'll
see that is doesn't always work. Requires JDK1.1.
o
Valdimir Vapnik's applet on Support Vector
Machines . This is a beautiful applet that displays the probability
density function associated with the generated decision surface.
o
N Queens
Problem. Not available now.
N Queens
problem is solved by local improvement or repair search. This same method is
most applied to scheduling problems, where it can generate anytime and
approximate solutions. Coded in ics175 by Son Tran.
Publications
2006
In silico prediction and functional validation of
sigma-28-regulated genes in Chlamydia and E. coli. Yu, H.H.Y., Ming Tan, and
Dennis Kibler. Journal of Bacteriology. Online at JB01082-06
2005
Using Hexamers to Predict Cis-Regulatory Modules in
Drosophila. Bob Chan and Dennis Kibler, BMC Bioinformatics.
http://www.biomedcentral.com/1471-2105/6/262 6: 262, October 2005.
2004
A horizontally transferred protist gene in
the Hydra genome Figure 1 Current
Biology. Robert E. Steele, Steven E. Hampson, Nicholas A. Stover, Dennis F.
Kibler, Hans R. Bode. Volume 14, number 8.
2003
Using DNA MicroArrays to Identify SP1 as a
Transcription Regulatory Element of Insulin-Like Growth Factor in Cardiac
Muscle Cells. Circulation Research. Tao Li, Yung-Hsiang Chen, Tsun-Jui Lui,
Jia Jia, Steven Hampson, Yue-Xin Shan, Dennis Kibler, Ping H. Wang. pp 1-35.
2003
Evaluating Representations for the Shine-Dalgarno
Site in Escherichia coli Steven Hampson and Dennis Kibler. TR#03-14.
School of Information and Computer Science. University of California,
Irvine.
2002
Characterizing the E. coli Shine-Dalgarno Site:
Probability Matrices and Weight Matrices Dennis Kibler and Steven Hampson,
International Conference on Mathematical and Engineering Techniques in Medicine
and Biological Science ( METMBS-2002). pp. 358-364.
Distribution Patterns of over-represented k-mers
in non-coding yeast DNA Steven Hampson, Dennis Kibler, and Pierre Baldi,
BioInformatics. vol. 18 no.4 pp. 513-528.
2001
Learning Weight Matrices for Identifying
Regulatory Elements, Dennis Kibler and Steven Hampson, International
Conference on Mathematical and Engineering Techniques in Medicine and
Biological Science ( METMBS-2001). pp. 208-214.
Bay, S. D., Kibler, D., Pazzani, M. J., and Smyth, P. (2001). The UCI KDD
Archive of Large Data Sets for Data Mining Research and Experimentation. In
Information Processing Society of Japan Magazine. Volume 42, Number
5, pages 462-466. English language version reprinted in SIGKDD Explorations.
Volume 2, Issue 2, pp. 81-85, 2000.
2000
Analysis of Yeast's ORF Upstream Regions by
Parallel Processing, Microarrays, and Computational Methods. Steve Hampson,
Pierre Baldi, Dennis Kibler, and Suzanne Sandmeyer. Tenth International
Conference on Intelligent Systems for Molecular Biology ( ISMB-2000). pp.
109-201.
Combinatorial Motif Analysis and
Hypothesis Generation on a Genomic Scale , with Yuh-Jyh Hu, Suzanne
Sandmeyer, Calvin McLaughlin. BioInformatics, Vol 16, 222-232.
1999
Minimum Generalization via Reflection: A Fast
Linear Threshold Learner , with Steven Hampson, Machine Learning 37
pp. 51-73. 1999.
Detecting Motifs from Sequences with Yuh-Jyh Hu
and Susan Sandmeyer, International Conference on Machine Learning 1999.
1997
Symbolic Nearest Mean Classifiers , with Piew
Datta, AAAI-97.
Learning Symbolic Prototypes , with Piew Datta,
ICML-97.
GalaII: Integrating Construction of Boolean and
Prototypical Features , with Yuh-Jyh Hu, ECML-97 (in press).
1996
A Generative Approach to Constructive Induction ,
with Yuh-Jyh Hu, AAAI-96.
1995
``Plateaus and Plateau Search in Boolean Satisfiability
Problems: When to Give Up Searching and Start Again,'' with Steven Hampson.
DIMACS Challenge, 1995.
Learning Prototypical Concept Descriptions ,
with Piew Datta, Twelfth International Conference on Machine Learning.
1993
``Learning recurring subplans'' with David Ruby. in Machine Learning Methods
for Planning, Minton, S., 466--497, Morgan Kaufman, 1993.
Concept Sharing: A Means to Improve Multi-Concept
Learning , with Piew Datta, Machine Learning Conference.
1992
``The Utility of Knowledge in Inductive Learning'', with Michael Pazzani,
Machine Learning, 9, 57--94, 1992.
Utilizing Prior Concepts , with Piew Datta,
Machine Learning Workshop on Bias.
1991
``Instance-Based Learning Algorithms'', with David Aha and Marc Albert, Machine
Learning, 37--66, 1991.
``SteppingStone: An Empirical and Analytic Evaluation'', with David Ruby,
Proceedings of the Ninth National Conference on Artificial Intelligence,
527--531, Morgan Kaufmann, 1991.
1990
``Machine Learning as an Experimental Science'', with Pat Langley. Readings in Machine
Learning, Dietterich, T., and Shavlik, J. (eds.), 38--43, Morgan Kaufmann,
1990.
1989
``Instance-Based Prediction of Real-Valued Attributes'', with David Aha and
Marc Albert, Computational Intelligence: an International Journal, Vol 6, 3,
51--57, 1989.
``Exploring the Episodic Structure of Algebra Story Problem Solving'', with
Rogers Hall, Etienne Wenger, and Chris Truxaw, Cognition and Instruction, 1989.
1986
``Experimental Goal Regression A Method for Learning Problem Solving
Heuristics'', with Bruce Porter, Machine Learning 3, 245--289, 1986.
1985
``Differing Methodological Perspectives in Artificial Intelligence Research'',
with Rogers P. Hall, Artificial Intelligence Magazine, Volume 6, Number 3, pp.
166-178, August 1985.
Professional Activities:
Reviewer for Bioinformatics, Machine Learning, KDD, IEEE
Scientific Advisor for Oncotech.
CEP and UCEP member.
Other Interests:
Reading, bridge, hiking.
Information and Computer Science
University of California, Irvine CA 92717-3425
Last modified: May 16, 2005