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Home , Computational biology

CS 378 Introduction to Computational and Systems (Course #: 51249; online only; https://canvas.utexas.edu/)

Fall 2020 Instructor Prof. Stephen Yi, Ph.D. Email: [email protected]

Teaching Assistant (TA) Yihao Feng (graduate student at the Department of Science) Email: [email protected]

Lecture meeting times Tuesdays & Thursdays / 3:30-5pm

Office hours Prof. Yi: Tuesdays / 7-8am TA: Fridays / 11am-12pm

Course description The course will cover big data technology platforms, data science analytical and in computational biology/medicine and network science. Topics include DNA and search, high-throughput big data platforms and analysis, network science, multi-omics profiling, , motif finding, molecular structure prediction, -wide association studies, artificial intelligence and personalized precision medicine. Computational algorithms covered include hidden Markov model, clustering, classification methods and others.

Course objectives The overarching goal of the course is to help students develop skills for applying concepts from and statistics to analyze and solve biology, engineering and medical problems. Computational biology and as a skill is highly sought after by industry/biotech employers and academia. Our expectation is that at the end of the course, students will be able to identify key information in real scenarios and reformulate problems using bioinformatic and network concepts, and ultimately develop successful solutions.

Prerequisites It is assumed that all students have taken basic programming (such as CS 312) and algorithms (such as CS 331). Knowledge in calculus, statistics, and linear algebra will be helpful for this course. Strong mathematical skills are required.

Lecture format The course is organized as lectures, computer labs and presentation/discussion sessions. Lectures will introduce students to key concepts in computational biology, bioinformatics and network science in biology and medicine. No textbooks are needed for this course. Lecture slides will be posted before each class, and we encourage students to be well prepared before each class. Most lectures will include interactive exercises or in-class computer labs in which student participation is expected (TA will assist with these interactive exercises). These sessions may require the use of personal . We will teach some programming in python or R in the labs. The following is highly recommended: “Python programming for beginners” (https://www.codecademy.com/learn/learn-python)

1 Quizzes: 9 quizzes will be given on Thursdays (10 min; after lecture) on material covered in class since the last quiz. The worst quiz (or a quiz that was not attended) will be dropped from the grade. There are no make-ups for quizzes. Surveys: Two surveys will be designed to improve class topic coverage and applicability among students.

Assignments No exams will be given. The course assignments are 2 homework sets, 2 mini-projects, and 1 paper review. All assignments should be submitted through our Canvas course interface. Homework. Each homework will consist of problems to be solved, analyze data using published algorithms or your own design, and coding (in python or R). Two homework sets will be posted, announced, and graded (each due within 1 week). Mini-projects. To gain hands-on experience, students will conduct two mini-projects that are collaborations between 2-3 students as a group. A mini-project will consist of a computational biology/bioinformatics topic selected by the student group (with approval by the instructor) containing independent and statistics elements using the tools learnt from class. Project 1 is due by midterm, and Project 2 is due by the final week (schedule will be announced in class). The last two classes will be spent presenting your final project (Project 2) to the class. Students are expected to discuss project materials with TA or instructor, and send their presentation slides before the presenting date. Paper Review. A systematic literature review on a computational biology topic (needs approval by the instructor) will be assigned. The format and template will be provided in class.

No makeup assignments will be allowed. However, a total of 3 late days (counting weekends/holidays) is granted, which you can use towards any assignment, although any single assignment could not be late for more than 1 day. After the 3 days are used up, 10% points will be deducted for each additional late day.

Grading: A=[100-85], B=[70-84], C=[55-69], D=[40-54].

Score Times Quizzes 24 9 (3 points x 8 best) Homework sets 16 2 (8 points each) Mini-project 1 10 1 Mini-project 2 20 1 Presentation 6 1 Paper review 10 1 Surveys 6 2 (3 points each) Class attendance 8 Total 100

Academic integrity Students could discuss course material with each other and with the TA, but the submitted work must be your own work, except for the mini-projects. Your codes, reports and paper reviews will be analyzed by automatic tools that detect plagiarism to ensure that they are original. Students are expected to strictly follow the UT honor code. Cheating/plagiarism in report writing, and use of programs from other sources are all forbidden, and can cause for dismissal with a failing grade. (https://deanofstudents.utexas.edu/conduct/academicintegrity.php)

Diversity statement and special accommodation We strive to create an inclusive environment. We are proud to contribute to making this a welcoming class for everybody (http://www.utexas.edu/diversity/campus-culture/). If you prefer we use a different pronoun for 2 you, kindly let us know. The University of Texas at Austin provides, upon request, appropriate academic adjustments for qualified students with disabilities. For more information, please refer to http://deanofstudents.utexas.edu/ssd/.

Fall 2020 syllabus disclosures Safety Information: Stay up-to-date on COVID (https://coronavirus.utexas.edu/students) Sharing of Course Materials is Prohibited: No materials used in this class may be shared online or with anyone outside of the class. Any unauthorized sharing of materials will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students, and can result in sanctions, including failure in the course. FERPA and Class Recordings: Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings. COVID Guidance: To help keep everyone at UT and in our safe, it is critical that students report COVID-19 symptoms and testing, regardless of test results, to University Health Services, and faculty and staff report to the HealthPoint Occupational Health Program (OHP) as soon as possible. In addition, to help understand what to do if a fellow student in the class (or the instructor or TA) tests positive for COVID, see this University Health Services link: https://healthyhorns.utexas.edu/coronavirus_exposure_action_chart.html

Lecture outline 1. Code of Life: Programming for health/medicine 2. Big Data Era: Network science in biology/medicine 3. Artificial Intelligence: Robot Models for Life? 4. Human Genome: Sequence analysis and alignment 5. Detectives: Genotype-Phenotype relationships; Gene expression analysis 6. : Biological communication and pathway analysis 7. Network Structure: Clustering and hierarchy in life 8. Dimensional reduction and Principal component analysis: The “single ” modern biology 9. for Health and Medicine 10. : Application for better drug development

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