printer friendly pageCS 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A)
Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing (technologies, assembly, personalized sequencing); functional landscape (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures from genomic researchers. No prerequisites. See
http://cs273a.stanford.edu/.
Terms: Aut
| Units: 3
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)
Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS109, and basic machine learning such as
CS229. No prior knowledge of genomics is necessary.
Terms: Aut
| Units: 3
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Devorah, A. (TA)
;
Hussami, N. (TA)
;
Ramesh, J. (TA)
CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214)
Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisite:
CS 106B; recommended:
CS161; consent of instructor for 3 units.
Terms: Aut
| Units: 3-4
CS 275: Translational Bioinformatics (BIOE 217, BIOMEDIN 217)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of
CS 106A and familiarity with biology and statistics.
Terms: Win
| Units: 4
Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
;
Gloudemans, M. (TA)
;
McInnes, G. (TA)
;
Shenoy, A. (TA)
CS 275A: Symbolic Musical Information (MUSIC 253)
Focus on symbolic data for music applications including advanced notation systems, optical music recognition, musical data conversion, and internal structure of MIDI files.
Terms: Win
| Units: 2-4
Instructors:
Rodriguez Marquez, M. (PI)
;
Selfridge-Field, E. (PI)
CS 275B: Music Query, Analysis, and Style Simulation (MUSIC 254)
Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.
Terms: Spr
| Units: 2-4
Instructors:
Rodriguez Marquez, M. (PI)
;
Selfridge-Field, E. (PI)
CS 276: Information Retrieval and Web Search (LINGUIST 286)
Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. Prerequisites:
CS 107,
CS 109,
CS 161.
Terms: Spr
| Units: 3
Instructors:
Manning, C. (PI)
;
Nayak, P. (PI)
CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CME 279)
Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (
CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut
| Units: 3
CS 294A: Research Project in Artificial Intelligence
Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor.
Last offered: Winter 2012
| Repeatable
for credit
CS 294H: Research Project in Human-Computer Interaction
Student teams under faculty supervision work on research and implementationnof a large project in HCI. State-of-the-art methods related to the problemndomain. Prerequisites
CS 377, 147, 247, or permission from instructor.
Last offered: Winter 2010
Filter Results: