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CS 254: Computational Complexity

An introduction to computational complexity theory. The P versus NP problem; diagonalization and relativization; space complexity, Savitch's algorithm, NL=coNL, Reingold's algorithm; counting problem and #P-completeness; circuit complexity; pseudorandomness, derandomixation, and the Natural Proofs barrier; complexity of approximation; quantum computing. Prerequisites: 154 or equivalent; mathematical maturity.
Instructors: Trevisan, L. (PI)

CS 255: Introduction to Cryptography

For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (single and double key), digital signatures, pseudo-random bit generation, authentication, electronic commerce (anonymous cash, micropayments), key management, PKI, zero-knowledge protocols. Prerequisite: basic probability theory.
Terms: Win | Units: 3
Instructors: Boneh, D. (PI)

CS 261: Optimization and Algorithmic Paradigms

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms. Prerequisite: 161 or equivalent.
Terms: Win | Units: 3
Instructors: Plotkin, S. (PI)

CS 262: Computational Genomics (BIOMEDIN 262)

Applications of computer science to genomics, and concepts in genomics from a computer science point of view. Topics: dynamic programming, sequence alignments, hidden Markov models, Gibbs sampling, and probabilistic context-free grammars. Applications of these tools to sequence analysis: comparative genomics, DNA sequencing and assembly, genomic annotation of repeats, genes, and regulatory sequences, microarrays and gene expression, phylogeny and molecular evolution, and RNA structure. Prerequisites: 161 or familiarity with basic algorithmic concepts. Recommended: basic knowledge of genetics.
Terms: Win | Units: 3

CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)

Methods for modeling biomedical systems and for making those models explicit in the context of building software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Recommended: exposure to object-oriented systems, basic biology.
Terms: Aut | Units: 3
Instructors: Musen, M. (PI)

CS 271: Effective Design in Clinical Informatics Systems (BIOMEDIN 211)

Methods of designing and engineering software systems in complex clinical environments. Case studies illustrate factors leading to success or failure of systems. Project assignments involve focused team-based design work. Topics: user and organizational requirements, data and knowledge modeling, component-based system design, system prototyping, and human-systems interaction. Prerequisite: BIOMEDIN 210 recommended, or database or object-oriented programming course.

CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)

Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Prerequisites: BIOMEDIN 210, 211, 214, 217 or consent of instructor.
Terms: Aut | Units: 3
Instructors: Altman, R. (PI)

CS 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 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, Gibbs Sampling, 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. Prerequisites: programming skills; consent of instructor for 3 units.
Terms: Spr | Units: 3-4

CS 275: Translational Bioinformatics (BIOMEDIN 217)

Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of CS 106A and familiarity with statistics and biology.
Terms: Win | Units: 4
Instructors: Butte, A. (PI)
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