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1 - 10 of 30 results for: GENE ; Currently searching autumn courses. You can expand your search to include all quarters

GENE 199: Undergraduate Research

Students undertake investigations sponsored by individual faculty members. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit

GENE 200: Genetics and Developmental Biology Training Camp (DBIO 200)

Open to first year Department of Genetics and Developmental Biology students, to others with consent of instructors. Introduction to basic manipulations, both experimental and conceptual, in genetics and developmental biology.
Terms: Aut | Units: 1

GENE 202: Human Genetics

Utilizes lectures and small group activities to develop a working knowlege of human genetics as applicable to clinical medicine. Basic principles of inheritance, risk assessment, and population genetics are illustrated using examples drawn from diverse areas of medical genetics practice including prenatal, pediatric, adult and cancer genetics. Practical aspects of molecular and cytogenetic diagnostic methods are emphasized. Existing and emerging treatment strategies for single gene disorders are also covered. Prerequisites: basic genetics. Only available to MD and MOM students.
Terms: Aut | Units: 4

GENE 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, CS 274)

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

GENE 215: Frontiers in Biological Research (BIOC 215, DBIO 215)

Students analyze cutting edge science, develop a logical framework for evaluating evidence and models, and enhance their ability to design original research through exposure to experimental tools and strategies. The class runs in parallel with the Frontiers in Biological Research seminar series. Students and faculty meet on the Tuesday preceding each seminar to discuss a landmark paper in the speaker's field of research. Following the Wednesday seminar, students meet briefly with the speaker for a free-range discussion which can include insights into the speakers' paths into science and how they pick scientific problems.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit

GENE 217: Translational Bioinformatics (BIOE 217, BIOMEDIN 217, CS 275)

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: Aut | Units: 4

GENE 219: Current Issues in Genetics

Current Issues in Genetics is an in-house seminar series that meets each Academic Quarter tor one hour per week (Friday, 4:00-5:00) and features talks by Genetics Department faculty, students, and postdoctoral fellows (with occasional visiting speakers from other Stanford departments). Thus, over the Academic Year, it provides a comprehensive overview of the work going on in the Department. Student attendance at the seminars will be required, with short written assignments (typically three per Quarter) to encourage thinking about the material presented in the talks.
Terms: Aut, Win, Spr | Units: 1

GENE 224: Principles of Pharmacogenomics (BIOMEDIN 224)

This course is an introduction to pharmacogenomics, including the relevant pharmacology, genomics, experimental methods (sequencing, expression, genotyping), data analysis methods and bioinformatics. The course reviews key gene classes (e.g., cytochromes, transporters) and key drugs (e.g., warfarin, clopidogrel, statins, cancer drugs) in the field. Resources for pharmacogenomics (e.g., PharmGKB, Drugbank, NCBI resources) are reviewed, as well as issues implementing pharmacogenomics testing in the clinical setting. Reading of key papers, including student presentations of this work; problem sets; final project selected with approval of instructor. Prerequisites: two of BIO 41, 42, 43, 44X, 44Y or consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 3

GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B)

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 CS 109, and basic machine learning such as CS 229. No prior knowledge of genomics is necessary.
Terms: Aut | Units: 3

GENE 260: Supervised Study

Genetics graduate student lab research from first quarter to filing of candidacy. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit
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