Bioinformatics and statistics have become increasingly important in recent years as the complexity of the pathways being studied has expanded. We have three project in this PPG that are using high- throughput, genome wide analyses to generate large data sets. It is one of the roles of this core to help manage the large volumes of data being produced and make that data available in a manageable form for the members of the Stanford community, and then the rest of the scientific community as well. We will establish algorithms that can be used by the different groups to cluster expression profiles using unsupervised as well as supervised machine learning. We will also establish web-based interfaces with the Stanford community, and yeast consortium to facilitate the analysis of the homozygote deletion pool. Finally, we will work with the projects on all experiments in order to help establish exacting criteria of statistical power and significance.
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| Tandon, Neha; Thakkar, Kaushik N; LaGory, Edward L et al. (2018) Generation of Stable Expression Mammalian Cell Lines Using Lentivirus. Bio Protoc 8: |
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| Rafat, Marjan; Aguilera, Todd A; Vilalta, Marta et al. (2018) Macrophages Promote Circulating Tumor Cell-Mediated Local Recurrence following Radiotherapy in Immunosuppressed Patients. Cancer Res 78:4241-4252 |
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| Vilalta, Marta; Hughes, Nicholas P; Von Eyben, Rie et al. (2017) Patterns of Vasculature in Mouse Models of Lung Cancer Are Dependent on Location. Mol Imaging Biol 19:215-224 |
| Boyko, Tatiana V; Bam, Rakesh; Jiang, Dadi et al. (2017) Inhibition of IRE1 results in decreased scar formation. Wound Repair Regen 25:964-971 |
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