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johnpeach committed Aug 26, 2019
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Topic modeling in cancer patients' mutational profiles

https://lda-mutations.netlify.com/#1

https://github.com/zhiiiyang
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# Topic modeling in cancer patients' mutational profiles

* 2019-07-30
* Speaker: Zhi Yang

## Abstract

Topic models allow us to access the contribution of each topic and its
representations across different documents. Human genomes have been
exposed to an assortment of mutational processes by contributing to
unique patterns of somatic mutations. What would happen if we apply
the same concept to the somatic mutations obtained from the cancer
patients and look for “topics” of mutations? I will introduce a simple
example of Latent Dirichlet Allocation (LDA) and its application in
investigating cancer patients' mutational profiles in addition to
available Bayesian tools in R to conduct statistical inference.
3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -6,8 +6,9 @@ Presentation materials from our monthly meetings

## 2019 ##

* 08/27/2019: *Olga Korosteleva*, [Regression Models for Count Data](https://github.com/ocrug/presentations/tree/master/2019-08-27_regression_models_for_count_data/)

* 08/27/2019: *John Peach*, [Regression Models for Count Data](https://github.com/ocrug/presentations/tree/master/2019-08-27_regression_models_for_count_data/)
* 07/30/2019: *Zhi Yang*, [Topic modeling in cancer patients' mutational profiles](https://github.com/ocrug/presentations/tree/master/2019-07-30_extracting_topics_from_cancer_patients_mutational_profiles)

* 07/30/2019: *John Peach*, [Error and Condition Handling in R](https://github.com/ocrug/presentations/tree/master/2019-07-30_error_and_condition_handling_in_R/)

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