{"id":15805,"date":"2021-11-04T08:41:01","date_gmt":"2021-11-04T12:41:01","guid":{"rendered":"https:\/\/web.uri.edu\/cs\/?p=15805"},"modified":"2022-02-14T14:43:38","modified_gmt":"2022-02-14T19:43:38","slug":"derek-aguiar","status":"publish","type":"post","link":"https:\/\/web.uri.edu\/cs\/derek-aguiar\/","title":{"rendered":"Derek Aguiar"},"content":{"rendered":"

Bayesian nonparametric modelling and scalable inference in genomics and transcriptomics data<\/h2>\n

Date: November 19th, 2021 @ 3pm-4pm<\/h3>\n

Location: Beaupre Center for Chemical & Forensic Sciences<\/a>. Room 105<\/h3>\n

Host: Noah Daniels<\/h3>\n
\n
Derek Aguiar's Website<\/a>   <\/div>\n
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Derek Aguiar's Seminar<\/div><\/div>
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Talk Description<\/h1>

   <\/p>\n

Bayesian nonparametric models provide a formal mechanism for encoding probabilistic assumptions about the data generation process where the dimension of the latent space is unknown a priori or may grow with additional samples. A common limitation of these models is that posterior inference is computationally intensive, particularly for nonconjugate models or when integrating over combinatorial structures. In this talk, I will introduce two hierarchical Bayesian nonparametric models and inference algorithms that scale to large genomics and transcriptomics data.<\/p>\n

First, I will present our genetic sequence clustering model based on fragmentation coagulation processes and how we scale nonconjugate model inference using maximization-expectation. Then, I will describe our mixed-membership model for alternatively spliced transcript discovery with explicit sparsity and inference algorithms based on stochastic variational inference. I will demonstrate the advantages of our Bayesian nonparametric approach when compared to state-of-the-art methods on simulated and experimental data. Time permitting, I will describe recent extensions to our alternative splicing methods that integrate graph theoretic algorithms with probabilistic modelling.<\/p>\n

<\/p><\/div><\/section><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Bayesian nonparametric modelling and scalable inference in genomics and transcriptomics data Date: November 19th, 2021 @ 3pm-4pm Location: Beaupre Center for Chemical & Forensic Sciences. Room 105 Host: Noah Daniels   <\/p>\n","protected":false},"author":4601,"featured_media":15771,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[85],"tags":[],"class_list":["post-15805","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-seminars"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts\/15805","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/users\/4601"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/comments?post=15805"}],"version-history":[{"count":5,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts\/15805\/revisions"}],"predecessor-version":[{"id":15945,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts\/15805\/revisions\/15945"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/media\/15771"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/media?parent=15805"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/categories?post=15805"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/tags?post=15805"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}