Browsing by Author "Henao, R"

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Open Access
Latent protein trees
(Annals of Applied Statistics, 2013-06-01) Henao, R; Thompson, JW; Moseley, MA; Ginsburg, GS; Carin, L; Lucas, JE
Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza. © Institute of Mathematical Statistics, 2013.
Open Access
Learning a hybrid architecture for sequence regression and annotation
(30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016-01-01) Zhang, Y; Henao, R; Carin, L; Zhong, J; Hartemink, AJ
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.When learning a hidden Markov model (HMM), sequential observations can often be complemented by real-valued summary response variables generated from the path of hidden states. Such settings arise in numerous domains, including many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible framework for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is compatible with a rich set of mapping functions. Results show that the availability of additional continuous response variables can simultaneously improve the annotation of the sequential observations and yield good prediction performance in both synthetic data and real-world datasets.
Open Access
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing.
(CoRR, 2018) Shen, D; Su, Q; Chapfuwa, P; Wang, W; Wang, G; Carin, L; Henao, R
Open Access
Non-Gaussian discriminative factor models via the max-margin rank-likelihood
(32nd International Conference on Machine Learning, ICML 2015, 2015-01-01) Yuan, X; Henao, R; Tsalik, EL; Langley, RJ; Carin, L
Copyright © 2015 by the author(s).We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new max-margin version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-likelihood and (linear) Bayesian support vector machines, which are also built on the max-margin principle. The discriminative factor model is further extended to the nonlinear case through mixtures of local linear classifiers, via Dirichlet processes. Fully local conjugacy of the model yields efficient inference with both Markov Chain Monte Carlo and variational Bayes approaches. Extensive experiments on benchmark and real data demonstrate superior performance of the proposed model and its potential for applications in computational biology.