Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach

A significant amount of search queries originate from some real world information need or tasks. In order to improve the search experience of the end users, it is important to have accurate representations of tasks. As a result, significant amount of research has been devoted to extracting proper representations of tasks in order to enable search systems to help users complete their tasks, as well as providing the end user with better query suggestions, for better recommendations, for satisfaction prediction, and for improved personalization in terms of tasks. Most existing task extraction methodologies focus on representing tasks as flat structures. However, tasks often tend to have multiple subtasks associated with them and a more naturalistic representation of tasks would be in terms of a hierarchy, where each task can be composed of multiple (sub)tasks. To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks \& subtasks. We evaluate our method based on real world query log data both through quantitative and crowdsourced experiments and highlight the importance of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape

Inferring interactions, expression programs and regulatory networks from high throughput biological data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (leaves 171-180).(cont.) For the networks level I present an algorithm that efficiently combines complementary large-scale expression and protein-DNA binding data to discover co-regulated modules of genes. This algorithm is extended so that it can infer sub-networks for specific systems in the cell. Finally, I present an algorithm which combines some of the above methods to automatically infer a dynamic sub-network for the cell cycle system.In this thesis I present algorithms for analyzing high throughput biological datasets. These algorithms work on a number of different analysis levels to infer interactions between genes, determine gene expression programs and model complex biological networks. Recent advances in high-throughput experimental methods in molecular biology hold great promise. DNA microarray technologies enable researchers to measure the expression levels of thousands of genes simultaneously. Time series expression data offers particularly rich opportunities for understanding the dynamics of biological processes. In addition to measuring expression data, microarrays have been recently exploited to measure genome-wide protein-DNA binding events. While these types of data are revolutionizing biology, they also present many computational challenges. Principled computational methods are required in order to make full use of each of these datasets, and to combine them to infer interactions and discover networks for modeling different systems in the cell. The algorithms presented in this thesis address three different analysis levels of high throughput biological data: Recovering individual gene values, pattern recognition and networks. For time series expression data, I present algorithms that permit the principled estimation of unobserved time-points, alignment and the identification of differentially expressed genes. For pattern recognition, I present algorithms for clustering continuous data, and for ordering the leaves of a clustering tree to infer expression programs.by Ziv Bar-Joseph.Ph.D

Probabilistic hierarchical clustering for biological data

Biological data, such as gene expression profiles or protein sequences, is often organized in a hierarchy of classes, where the instances assigned to “nearby ” classes in the tree are similar. Most approaches for constructing a hierarchy use simple local operations, that are very sensitive to noise or variation in the data. In this paper, we describe probabilistic abstraction hierarchies (PAH) [11], a general probabilistic framework for clustering data into a hierarchy, and show how it can be applied to a wide variety of biological data sets. In a PAH, each class is associated with a probabilistic generative model for the data in the class. The PAH clustering algorithm simultaneously optimizes three things: the assignment of data instances to clusters, the models associated with the clusters, and the structure of the abstraction hierarchy. A unique feature of the PAH approach is that it utilizes global optimization algorithms for the last two steps, substantially reducing the sensitivity to noise and the propensity to local maxima. We show how to apply this framework to gene expression data, protein sequence data, and HIV protease sequence data. We also show how our framework supports hierarchies involving more than one type of data. We demonstrate that our method extracts useful biological knowledge and is substantially more robust than hierarchical agglomerative clustering. 1

Probabilistic Hierarchical Clustering for Biological Data

Biological data, such as gene expression profiles or protein sequences, is often organized in a hierarchy of classes, where the instances assigned to "nearby" classes in the tree are similar. Most approaches for constructing a hierarchy use simple local operations, that are very sensitive to noise or variation in the data. In this paper, we describe probabilistic abstraction hierarchies (PAH) [11], a general probabilistic framework for clustering data into a hierarchy, and show how it can be applied to a wide variety of biological data sets. In a PAH, each class is associated with a probabilistic generative model for the data in the class. The PAH clustering algorithm simultaneously optimizes three things: the assignment of data instances to clusters, the models associated with the clusters, and the structure of the abstraction hierarchy. A unique feature of the PAH approach is that it utilizes global optimization algorithms for the last two steps, substantially reducing the sensitivity to noise and the propensity to local maxima. We show how to apply this framework to gene expression data, protein sequence data, and HIV protease sequence data. We also show how our framework supports hierarchies involving more than one type of data. We demonstrate that our method extracts useful biological knowledge and is substantially more robust than hierarchical agglomerative clustering