UC Santa Barbara

Sequence data are ubiquitous in diverse domains such as bioinformatics, computational neuroscience, and user behavior analysis. As a result, many critical applications require extracting knowledge from sequences in multi-level. For example, mining frequent patterns is the central goal of motif discovery in biological sequences, while in computational neuronal science, one essential task is to infer causal networks from neural event sequences (spike trains). Given the wide application of pattern and network mining tools for sequence data, they are facing new challenges posted by modern instruments. That is, as large scale and high resolution sequence data become available, we need new methods with better efficiency and higher accuracy.

In this dissertation, we propose several approaches to improve existing pattern and network mining tools to meet new challenges in terms of efficiency and accuracy. The first problem is how to scale existing motif discovery algorithms. Our work on motif discovery focuses on the challenge of discovering motifs from a large scale of short sequences that none of existing motif finding algorithms can handle. We propose an anchor based clustering algorithm that could significantly improve the scalability of all the existing motif finding algorithms without losing accuracy at all. In particular, our algorithm could reduce the running time of a very popular motif finding algorithm, MEME, from weeks to a few minutes with even better accuracy.

In another work, we study the problem of how to accurately infer a functional network from neural recordings (spike trains), which is an essential task in many real world applications such as diagnosing neurodegenerative diseases. We introduce a statistical tool that could be used to accurately identify inhibitory causal relations from spike trains. While most of existing works devote their efforts on characterizing the statistics of neural spike trains, we show that it is crucial to make predictions about the response of neurons to changes. More importantly, our results are validated by real biological experiments with a novel instrument, which makes this work the first of its kind.

Furthermore, while most existing methods focus on learning functional networks from purely observational data, we propose an active learning framework that could intelligently generate and utilize interventional data. We demonstrate that by intelligently adopting interventional data using the active learning models we propose, the accuracy of the inferred functional network could be substantially improved with the same amount of training data.