Research

I work in the area of computational biology and bioinformatics (CBB). It is a fascinating field of research under rapid growth, with an aim of using computational methods to study biological and medical phenomena.

The very first reason for the need of CBB is the huge amount of experimental data pending analysis and interpretation. For example,

1. Each human genome contains about 3 billion base pairs. If we have 1,000 such genomes, how should we store the data in order to save storage space and facilitate efficient access? How can we quickly identify all the differences between these long sequences? Among these differences, how do we pinpoint those that are associated with diseases? How can we tell their statistical and biological significance?
2. Within the human genome, there are 20,000-25,000 protein coding genes. How can we predict their functions and interactions based on the many different types of data available? How can we tell if the predictions are correct? How can we use the predictions to advance biological and medical research?

As you can see, CBB research touches on many areas of computer science, including data mining, machine learning, database management and algorithm design. There are many real-world applications, and there is an urgent need of new research to solve many evolving new problems.

Whole-genome identification of sequence elements

The genomes of human and many other organisms have been sequenced, yet understanding what the different parts of DNA do and their roles in the overall systems is still an ongoing endeavor. The ENCODE (Encyclopedia of DNA Elements) and modENCODE (ENCODE for model organisms) consortia combine the efforts of many academic and research institutes worldwide with an aim to identify and characterize the functional elements in genomes. We have been participating in the analysis working groups. Specific projects include the identification of non-coding RNAs, transcription factor binding sites and enhancers, and studying their functional roles.

Computational problems and techniques: discriminative learning

Selected publications:

• Lu and Yip et al., Prediction and Characterization of Non-coding RNAs in C. elegans by Integrating Conservation, Secondary Structure and High Throughput Sequencing and Array Data. Genome Research, vol. 21, no. 2, pp. 276-285, (2011).
• The modENCODE consortium, Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project. Science, vol. 330, no. 6012, pp. 1775-1787, (2010).

Reconstruction of biological networks

Biological objects do not work alone, but rather interact with other objects to form complex networks. Cataloging these interactions is a first step to understand the functions of individual objects and the biological systems as a whole. Specific interactions have been identified and studied thoroughly, but high-throughput techniques for probing whole networks are still catching up with the high data quality required for in-depth analyses. We have been using computational methods to integrate different types of data with a goal of reconstructing the not yet fully known biological networks with high precision and coverage.

Computational problems and techniques: graph learning, data integration, kernel methods, probabilistic modeling, time-series analysis

Selected publications:

• Yip et al., Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data. PLoS ONE, vol. 5, issue 1, no. e8121, (2010).
• Yip et al., Multi-level Learning: Improving the Prediction of Protein, Domain and Residue Interactions by Allowing Information Flow between Levels. BMC Bioinformatics, vol. 10, no. 241, (2009).
• Yip et al., Training Set Expansion: An Approach to Improving the Reconstruction of Biological Networks from Limited and Uneven Reliable Interactions. Bioinformatics, vol. 25, no. 2, pp. 243-250, (2009).
• Yip et al., The tYNA Platform for Comparative Interactomics: A Web Tool for Managing, Comparing and Mining Multiple Networks. Bioinformatics, vol. 22, no. 23, pp. 2968-2970, (2006).

Prediction of functionally coupled objects through co-evolutionary analysis

Biological objects that are functionally coupled, such as protein domains that interact, are restrained from independent evolutionary events that prohibit them from normal interactions. Instead, they may undergo co-evolution, in which the fitness loss due to one of the evolutionary events is restored by the other event. Taking this idea in the reverse direction, by looking for objects that display co-evolutionary patterns, we could discover functionally coupled objects and advance our understanding of the corresponding biological pathways.

Computational problems and techniques: statistical modeling, information theory, correlation analysis

Selected publications:

• Yip and Utz et al., Identification of Specificity Determining Residues in Peptide Recognition Domains using an Information Theoretic Approach Applied to Large-Scale Binding Maps. BMC Biology, (in press).
• Yip and Patel et al., An Integrated System for Studying Residue Coevolution in Proteins. Bioinformatics, vol. 24, no. 2, pp. 290-292, (2008).

Mining useful information from data with uncertainty

Many types of data contain certain uncertainty due to factors such as low measurement resolution, noise and staleness. The uncertainty could hinder or mislead the mining of useful information. Taking data uncertainty and related information such as repeated measurements into account in the mining process could help uncover hidden patterns. We have been designing algorithms to handle data uncertainty for a variety of data mining problems.

Computational problems and techniques: clustering, classification, pattern mining, data structures, data pruning

Selected publications:

• Yip et al., Mining Order-Preserving Submatrices from Data with Repeated Measurements. IEEE Transactions on Knowledge and Data Engineering (TKDE), (in press).
• Ngai et al., Metric and Trigonometric Pruning for Clustering of Uncertain Data in 2D Geometric Space. Information Systems, vol. 36, issue 2, pp. 476-497, (2011).
• Tsang et al., Decision Trees for Uncertain Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 23, no. 1, pp. 64-78, (2011).

Tool developments

• : Analyzing network topology.
• Coevolution: Studying the coevolution signal between residues of a protein.
• ACT: For aggregation, correlation and saturation of signal tracks from genomic experiments.