若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類

Name: NGUYEN HAO CANH
Title: Latent Feature Models for Biological Knowledge Discovery
Institute: 京都大学化学研究所バイオインフォマティクスセンター
Partner institute of your choice： Centre for Computational Biology, Mines ParisTech
Duration of your choice: Dec 15th, 2012 ~ Mar 14th 2013

Background
It is the objective to find the common latent features that generate patterns from different datatypes of biological entities (genes, proteins, chemical compounds, networks). These latent features
are candidates for explaining biological mechanisms. However, due to its constraints, the problemof finding latent features is computationally infeasible. The aim of this proposal is to contribute to
the process of making it possible to construct and train the models approximately or accurately onlarge biological data. The research falls in the categories of Machine Learning/Data Mining andBioinformatics. The final target is to use the models to understand biological processes such asprotein-protein interactions, gene regulations, subnetwork, pathway formations, gene/chemicalcompound relationships.
These days, a massive amount of biological data is available from high throughput experiments
such as sequencing and microarray. Traditional experimental data are also collected and organizedin comprehensive databases. This is the chance to study Biology from systems level, to look at
biological mechanisms involving multiple entities at the same time. This poses many problems incomputational sciences, statistical sciences and so on. This research is in this area, trying to makesense out of the vast biological data.
The collected biological data are of different natures, making traditional analyzing methods notreadily usable. Sequential data, for example, needs to be transformed into Euclideanrepresentations, inducing extremely high dimensions while most of them are unrelated to theproblem. To make the matter worse, spurious knowledge may be inferred due to the dimensionality
problem. This is also the problem of time series of microarray data, selecting and combining
appropriate features are usually very hard computationally. Data from interactomes (set of all
biological interactions), on the other hand, are relational data. While traditional experiments only
give individual interactions (such as from one protein to another), having a large number ofinteractions opens challenging problems. That is, by looking at the whole interactomes asnetworks, it gives rise to network analysis problems that infer additional knowledge that cannot beinduced otherwise.
Biological processes usually involve multiple steps, such as pathways, requiring a throughout
investigation of many entities at different times. To understand the whole processes, integrated
models are required. There are needs to construct models for complexly structured data, involving
many data types into the same framework. As the entities are diverse, we have to deal with very
sparse data. As data are collected from independent experiments for different needs, from thewhole systems' viewpoint, the data are generated with unknown distributions and statistical models
need to take this fact into account.
The final goal is to present to biologists comprehensible explanations of biological mechanisms forverification. To this end, one must be able to describe the common mechanisms through different
data types involved. For example, the phenomenon of a protein interacting to another protein (PPI)
may involve some network substructures, requiring subsequences of the DNA and having certainchanges in gene expressions. These are the patterns uniquely associated to the phenomenon. It iscommon in Statistics that these patterns are hypothesized to be generated by certain latent features.
A typical example is a PPI is determined by a pair of interacting sites that interact to each other,and the interacting sites generates the patterns in PPI networks and expression profiles. Our
research covers three aspects: statistical models for biological data, optimization methods for thesemodels and biological validation of the computed results.

若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 応募書類Plan
Our aim is to develop different statistical models specifically for the nature of biological data. As
the nature of collected data are sequences, time series or networks, the statistical models of thebiological systems have particular characteristics that need to take into account. First, they are ofvery high dimension. However, a biological phenomenon is usually indicated by a combination ofobserved features (a latent feature). This results in large structured models in the family ofBayesian or Markovian models. These models are difficult to train as they need combinatorial
optimization algorithms. General methods of learning these models such as structured sparse
approximation, graphical lasso are general-purposed and require an exponentially increasing
amount of data. To address these issues, we specifically plan to investigate the following topics.
1. Latent features transfer: Since the models involve many variables, maximum likelihood or

general Bayesian estimations may overfit the training data. It is the problem to incorporate
suitable prior knowledge into the models. The priors are biological knowledge such asknown relationships between entities or from closely-related species. For this purpose, weplan to design graphical models of biological processes that reflect the partial knowledge
such as known subnetworks of the models or similar networks for closely-related species on
the phylogenetic trees, i.e., the networks that are close to the known networks of closelyrelated species. These problems belong to the categories of predicting missing edges ortransfer learning. In these models, the known subnetworks or the parts of networks, that aresimilar to other species, become the latent features of the biological processes.
2. Inducing networks with latent features: Given the problem of high dimension, statisticalestimations need to be regularized. Sparsity is one of the common regularizers that induce
reliable estimators. To apply to biological processes, sparsity is usually in form of sparse
networks. However, the usual sparsity-inducing methods do not impose any conditions on
the inducing models. In biological networks, the networks are known to follow special
patterns. This is a gap between general methods of structured sparse estimation andbiological networks estimation. This poses different problems. One is the problem ofdesigning regularizers for graphical models that induce the networks that are close toknown ones. Another problem is to design regularizers for graphical models that induce
networks having the properties of biological networks.
3. Integrated models for latent features: It is generally a dilemma on latent features of the two

representations: networks and Euclidean data types (such as transformed from sequences ortime series data). On the one hand, features of network structures are used to predict
relationship of expressions of genes. On the other hand, sequences or gene expressions areused to predict network structures. In the case of similarity networks, it is to put both
representations into the same space. However, it is extremely difficult to put them into thesame space for non-similarity networks. Depending strongly on the type of the networks,
the relationship between the two representations must be defined accordingly. It is our aim
here to provide an integrated models of latent features of networks and of Euclidean datafor general network types.