Mehmet Tan

Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks

Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.

Combining multiple types of biological data in constraint-based learning of gene regulatory networks

Due to the complex structure and scale of gene regulatory networks, we support the argument that combination of multiple types of biological data to derive satisfactory network structures is necessary to understand the regulatory mechanisms of cellular systems. In this paper, we propose a simple but effective method of combining two types of biological data, namely microarray and transcription factor (TF) binding data, to construct gene regulatory networks. The proposed algorithm is based on and extends the well-known PC algorithm. Further, we developed a method for measuring the significance of the interactions between the genes and the TFs. The reported test results on both synthetic and real data sets demonstrate the applicability and effectiveness of the proposed approach; we also report the results of some comparative analysis that highlights the power of the proposed approach.

Positive unlabeled learning for deriving protein interaction networks

Binary classification is the process of labeling the members of a given data set on the basis of whether they have some property or not. To train a binary classifier, normally one needs two sets of examples from each group, usually named as positive and negative examples. However, in some domains, negative examples are either hard to obtain or even not available at all. In these problems, data consist of positive and unlabeled examples. This paper first presents a survey of algorithms which can handle such problems, and then it provides a comparison of some of these algorithms on the protein–protein interaction derivation problem by using the available (positive) interaction information.

Automated Large-Scale Control of Gene Regulatory Networks

Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem (FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.

Large-scale approximate intervention strategies for Probabilistic Boolean Networks as models of gene regulation

Control of Probabilistic Boolean Networks as models of gene regulation is an important problem; the solution may help researchers in various different areas. But as generally applies to control problems, the size of the state space in gene regulatory networks is too large to be considered for comprehensive solution to the problem; this is evident from the work done in the field, where only very small portions of the whole genome of an organism could be used in control applications. The Factored Markov Decision Problem (FMDP) framework avoids enumerating the whole state space by representing the probability distribution of state transitions using compact models like dynamic bayesian networks. In this paper, we successfully applied FMDP to gene regulatory network control, and proposed a model minimization method that helps finding better approximate policies by using existing FMDP solvers. The results reported on gene expression data demonstrate the applicability and effectiveness of the proposed approach.

Prediction of anti-cancer drug response by kernelized multi-task learning

MOTIVATION Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim. OBJECTIVE Data produced by using cancer cell lines provide a test bed for machine learning algorithms that try to predict the response of cancer cells to different agents. The potential use of these algorithms in drug discovery/repositioning and personalized treatments motivated us in this study to work on predicting drug response by exploiting the recent pharmacogenomic databases. We aim to improve the prediction of drug response of cancer cell lines. METHODS We propose to use a method that employs multi-task learning to improve learning by transfer, and kernels to extract non-linear relationships to predict drug response. RESULTS The method outperforms three state-of-the-art algorithms on three anti-cancer drug screen datasets. We achieved a mean squared error of 3.305 and 0.501 on two different large scale screen data sets. On a recent challenge dataset, we obtained an error of 0.556. We report the methodological comparison results as well as the performance of the proposed algorithm on each single drug. CONCLUSION The results show that the proposed method is a strong candidate to predict drug response of cancer cell lines in silico for pre-clinical studies. The source code of the algorithm and data used can be obtained from http://mtan.etu.edu.tr/Supplementary/kMTrace/.

Prediction of influenza outbreaks by integrating Wikipedia article access logs and Google flu trend data

Prediction of influenza outbreaks is of utmost importance for health practitioners, officers and people. After the increasing usage of internet, it became easier and more valuable to fetch and process internet search query data. There are two significant platforms that people widely use, Google and Wikipedia. In both platforms, access logs are available which means that we can see how often any query/article was searched. Google has its own web service for monitoring and forecasting influenza-illness which is called the Google Flu Trends. It provides estimates of influenza activity for some countries. The second alternative is Wikipedia access logs which provide the number of visits for the articles on Wikipedia. There are papers which work with these platforms separately. In this paper, we propose a new technique to use these two sources together to improve the prediction of influenza outbreaks. We achieved promising results for both nowcasting and forecasting with linear regression models.

Employing Machine Learning Techniques for Data Enrichment: Increasing the Number of Samples for Effective Gene Expression Data Analysis

For certain domains, e.g. bioinformatics, producing more real samples is costly, error prone and time consuming. Therefore, there is a need for an intelligent automated process capable of substituting the real samples by artificial samples that carry the same characteristics as the real samples and hence could be used for running comprehensive testing of new methodologies. Motivated by this need, we describe a novel approach that integrates Probabilistic Boolean Network and genetic algorithm based techniques into a framework that uses some existing real samples as input and successfully produces new samples as output. The new samples will inspire the characteristics of the existing samples without duplicating them. This leads to diversity in the samples and hence a more rich set of samples to be used in testing. The developed framework incorporates two models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a high demanding area that has not received attention. The two perspectives employed in the process are based on models that are not closely related, the independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

Scalable approach for effective control of gene regulatory networks

OBJECTIVE Interactions between genes are realized as gene regulatory networks (GRNs). The control of such networks is essential for investigating issues like different diseases. Control is the process of studying the states and behavior of a given system under different conditions. The system considered in this study is a gene regulatory network (GRN), and one of the most important aspects in the control of GRNs is scalability. Consequently, the objective of this study is to develop a scalable technique that facilitates the control of GRNs. METHOD As the approach described in this paper concentrates on the control of GRNs, we argue that it is possible to improve scalability by reducing the number of genes to be considered by the control policy. Consequently, we propose a novel method that considers gene relevancy to estimate genes that are less important for control. This way, it is possible to get a reduced model after identifying genes that can be ignored in model-building. The latter genes are located based on a threshold value which is expected to be provided by a domain expert. Some guidelines are listed to help the domain expert in setting appropriate threshold value. RESULTS We run experiments using both synthetic and real data, including metastatic melanoma and budding yeast (Saccharomyces cerevisiae). The reported test results identified genes that could be eliminated from each of the investigated GRNs. For instance, test results on budding yeast identified the two genes SWI5 and MCM1 as candidates to be eliminated. This considerably reduces the computation cost and hence demonstrate the applicability and effectiveness of the proposed approach. CONCLUSION Employing the proposed reduction strategy results in close to optimal solutions to the control of GRNs, which are otherwise intractable due to the huge state space implied by the large number of genes.

Feature Reduction for Gene Regulatory Network Control

Scalability is one of the most important issues in control problems, including the control of gene regulatory networks. In this paper, we argue that it is possible to improve scalability of gene regulatory networks control by reducing the number of genes to be considered by the control policy; and consequently propose a novel method to estimate genes that are less important for control. The reported test results on real and synthetic data demonstrate the applicability and effectiveness of the proposed approach.