Characterization of pharmacological signal transductions leading to drug-induced expressions of genes and proteins requires the capability to identify interactions among different potential predictor components, e.g. genomic data, clinical data, and environmental data. The detection of these gene-gene and gene-environment interactions remains challenging due to the exponential computational complexity and high dimensionality of the interaction problem. The problem is further exacerbated due to the involvement of very large-scale epidemiological datasets. Efficient high-order interaction analysis of such large-scale data is not feasible with the traditional frameworks. Parallel implementations of such applications in traditional cluster environments are often inefficient due to the storage bandwidth and network I/O limitations. Scalable distributed platforms can offer better scalability to such problems compared to the cluster architectures. Moreover, such data- and compute- intensive problems can benefit even further from data-intensive supercomputing (DISC) architectures that have been shown to yield superior performance compared to the regularly used cluster platforms. In this paper, we evaluate the applicability of different architectures such as traditional server based distributed architectures supported on commodity hardware and shared nothing architectures with massively parallel processing capabilities, towards the Interaction Analysis problem. Our experiments show that the massively parallel processing, shared-nothing architecture outweigh the benefits often realized through traditional server based and even distributed computing architectures. We conclude that the rapidly growing class of shared nothing architectures offers a potentially efficient and viable alternative to facilitate high-order interaction analysis involving extremely large scale biological datasets and is well suited to this category of data- and compute- intensive problems that cannot be addressed effectively using traditional frameworks.

With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not only models the progression of the disease, but also predicts the risk of the disease for early disease control or prevention. Existing models for answering these questions usually fall into two categories: the expert knowledge based model or the handcrafted feature set based model. To fully utilize the whole EHR data, we will build a framework to construct an integrated representation of features from all available risk factors in the EHR data and use these integrated features to effectively predict osteoporosis and bone fractures. We will also develop a framework for informative risk factor selection of bone diseases. A pair of models for two contrast cohorts (e.g., diseased patients versus non-diseased patients) will be established to discriminate their characteristics and find the most informative risk factors. Several empirical results on a real bone disease data set show that the proposed framework can successfully predict bone diseases and select informative risk factors that are beneficial and useful to guide clinical decisions.

With the rapid development of healthcare industry, the overwhelming amounts of electronic health records (EHRs) have been well documented and shared by healthcare institutions and practitioners. It is important to take advantage of EHR data to develop an effective disease risk management model that not only predicts the progression of the disease, but also provides a candidate list of informative risk factors (RFs) in order to prevent the disease. Although EHRs are valuable sources due to the comprehensive patient information, it is difficult to pinpoint the underlying causes of the disease in order to assess the risk of a patient in developing a target disease. Because of the entangled EHR data, it is also challenging to discriminate between patients suffering from the disease and without the disease for the purpose of selecting RFs that cause the disease. To tackle these challenges, we propose a disease memory (DM) framework which can extract the integrated features by modeling the relationships among RFs and more importantly between RFs and the target disease by establishing a deep graphical model with two types of labels. The variants of DM can model characteristics for patients with disease and without disease respectively via training deep networks with different samples. Experiments on a real bone disease data set show that the proposed framework can successfully predict the bone disease and select the informative RFs that are beneficial and useful to aid clinical decision support. Most of the selected RFs are validated by medical literature and some new RFs will attract interests in medical research. The stable and promising performance on evaluation metrics confirms the effectiveness of our model.

Bones are firm and solid organs that form the skeleton of the body. Bone consists of living tissues with which bone remodeling occurs asynchronously at various sites and involves resorption by osteoclast, followed by formation of new bone by osteoblast. Although bone is a simple composite of a mineral structure, its structure highly contributes to the strength of the bone. In this paper, we develop a computational framework for microstructural bone dynamics modeling which is capable of quantitative assessment of bone mineral density and bone micro-architecture. Our paper focuses on bone microstructure and remodeling dynamics based on a bone network model. First, we generate a bone network based on our bone model reflecting bone microstructure. Next, we introduce a mathematical model of autocrine and paracrine interactions among osteoblast and osteoclast. It allows us to calculate cell population dynamics and changes in bone mass at multiple sites of bone remodeling. Last, we analyze two bone networks representing healthy bone and bone with osteoporosis with our evaluation measurements. Our study provides an initial framework of bone remodeling simulation that can explain experimental observations in bone biology to explore bone diseases such as osteoporosis.

Identification of a critical location in complex structures is one of the most important issues that affects the quality of safety in human life. Specifically, finding high fracture spots of a bone microstructure in our body is a very important research topic; however, it is still not well understood. In this paper, we study on identifying critical locations in a bone microstructure with our bone network model. In fact, about 25 million people in the United States suffer from osteoporosis, which is a systemic skeletal disease characterized by low bone mass and micro-architectural deterioration of bone tissue leading to enhanced bone fragility and a consequent increase in fracture risk. However, currently available techniques for the diagnosis of osteoporosis and the identification of critical locations of bone microstructure are limited. We create a bone network model based on properties of a bone microstructure and we develop a method, called information propagation, to identify critical locations in a bone network. Our paper focuses on detecting important edges as critical locations for the strength of bone microstructure when there are external forces applied to the bone network. Then, we evaluate results of the method in comparison with existing methods including the weighted betweenness centrality and the weighted bridge coefficient. We conclude with the discussion on advantages and disadvantages among those methods.

Knowledge of the association information between the attributes in a data set provides insight into the underlying structure of the data and explains the relationships (independence, synergy, redundancy) between the attributes. Complex models learnt computationally from the data are more interpretable to a human analyst when such interdependencies are known. In this paper, we focus on mining two types of association information among the attributes – correlation information and interaction information which capture multivariate dependencies between the data attributes. Identifying the statistically significant attribute associations is a computationally challenging task – the number of possible associations increases exponentially and many associations contain redundant information when a number of correlated attributes are present. In this paper, we explore efficient data mining methods to discover non-redundant attribute sets that contain significant association information indicating the presence of informative patterns in the data.

This research presents a multi agent model to estimate and predict the spread of Avian Influenza (AI) in various attributes and environments in a given population. AI can be transmitted by air and is a critical hazard to birds, especially chickens and ducks. The virus occurs naturally in birds and is capable of being transmitted from an infected bird to another. It is conceivable that AI could be a major threat to human health if the virus becomes capable of transmission to human beings. The next outbreak of AI could lead to millions of deaths unless a feasible strategy for AI containment can be developed.This paper focuses the flexibility that a multi agent system offers. Agent-based models can closely mimic the situations that exist in real system where several autonomous components may be interacting with each other. The modeling approach offers the advantage of examining the interactions between the agents. This research studies the interactions of three critical factors that characterize AI outbreaks. These properties are quarantine range, incubation period and infection probability. The multi agent model investigates the nature of spreading of AI by incorporating these three properties. We illustrate the potential benefits of multi agent modeling in containing the spreading of AI by presenting how efficiently the virus can be contained. Our work exploits data on the 2008 outbreak of AI in South Korea.

Despite the pervasiveness of networks as models for real world systems ranging from the Internet, the World Wide Web to gene regulation and scientific collaborations, only a limited number of metrics capable of characterizing these systems are available. The existing metrics for characterizing networks have broad specificity and lack the selectivity for many applications. The purpose of this paper is to identify and critically evaluate a metric, termed bridging centrality, which is highly selective for identifying bridges in networks. The properties of bridges are unique compared to the other network metrics. For a diverse range of data sets, we found that networks are highly susceptible to disruption but robust to loss structural integrity upon targeted deletion of bridging nodes. A novel graph clustering approach, termed `bridge cut', utilizing bridging edges as module boundary is also proposed. The modules identified by the bridge cut algorithm are more effective than the other graph clustering methods. Thus, bridging centrality is a network metric with unique properties that may aid in network analysis from element to group level in various areas including systems biology and national security applications.

BACKGROUND: The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage. RESULTS: Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO) database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy. In particular, it handles the high false positive rates of current interaction data well. CONCLUSION: The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available.

BACKGROUND: Quantitative characterization of the topological characteristics of protein-protein interaction (PPI) networks can enable the elucidation of biological functional modules. Here, we present a novel clustering methodology for PPI networks wherein the biological and topological influence of each protein on other proteins is modeled using the probability distribution that the series of interactions necessary to link a pair of distant proteins in the network occur within a time constant (the occurrence probability). RESULTS: CASCADE selects representative nodes for each cluster and iteratively refines clusters based on a combination of the occurrence probability and graph topology between every protein pair. The CASCADE approach is compared to nine competing approaches. The clusters obtained by each technique are compared for enrichment of biological function. CASCADE generates larger clusters and the clusters identified have p-values for biological function that are approximately 1000-fold better than the other methods on the yeast PPI network dataset. An important strength of CASCADE is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches which have an average discard rate of 45% on the yeast protein-protein interaction network. CONCLUSION: CASCADE is effective at detecting biologically relevant clusters of interactions.

High-throughput techniques for protein-protein interaction detection in a genomic scale have provided us a genomic wide view of molecular interactions of many living organisms. A few approaches were proposed to scrutinize protein-protein interactions of living organisms. By the way, the binary nature of the current protein interaction data sets imposes challenges for effective analysis. Furthermore, their performance was suffered by the intrinsic defect, i.e., high noise level, of high-throughput data. This unpleasantly high false positive rate could lead many devoted researches to erroneous biological conclusions. We propose a novel reliability measurement for protein interactions integrating the similarity in gene ontology and the topological similarity in protein interaction networks. Our metric has been proven to be an effective reliability metric for identifying biologically more reliable interactions through the analyses performed from various view points, e.g., functional homogeneity, subcellular localizational homogeneity, and gene expression correlation, etc.

BACKGROUND: The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. RESULTS: We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. CONCLUSION: The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

MOTIVATION: The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets. RESULTS: VizStruct is an interactive visualization technique that reduces multi-dimensional data to two dimensions using the complex-valued harmonics of the discrete Fourier transform (DFT). In the 3D VizStruct extension, the multi-dimensional SNP data vectors are reduced to three dimensions using a combination of the DFT and the Kullback-Leibler divergence. The performance of 3D VizStruct was challenged with several biologically relevant published datasets that included human Chromosome 21, the human lipoprotein lipase (LPL) gene locus and the multi-locus genotypes of coral populations. In every case, the 3D VizStruct mapping provided an intuitive visual description of the key characteristics of the underlying multi-dimensional genotype.

PURPOSE: To evaluate a semi-parametric, model-based approach for obtaining transcription rates from mRNA and protein expression. METHODS: The transcription profile input was modeled using an exponential function of a cubic spline and the dynamics of translation; mRNA and protein degradation were modeled using the Hargrove-Schmidt model. The transcription rate profile and the translation, and mRNA and protein degradation rate constants were estimated by the maximum likelihood method. RESULTS: Simulated datasets generated from the stochastic, transit compartment and dispersion signaling models were used to test the approach. The approach satisfactorily fit the mRNA and protein data, and accurately recapitulated the parameter and the normalized transcription rate profile values. The approach was successfully used to model published data on tyrosine aminotransferase pharmacodynamics. CONCLUSIONS: The semi-parametric approach is effective and could be useful for delineating the genomic effects of drugs. AVAILABILITY: Code suitable for use with the ADAPT software program is available from the corresponding author. CONTACT: [email protected].

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene-sample-time microarray data sets, which records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene-Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters.

MOTIVATION: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are relevant makes it difficult to construct an informative gene space. Unsupervised empirical sample pattern discovery and informative genes identification of such sparse high-dimensional datasets present interesting but challenging problems. RESULTS: A new model called empirical sample pattern detection (ESPD) is proposed to delineate pattern quality with informative genes. By integrating statistical metrics, data mining and machine learning techniques, this model dynamically measures and manipulates the relationship between samples and genes while conducting an iterative detection of informative space and the empirical pattern. The performance of the proposed method with various array datasets is illustrated.

MOTIVATION: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.

MOTIVATION: Arrays allow measurements of the expression levels of thousands of mRNAs to be made simultaneously. The resulting data sets are information rich but require extensive mining to enhance their usefulness. Information theoretic methods are capable of assessing similarities and dissimilarities between data distributions and may be suited to the analysis of gene expression experiments. The purpose of this study was to investigate information theoretic data mining approaches to discover temporal patterns of gene expression from array-derived gene expression data. RESULTS: The Kullback-Leibler divergence, an information-theoretic distance that measures the relative dissimilarity between two data distribution profiles, was used in conjunction with an unsupervised self-organizing map algorithm. Two published, array-derived gene expression data sets were analyzed. The patterns obtained with the KL clustering method were found to be superior to those obtained with the hierarchical clustering algorithm using the Pearson correlation distance measure. The biological significance of the results was also examined. AVAILABILITY: Software code is available by request from the authors. All programs were written in ANSI C and Matlab (Mathworks Inc., Natick, MA).

Visualization enables us to find structures, features, patterns and relationship in a dataset by presenting the data in various graphical forms with possible interactions. Recent development of DNA microarray technology can be used to measure the expression levels of thousands of genes simultaneously. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analysis and visualize these fast growing data. In this paper, we present VizCluster, an interactive visualization approach to cluster analysis, and its application on microarray data. VizCluster combines the merits of both high dimensional scatter-plot and parallel coordinates. Integrated with useful features, it can give a simple, fast, intuitive and yet powerful view of the data set. VizCluster supports three major analyzing modes: cluster/class discovery, class prediction, and class assessment. Its primary applications are the classification of samples on microarray datasets. The experiments are based on gene expression data from a study of multiple sclerosis and leukemia patients.

DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Most research is focusing on interpretation of the meaning of the data. However, the majority of methods are supervised, with less attention having been paid to unsupervised approaches which are important when domain knowledge is incomplete or hard to obtain. In this paper we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach to the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.