Behavioral observation plays an essential role in the diagnosis of Autism Spectrum Disorder (ASD) by analyzing children's atypical patterns in social activities (e.g., impaired social interaction, restricted interests, and repetitive behavior). To date, this process still heavily relies on the questionnaire survey, clinical observation, or retrospective video analysis, leading to high demand for professionals with massive labor costs. This article proposes a standardized platform for stimulating, gathering, analyzing, modeling, and interpreting human behavioral data in the application of computer-aided ASD diagnosis. By a structured assessment process, the proposed system can automatically evaluate children's multiple social interaction skills using the captured audio-visual data and provide the final diagnostic suggestions. We collect a multimodal behavioral database of 95 participants (71 children with ASD and 24 age-matched typical controls) in a real clinic environment, the Third Affiliated Hospital of Sun Yat-sen University, China. On the clinical database, our proposed computer-aided ASD diagnosis system obtains an accuracy of 88.42% for identifying ASD children with an average age of 24 months, representing a performance comparable to top-level human experts. As a unified and replicable solution, it has good potential to be promoted to less developed areas with limited high-quality medical resources.

Ubiquitin (Ub) can generate versatile molecular signals and lead to different celluar fates. The functional poly-valence of Ub is believed to be resulted from its ability to form distinct polymerized chains with eight linkage types. To provide a full picture of ubiquitin code, we explore the binding landscape of two free Ub monomers and also the functional landscapes of of all eight linkage types by theoretical modeling. Remarkably, we found that most of the compact structures of covalently connected dimeric Ub chains (diUbs) pre-exist on the binding landscape. These compact functional states were subsequently validated by corresponding linkage models. This leads to the proposal that the folding architecture of Ub monomer has encoded all functional states into its binding landscape, which is further selected by different topologies of polymeric Ub chains. Moreover, our results revealed that covalent linkage leads to symmetry breaking of interfacial interactions. We further propose that topological constraint not only limits the conformational space for effective switching between functional states, but also selects the local interactions for realizing the corresponding biological function. Therefore, the topological constraint provides a way for breaking the binding symmetry and reaching the functional specificity. The simulation results also provide several predictions that qualitatively and quantitatively consistent with experiments. Importantly, the K48 linkage model successfully predicted intermediate states. The resulting multi-state energy landscape was further employed to reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb. Our results further suggest that hydrophobic interactions are dominant in the functional landscapes of K6-, K11-, K33- and K48 diUbs, while electrostatic interactions play a more important role in the functional landscapes of K27, K29, K63 and linear linkages.

An increasing number of biological machines have been revealed to have more than two macroscopic states. Quantifying the underlying multiple-basin functional landscape is essential for understanding their functions. However, the present models seem to be insufficient to describe such multiple-state systems. To meet this challenge, we have developed a coarse grained triple-basin structure-based model with implicit ligand. Based on our model, the constructed functional landscape is sufficiently sampled by the brute-force molecular dynamics simulation. We explored maltose-binding protein (MBP) which undergoes large-scale domain motion between open, apo-closed (partially closed) and holo-closed (fully closed) states responding to ligand binding. We revealed an underlying mechanism whereby major induced fit and minor population shift pathways co-exist by quantitative flux analysis. We found that the hinge regions play an important role in the functional dynamics as well as that increases in its flexibility promote population shifts. This finding provides a theoretical explanation of the mechanistic discrepancies in PBP protein family. We also found a functional "backtracking" behavior that favors conformational change. We further explored the underlying folding landscape in response to ligand binding. Consistent with earlier experimental findings, the presence of ligand increases the cooperativity and stability of MBP. This work provides the first study to explore the folding dynamics and functional dynamics under the same theoretical framework using our triple-basin functional model.

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.

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.

DNA microarray technology is now widely used in basic biomedical research for mRNA expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. Automatically detecting phenotype structures from gene expression profiles can provide deep insight into the nature of many diseases as well as lead in the development of new drugs. While most of the previous studies focus on only mining empirical phenotype structure which the experiment controls, it is also interesting to detect possible hidden phenotype structures underlying gene expression profiles.Since the number of samples is usually limited, such data sets are very sparse in high-dimensional gene space. Furthermore, most of the genes of interest are buried in large amount of noise. Unsupervised phenotype structure discovery of such sparse high-dimensional data sets present interesting but challenging problems. In this paper, we propose the model of simultaneously mining both empirical and hidden phenotype structures from gene expression data. We demonstrate the effectiveness and efficiency of the proposed method on various real-world data sets.

Mining microarray gene expression data is an important research topic in bioinformatics with broad applications. While most of the previous studies focus on clustering either genes or samples, it is interesting to ask whether we can partition the complete set of samples into exclusive groups (called phenotypes) and find a set of informative genes that can manifest the phenotype structure. In this paper, we propose a new problem of simultaneously mining phenotypes and informative genes from gene expression data. Some statistics-based metrics are proposed to measure the quality of the mining results. Two interesting algorithms are developed: the heuristic search and the mutual reinforcing adjustment method. We present an extensive performance study on both real-world data sets and synthetic data sets. The mining results from the two proposed methods are clearly better than those from the previous methods. They are ready for the real-world applications. Between the two methods, the mutual reinforcing adjustment method is in general more scalable, more effective and with better quality of the mining results.

High-dimensional data representation in which each data item (termed target object) is described by many features, is a necessary component of many applications. For example, in DNA microarrays, each sample (target object) is represented by thousands of genes as features. Pattern discovery of target objects presents interesting but also very challenging problems. The data sets are typically not task-specific, many features are irrelevant or redundant and should be pruned out or filtered for the purpose of classifying target objects to find empirical pattern. Uncertainty about which features are relevant makes it difficult to construct an informative feature space. This paper proposes an iterative strategy for pattern discovery in high-dimensional data sets. In this approach, the iterative process consists of two interactive components: discovering patterns within target objects and pruning irrelevant features. The performance of the proposed method with various real data sets is also illustrated.

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.

Keyblock, which is a new framework we proposed for content-based image retrieval, is a generalization of the textbased information retrieval technology in the image domain. In this framework, keyblocks, which are analogous to keywords in text document retrieval, can be constructed by exploiting the method of Vector Quantization (VQ). Then an image can be represented as a list of keyblocks similar to a text document which can be considered as a list of keywords. Based on this image representation, various feature models can be constructed for supporting image retrieval. In this paper, we present a new feature representation model which use the keyblock-keyblock correlation matrix, termed keyblock-thesaurus, to facilitate the image retrieval. The feature vectors of this new model incorporate the effect of correlation between keyblocks, thus being more effective in representing image content.