Abstract Background mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. Results In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. Conclusions We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

The problem of active diagnosis arises in several applications such as disease diagnosis and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, potentially noisy responses to binary valued queries. Previous work in this area chooses queries sequentially based on Information gain, and the object states are inferred by maximum a posteriori (MAP) estimation. In this work, rather than MAP estimation, we aim to rank objects according to their posterior fault probability. We propose a greedy algorithm to choose queries sequentially by maximizing the area under the ROC curve associated with the ranked list. The proposed algorithm overcomes limitations of existing work. When multiple faults may be present, the proposed algorithm does not rely on belief propagation, making it feasible for large scale networks with little loss in performance. When a single fault is present, the proposed algorithm can be implemented without knowledge of the underlying query noise distribution, making it robust to any misspecification of these noise parameters. We demonstrate the performance of the proposed algorithm through experiments on computer networks, a toxic chemical database, and synthetic datasets.

OBJECTIVE: Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case-control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships. METHODS: To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes. RESULTS: The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease. CONCLUSION: The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.

In applications such as active learning and disease/fault diagnosis, one often encounters the problem of identifying an unknown object through a minimal number of queries. This problem has been referred to as query learning or object/entity identification. We consider three extensions of this fundamental problem that are motivated by practical considerations in real-world,time-critical identification tasks such as emergency response. First, we consider the problem where the objects are partitioned into groups, and the goal is to identify only the group to which the object belongs. Second, we address the situation where the queries are partitioned into groups, and an algorithm may suggest a group of queries to a human user, who then selects the actual query. Third, we consider the problem of object identification in the presence of persistent query noise, and relate it to group identification. To address these problems we show that a standard algorithm for object identification, known as generalized binary search, may be viewed as a generalization of Shannon-Fano coding. We then extend this result to the group-based settings, leading to new algorithms, whose performance is demonstrated through a logarithmic approximation bound, and through experiments on simulated data and a database used for toxic chemical identification.

Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.

In this paper, we report a qualitative study of translational researchers in health sciences in a large public Research I university in the United States. This paper first identifies challenges faced by translational researchers in mobilizing necessary resources such as data and personnel to effectively conduct research. Then we use the theoretical constructs underlying those challenges to identify social and technical infrastructural needs that facilitate translational research. The major challenges emerged from the data include difficulty in identifying compatible collaborators, barriers within the existing research culture, insufficient organizational and technical support for complex data management tasks, and the lack of project management skills and tools. Using institutional infrastructures to support translational research is suggested.

Generalized Binary Search (GBS) is a well known greedy algorithm for identifying an unknown object while minimizing the number of yes" or "no" questions posed about that object, and arises in problems such as active learning and active diagnosis. Here, we provide a coding-theoretic interpretation for GBS and show that GBS can be viewed as a top-down algorithm that greedily minimizes the expected number of queries required to identify an object. This interpretation is then used to extend GBS in two ways. First, we consider the case where the objects are partitioned into groups, and the objective is to identify only the group to which the object belongs. Then, we consider the case where the cost of identifying an object grows exponentially in the number of queries. In each case, we present an exact formula for the objective function involving Shannon or Renyi entropy, and develop a greedy algorithm for minimizing it."

that suggests the role each of the above-mentioned page clusters play in providing comprehensive information. These results provide implications for the design of Web sites, search tools, and training to help users find comprehensive information about a topic and for a hypothesis describing the underlying mechanisms causing the scatter. We conclude by briefly discussing how the analysis of information scatter, at the granularity of facts, complements existing theories of information-seeking behavior.

BACKGROUND: Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases. RESULTS: The analysis revealed three main regularities: (1) Many genes associated with a single disease, and fewer genes associated with many diseases. (2) Unexpected combinations of renal diseases that share relatively large numbers of genes. (3) Uniform concordance in the regulation of all genes in the network. CONCLUSION: The overall results suggest the need to define a molecular-based classification of renal diseases, in addition to hypotheses for the unexpected patterns of shared genes and the uniformity in gene concordance. Furthermore, the results demonstrate the utility of network analyses to rapidly understand complex relationships between diseases and regulated genes.

Numerous studies have shown that many users do not acquire the knowledge necessary for the effective and efficient use of computer applications such as spreadsheets and Web-authoring tools. While many cognitive, cultural, and social reasons have been offered to explain this phenomenon, there have been few systematic attempts to address it. This article describes how we identified a framework to organize effective and efficient strategies to use computer applications and used an approach called strategy-based instruction to teach those strategies over five years to almost 400 students. Controlled experiments demonstrated that the instructional approach (1) enables students to learn strategies without harming command knowledge, (2) benefits students from technical and nontechnical majors, and (3) is robust across different instructional contexts and new applications. Real-world classroom experience of teaching strategy-based instruction over several instantiations has enabled the approach to be disseminated to other universities. The lessons learned throughout the process of design, implementation, evaluation, and dissemination should allow teaching a large number of users in many organizations to rapidly acquire the strategic knowledge to make more effective and efficient use of computer applications.

Abstract Recent studies suggest that the wide variability in type, detail, and reliability of online information motivate expert searchers to develop procedural search knowledge. In contrast to prior research that has focused on finding relevant sources, procedural search knowledge focuses on how to order multiple relevant sources with the goal of retrieving comprehensive information. Because such procedural search knowledge is neither spontaneously inferred from the results of search engines, nor from the categories provided by domain‐specific portals, the lack of such knowledge leads most novice searchers to retrieve incomplete information. In domains like healthcare, such incomplete information can lead to dangerous consequences. To address the above problem, a new kind of domain portal called a Strategy Hub was developed and tested. Strategy Hubs provide critical search procedures and associated high‐quality links to enable users to find comprehensive and accurate information. We begin by describing how we collaborated with physicians to systematically identify generalizable search procedures to find comprehensive information about a disease, and how these search procedures were made available through the Strategy Hub. A controlled experiment suggests that this approach can improve the ability of novice searchers in finding comprehensive and accurate information, when compared to general‐purpose search engines and domain‐specific portals. We conclude with insights on how to refine and automate the Strategy Hub design, with the ultimate goal of helping users find more comprehensive information when searching in unfamiliar domains.

Abstract The rapid development of Web sites providing extensive coverage of a topic, coupled with the development of powerful search engines (designed to help users find such Web sites), suggests that users can easily find comprehensive information about a topic. In domains such as consumer healthcare, finding comprehensive information about a topic is critical as it can improve a patient's judgment in making healthcare decisions, and can encourage higher compliance with treatment. However, recent studies show that despite using powerful search engines, many healthcare information seekers have difficulty finding comprehensive information even for narrow healthcare topics because the relevant information is scattered across many Web sites. To date, no studies have analyzed how facts related to a search topic are distributed across relevant Web pages and Web sites. In this study, the distribution of facts related to five common healthcare topics across high‐quality sites is analyzed, and the reasons underlying those distributions are explored. The analysis revealed the existence of few pages that had many facts, many pages that had few facts, and no single page or site that provided all the facts. While such a distribution conforms to other information‐related phenomena, a deeper analysis revealed that the distributions were caused by a trade‐off between depth and breadth, leading to the existence of general, specialized, and sparse pages. Furthermore, the results helped to make explicit the knowledge needed by searchers to find comprehensive healthcare information, and suggested the motivation to explore distribution‐conscious approaches for the development of future search systems, search interfaces, Web page designs, and training.

Current search tools on the Web, such as general-purpose search engines (e.g. Google) and domain-specific portals (e.g. MEDLINEplus), do not provide search procedures that guide users to form appropriately ordered sub-goals. The lack of such procedural knowledge often leads users searching in unfamiliar domains to retrieve incomplete information. In critical domains such as in healthcare, such ineffective searches can have dangerous consequences. To address this situation, we developed a new type of domain portal called a Strategy Hub. Strategy Hubs provide the critical search procedures and associated high-quality links that enable users to find comprehensive and accurate information. This paper describes how we collaborated with skin cancer physicians to systematically identify generalizeable search procedures to find comprehensive information about melanoma, and how these search procedures were made available through the Strategy Hub for healthcare. A pilot study suggests that this approach can improve the efficacy, efficiency, and satisfaction of even expert searchers. We conclude with insights on how to refine the design of the Strategy Hub, and how it can be used to provide search procedures across domains.

Despite experience, many users do not make efficient use of complex computer applications. We argue that this is caused by a lack of strategic knowledge that is difficult to acquire just by knowing how to use commands. To address this problem, we present efficient and general strategies for using computer applications, and identify the components of strategic knowledge required to use them. We propose a framework for teaching strategic knowledge, and show how we implemented it in a course for freshman students. In a controlled study, we compared our approach to the traditional approach of just teaching commands. The results show that efficient and general strategies can in fact be taught to students of diverse backgrounds in a limited time without harming command knowledge. The experiment also pinpointed those strategies that can be automatically learned just from learning commands, and those that require more practice than we provided. These results are important to universities and companies that wish to foster more efficient use of complex computer applications.

Several studies show that despite experience, many users with basic command knowledge do not progress to an efficient use of complex computer applications. These studies suggest that knowledge of tasks and knowledge of tools are insufficient to lead users to become efficient. To address this problem, we argue that users also need to learn strategies in the intermediate layers of knowledge lying between tasks and tools. These strategies are (a) efficient because they exploit specific powers of computers, (b) difficult to acquire because they are suggested by neither tasks nor tools, and (c) general in nature having wide applicability. The above characteristics are first demonstrated in the context of aggregation strategies that exploit the iterative power of computers. A cognitive analysis of a real-world task reveals that even though such aggregation strategies can have large effects on task time, errors, and on the quality of the final product, they are not often used by even experienced users. We identify other strategies beyond aggregation that can be efficient and useful across computer applications and show how they were used to develop a new approach to training with promising results. We conclude by suggesting that a systematic analysis of strategies in the intermediate layers of knowledge can lead not only to more effective ways to design training but also to more principled approaches to design systems. These advances should lead users to make more efficient use of complex computer systems.

The inefficient use of complex computer systems has been widely reported. These studies show the persistence of inefficient methods despite many years of experience and formal training. To counteract this phenomenon, we present the design of a new course, called the Strategic Use of CAD. The course aims at teaching students efficient strategies to use a computer-aided drafting system through a two-pronged approach. Learning to See teaches students to recognize opportunities to use efficient strategies by studying the nature of the task, and Learning to Do teaches students to implement the strategies. Results from a pilot experiment show that this approach had a positive effect on the strategic behavior of students who did not exhibit knowledge of efficient strategies before the class, and had no effect on the strategic behavior of those who did. Strategic training can thus assist users in recognizing opportunities to use efficient strategies. We present the ramifications of these results on the design of training and future experiments.

Throughout history, inefficient methods to use devices have been replaced by more efficient ones.This shift typically occurs when users discover how to &legate work to the powers of a tool, and to circumvent its liiitations.Strategies of delegation and circumvention, therefore, appear to be the core of efficient use.To show how this approach can expiain the relationship between tools and strategies in complex computer systems, we describe five ways to perform a real-world drawing task with current as well as 5.rture tools.We then present five corresponding GOMS models that demonstrate the value of efficient strategies when compared to the observed behavior of a professional CAD user.We conclude by presenting a generalized framework to characterize efficient strategies and discuss its relevance to design and training.

Can good design guarantee the eflicient use of computer tools?Can experience guarantee it?We raise these questions to explore why empirical studies of real-world usage show even experienced users under-utilizing the capabilities of computer applications.By analyzing the use of everyday devices and computer applications, as well as reviewing empirical studies, we conclude that neither good design nor experience may be able to guarantee efficient usage.Efficient use requires task decomposition strategies that exploit capabilities offered by computer applications such as the ability to aggregute objects, and to manipulate the aggregates with powerful operators.To understand the effects that strategies can have on performance, we present results from a GOMS analysis of a CAD task.Furthermore, we identify some key aggregation strategies that appear to generalize across applications.Such strategies may provide a framework to enable users to move from a sufficient to a more ef)icient use of computer tools.

Despite huge investments by vendors and users, CAD productivity remains disappointing. Our analysis of real-world CAD usage shows that even after many years of experience, users tend to use suboptimal strategies to perform complex CAD tasks. Additionally, some of these strategies have a marked resemblance to manual drafting techniques. Although this phenomenon has been previously reported, this paper explores explanations for its causes and persistence. We argue that the strategic knowledge to use CAD effectively is neither defined nor explicitly taught. In the absence of a well-formed strategy, users often develop a synthetic mental model of CAD containing a mixture of manual and CAD methods. As these suboptimal strategies do not necessarily prevent users from producing clean, accurate drawings, the inefficiencies tend to remain unrecognized and users have little motivation to develop better strategies. To reverse this situation we recommend that the strategic knowledge to use CAD effectively should be made explicit and provided early in training. We use our analysis to begin the process of making this strategic knowledge explicit. We conclude by discussing the ramifications of this research in training as well as in the development of future computer aids for drawing and design.