ENHANCING BUSINESS-INTELLIGENCE TOOLS WITH VALUE-DRIVEN RECOMMENDATIONS

Business-intelligence (BI) tools are broadly adopted in organizations today, supporting activities such as data analysis, decision making, and performance measurement. This study investigates the integration of feedback and recommendation mechanisms (FRM) into BI tool, defining FRM as visual cues that are embedded into the tools and provide the end-user with usage guidelines. The study focuses on FRM that are based on assessment of previous usage. It introduces the concept of valuedriven usage metadata - a novel methodology for tracking and communicating the usage of data resources, linked to a quantitative assessment of the value gained. A laboratory experiment tested FRM-integration with 200 participants and confirmed our assumptions that FRM integration will improve the usability of BI tools and increase the benefits that can be gained from data resources. It also highlighted the potential benefits of collecting value-driven usage metadata and using it to generated usage recommendations

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

BACKGROUND: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. METHODOLOGY/PRINCIPAL FINDINGS: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. CONCLUSIONS: Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations

Toxin-Based Therapeutic Approaches

Protein toxins confer a defense against predation/grazing or a superior pathogenic competence upon the producing organism. Such toxins have been perfected through evolution in poisonous animals/plants and pathogenic bacteria. Over the past five decades, a lot of effort has been invested in studying their mechanism of action, the way they contribute to pathogenicity and in the development of antidotes that neutralize their action. In parallel, many research groups turned to explore the pharmaceutical potential of such toxins when they are used to efficiently impair essential cellular processes and/or damage the integrity of their target cells. The following review summarizes major advances in the field of toxin based therapeutics and offers a comprehensive description of the mode of action of each applied toxin

Clustering Algorithms Optimizer: A Framework for Large Datasets

Abstract. Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are routinely applied, many of them suffer from the following limitations: (i) relying on predetermined parameters tuning, such as a-priori knowledge regarding the number of clusters; (ii) involving nondeterministic procedures that yield inconsistent outcomes. Thus, a framework that addresses these shortcomings is desirable. We provide a datadriven framework that includes two interrelated steps. The first one is SVDbased dimension reduction and the second is an automated tuning of the algorithm’s parameter(s). The dimension reduction step is efficiently adjusted for very large datasets. The optimal parameter setting is identified according to the internal evaluation criterion known as Bayesian Information Criterion (BIC). This framework can incorporate most clustering algorithms and improve their performance. In this study we illustrate the effectiveness of this platform by incorporating the standard K-Means and the Quantum Clustering algorithms. The implementations are applied to several gene-expression benchmarks with significant success