University of Minnesota

About: University of Minnesota is a education organization based out in Minneapolis, Minnesota, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 117432 authors who have published 257986 publications receiving 11944239 citations. The organization is also known as: University of Minnesota, Twin Cities & University of Minnesota-Twin Cities.

Top 10 algorithms in data mining

Abstract: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

Developmental Processes of Cooperative Interorganizational Relationships

Abstract: This article examines the developmental process of cooperative interorganizational relationships (IORs) that entail transaction-specific investments in deals that cannot be fully specified or controlled by the parties in advance of their execution. A process framework is introduced that focuses on formal, legal, and informal social-psychological processes by which organizational parties jointly negotiate, commit to. and execute their relationship in ways that achieve efficient and equitable outcomes and internal solutions to conflicts when they arise. The framework is elaborated with a set of propositions that explain how and why cooperative IORs emerge, evolve, and dissolve. The propositions have academic implications for enriching interorganizational relationships, transaction cost economics, agency theories, and practical implications for managing the relationship journey.

The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans

Abstract: The C. elegans heterochronic gene pathway consists of a cascade of regulatory genes that are temporally controlled to specify the timing of developmental events1. Mutations in heterochronic genes cause temporal transformations in cell fates in which stage-specific events are omitted or reiterated2. Here we show that let-7 is a heterochronic switch gene. Loss of let-7 gene activity causes reiteration of larval cell fates during the adult stage, whereas increased let-7 gene dosage causes precocious expression of adult fates during larval stages. let-7 encodes a temporally regulated 21-nucleotide RNA that is complementary to elements in the 3′ untranslated regions of the heterochronic genes lin-14, lin-28, lin-41, lin-42 and daf-12, indicating that expression of these genes may be directly controlled by let-7. A reporter gene bearing the lin-41 3′ untranslated region is temporally regulated in a let-7-dependent manner. A second regulatory RNA, lin-4, negatively regulates lin-14 and lin-28 through RNA–RNA interactions with their 3′ untranslated regions3,4. We propose that the sequential stage-specific expression of the lin-4 and let-7 regulatory RNAs triggers transitions in the complement of heterochronic regulatory proteins to coordinate developmental timing.

Task-technology fit and individual performance

Abstract: A key concern in Information Systems (IS) research has been to better understand the linkage between information systems and individual performance. The research reported in this study has two primary objectives: (1) to propose a comprehensive theoretical model that incorporates valuable insights from two complementary streams of research, and (2) to empirically test the core of the model. At the heart of the new model is the assertion that for an information technology to have a positive impact on individual performance, the technology: (1) must be utilized and (2) must be a good fit with the tasks it supports. This new model is moderately supported by an analysis of data from over 600 individuals in two companies. This research highlights the importance of the fit between technologies and users' tasks in achieving individual performance impacts from information technology. It also suggests that task-technology fit when decomposed into its more detailed components, could be the basis for a strong diagnostic tool to evaluate whether information systems and services in a given organization are meeting user needs.