Citation analysis of documents retrieved from the Medline database (at the Web of Knowledge) has been possible only on a case-by-case basis. A technique is here developed for citation analysis in batch mode using both Medical Subject Headings (MeSH) at the Web of Knowledge and the Science Citation Index at the Web of Science. This freeware routine is applied to the case of "Brugada Syndrome," a specific disease and field of research (since 1992). The journals containing these publications, for example, are attributed to Web-of-Science Categories other than "Cardiac and Cardiovascular Systems"), perhaps because of the possibility of genetic testing for this syndrome in the clinic. With this routine, all the instruments available for citation analysis can now be used on the basis of MeSH terms. Other options for crossing between Medline, WoS, and Scopus are also reviewed.; Comment: Journal of the American Society for Information Science and Technology (2012, in press)

Normalization of citation scores using reference sets based on Web-of-Science Subject Categories (WCs) has become an established ("best") practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC "information science & library science" and the WCs attributed to journals in the field of "science and technology studies," we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of "indexer effects." Can the compliance with "best practices" be replaced with an ambition to develop "best possible practices"? New research questions can then be envisaged.; Comment: accepted for publication in the Journal of the Association for Information Science and Technology (JASIST); 22 August 2014

To paraphrase Stan Ulam, a Polish mathematician who became a leading figure in the Manhattan Project, in this dissertation I focus not only on how computer science can help biologists, but also on how biology can inspire computer scientists. On one hand, computer science provides powerful abstraction tools for metabolic networks. Cell metabolism is the set of chemical reactions taking place in a cell, with the aim of maintaining the living state of the cell. Due to the intrinsic complexity of metabolic networks, predicting the phenotypic traits resulting from a given genotype and metabolic structure is a challenging task. To this end, mathematical models of metabolic networks, called genome-scale metabolic models, contain all known metabolic reactions in an organism and can be analyzed with computational methods. In this dissertation, I propose a set of methods to investigate models of metabolic networks. These include multi-objective optimization, sensitivity, robustness and identifiability analysis, and are applied to a set of genome-scale models. Then, I augment the framework to predict metabolic adaptation to a changing environment. The adaptation of a microorganism to new environmental conditions involves shifts in its biochemical network and in the gene expression level. However...