Institution
Turku Centre for Computer Science
Facility•Turku, Finland•
About: Turku Centre for Computer Science is a facility organization based out in Turku, Finland. It is known for research contribution in the topics: Decidability & Word (group theory). The organization has 382 authors who have published 1027 publications receiving 19560 citations.
Papers published on a yearly basis
Papers
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12 Oct 1999TL;DR: In this article, the authors present vUML, a tool that automatically verifies UML models where the behaviour of the objects is described using UML Statecharts diagrams, but the user does not have to know how to use SPIN or the PROMELA language.
Abstract: The Unified Modelling Language (UML) is a standardised notation for describing object oriented software designs. We present vUML, a tool that automatically verifies UML models where the behaviour of the objects is described using UML Statecharts diagrams. The tool uses the SPIN model checker to perform the verification, but the user does not have to know how to use SPIN or the PROMELA language. If an error is found during the verification, the tool creates a UML sequence diagram showing how to reproduce the error in the model.
250 citations
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TL;DR: This paper analytically derive the minimal variability weighting vector for any level of orness, using the Kuhn-Tucker second-order sufficiency conditions for optimality.
248 citations
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TL;DR: This first comparative evaluation of the diverse PPI corpora is presented, performing quantitative evaluation using two separate information extraction methods as well as detailed statistical and qualitative analyses of their properties.
Abstract: Background
Growing interest in the application of natural language processing methods to biomedical text has led to an increasing number of corpora and methods targeting protein-protein interaction (PPI) extraction. However, there is no general consensus regarding PPI annotation and consequently resources are largely incompatible and methods are difficult to evaluate.
240 citations
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05 Jun 2009TL;DR: A system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction, which defines a wide array of features and makes extensive use of dependency parse graphs.
Abstract: We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.
231 citations
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TL;DR: The third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed, concluded that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not.
Abstract: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.
227 citations
Authors
Showing all 383 results
Name | H-index | Papers | Citations |
---|---|---|---|
José A. Teixeira | 101 | 1414 | 47329 |
Cunsheng Ding | 61 | 254 | 11116 |
Jun'ichi Tsujii | 59 | 389 | 15985 |
Arto Salomaa | 56 | 374 | 17706 |
Tero Aittokallio | 52 | 271 | 8689 |
Risto Lahdelma | 48 | 149 | 6637 |
Hannu Tenhunen | 45 | 819 | 11661 |
Mats Gyllenberg | 44 | 204 | 8029 |
Sampo Pyysalo | 42 | 153 | 8839 |
Olli Polo | 42 | 140 | 5303 |
Pasi Liljeberg | 40 | 306 | 6959 |
Tapio Salakoski | 38 | 231 | 7271 |
Filip Ginter | 37 | 156 | 7294 |
Robert Fullér | 37 | 152 | 5848 |
Juha Plosila | 35 | 342 | 4917 |