Reetina periflebiit ja selle patogeneetilised seosed hulgiskleroosiga

Artiklis on antud ülevaade reetina periflebiidist (RP) hulgiskleroosi ehk multiipelskleroosi (MS) korral. Kuigi RP esinemist osal MSi-haigetel on täheldatud aastakümneid, on nende seisundite patogeneetiline seos kirjanduses vähe kajastamist leidnud. Siiani ei ole suuresti teada, millised põletikulised mehhanismid põhjustavad RPd. MS on heterogeense immuunmehhanismiga kesknärvisüsteemi demüeliniseeriv haigus, mille kolded arenevad tüüpiliselt veenide ümber. MSi korral esinev põletikuline protsess on vahendatud nii rakulise kui ka humoraalse immuunsuse poolt, mis erinevatel haigetel võivad olla erineva osakaaluga. Arvestades uuemaid teadmisi, võib RP osutuda kliiniliseks markeriks, mis kirjeldab MSi-patsientide ühe alarühma immuunpatoloogilist fenotüüpi ning võib tulevikus abistada ravivalikute tegemisel

An environment for relation mining over richly annotated corpora: the case of GENIA

BACKGROUND: The biomedical domain is witnessing a rapid growth of the amount of published scientific results, which makes it increasingly difficult to filter the core information. There is a real need for support tools that 'digest' the published results and extract the most important information. RESULTS: We describe and evaluate an environment supporting the extraction of domain-specific relations, such as protein-protein interactions, from a richly-annotated corpus. We use full, deep-linguistic parsing and manually created, versatile patterns, expressing a large set of syntactic alternations, plus semantic ontology information. CONCLUSION: The experiments show that our approach described is capable of delivering high-precision results, while maintaining sufficient levels of recall. The high level of abstraction of the rules used by the system, which are considerably more powerful and versatile than finite-state approaches, allows speedy interactive development and validation

UZurich in the BioNLP 2009 Shared Task

We describe a biological event detection method implemented for the BioNLP 2009 Shared Task 1. The method relies entirely on the chunk and syntactic dependency relations provided by a general NLP pipeline which was not adapted in any way for the purposes of the shared task. The method maps the syntactic relations to event structures while being guided by the probabilities of the syntactic features of events which were automatically learned from the training data. Our method achieved a recall of 26% and a precision of 44% in the official test run, under "strict equality" of events

Controlled Natural Language4th International Workshop, CNL 2014, Galway, Ireland, August 20-22, 2014. Proceedings /

X, 202 p. 40 illus.online resource

Relation mining over a corpus of scientific literature

The amount of new discoveries (as published in the scientific literature) in the area of Molecular Biology is currently growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and the extraction of the core information, for inclusion in one of the knowledge resources being maintained by the research community, becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting semantic relations (e.g. interactions between genes and proteins) from scientific literature in the domain of Molecular Biology. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus