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• W2171283231 abstract "Systems biology recognizes in particular the importance of interactions between biological components and the consequences of these interactions. Such interactions and their downstream effects are known as events. To computationally mine the literature for such events, text mining methods that can detect, extract and annotate them are required. This review summarizes the methods that are currently available, with a specific focus on protein–protein interactions and pathway or network reconstruction. The approaches described will be of considerable value in associating particular pathways and their components with higher-order physiological properties, including disease states. Systems biology recognizes in particular the importance of interactions between biological components and the consequences of these interactions. Such interactions and their downstream effects are known as events. To computationally mine the literature for such events, text mining methods that can detect, extract and annotate them are required. This review summarizes the methods that are currently available, with a specific focus on protein–protein interactions and pathway or network reconstruction. The approaches described will be of considerable value in associating particular pathways and their components with higher-order physiological properties, including disease states. the occurrence of terms together in text can serve as an indication of a relationship between them. For example, the co-occurrence of two protein names within a single sentence can suggest an interaction between the proteins. Mutual information (MI) can be further used to examine the strength of the suggested relation. MI compares the joint probability of two items occurring [p(x,y)] with the probability of independent occurrence [p(x)×p(y)]. The higher the MI value, the greater is the amount of shared information; in other words, the higher the MI value, the greater is the confidence in hypothesizing that the occurrence of one determines or predicts the occurrence of the other. natural language text frequently contains words that have more than one possible interpretation. Disambiguation tasks involve selection of the correct interpretation among ambiguous alternatives, typically drawing on information from the context of the ambiguous expression. component of text mining that takes natural language text from a document source, extracts essential facts about one or more predefined fact types, and represents each fact as a template with slots filled on the basis of what is found from the text. To this end, various techniques are deployed to recognize entities and relations, which are then used to construct fact templates. ‘data about data’ (i.e. structured information regarding another piece of information). In a search context, metadata typically refer to keywords that identify concepts that are important for indexing of documents. task of automatically identifying mentioned names that refer to types of entities, such as genes and proteins, in text. in text, a particular concept can be denoted by various surface realizations, which are called term variants. For example, TIF2, TIF-2, transcription intermediary factor-2 and transcriptional intermediate factor 2 all denote the same concept. Usually, one of these term variants is considered as the preferred term. Normalization refers to the automated process by which all term variants are grouped together into an equivalent class. conceptual models used to support consistent and unambiguous knowledge sharing and to provide a framework for knowledge integration. For example, a biomolecular ontology might define concepts such as organic compounds, proteins and DNA, and organize them to specify that the latter two are subtypes of the first. In addition to organizing concepts in ‘is a’ hierarchies, ontologies can specify other general relations such as ‘part of’ and ‘located in’, as well as domain-specific relations, such as ‘translated into’ and ‘transcribed into’. for an ordered pair, (a,b) differs from (b,a). An ordered entity pair representation can be used to model directed relations, such as phosphorylation, in which the roles of the entities are different, whereas unordered pairs are appropriate for simple symmetric relations, such as binding. also referred to as syntactic analysis, parsing is the process of determining the syntactic structure of sentences. There are various approaches to parsing. One major division is between constituency (or phrase structure) and dependency approaches. The former can involve the building of increasing levels of hierarchy from the basic constituents (nouns, verbs, adjectives) to more complex constituents (noun phrases, verb phrases, sentences) in syntactic representation; the latter establishes relations (or dependencies) between the organizing verb and its dependent arguments. Syntactic analysis can also be categorized into full (deep) and partial (shallow) parsing, depending on whether the entire sentence structure or only part is resolved, such as the major top-level phrases. Deep parsing provides relationships not explicitly stated among words in a sentence; this is why it is commonly used for event extraction. For example, in the sentence ‘p53 is shown to activate transcription’, deep parsing encodes this information as follows: ‘p53’ is the subject of the predicate ‘to activate’ and ‘transcription’ is an object. Deep parsing often uses predicate argument structures. a normalized form representing syntactic relations, as in the example ‘ENTITY1 INHIBITS ENTITY2’. Here, the formal symbol INHIBITS is the predicate, which contains the main meaning of the predicate argument structure, and the formal symbols ENTITY1 and ENTITY2 are its arguments, carrying information about the participants described by the predicate. conditional probability that the case is correctly classified {=true positives/[true positives+false negatives]}. conditional probability that non-cases are correctly classified {=true negatives/[true negatives+false positives]}. assignment of a type with specified meaning to identify the category of an item. The definitions of types, such as ‘protein’ and ‘regulation’, would typically be defined in an appropriate ontology. in natural language processing, tagging is used to refer to tasks like part-of-speech tagging in which tags or labels representing grammatical parts-of-speech are assigned to a sequence of words or word-like units, such as ‘monocyte:NN, noun’. Other tasks add labels as tagging; for example, NER can be performed by marking each word with an additional label (e.g. monocyte: cell-line)." @default.
• W2171283231 created "2016-06-24" @default.
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• W2171283231 creator A5066925770 @default.
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• W2171283231 date "2010-07-01" @default.
• W2171283231 modified "2023-10-16" @default.
• W2171283231 title "Event extraction for systems biology by text mining the literature" @default.
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• W2171283231 doi "https://doi.org/10.1016/j.tibtech.2010.04.005" @default.
• W2171283231 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20570001" @default.
• W2171283231 hasPublicationYear "2010" @default.
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