Computing Fuzzy Associations for the Analysis of Biological Literature

Abstract

The increase of information in biology makes it difficult for researchers in any field to keep current with the literature. The MEDLINE database of scientific abstracts can be quickly scanned using electronic mechanisms. Potentially interesting abstracts can be selected by matching words joined by Boolean operators. However this means of selecting documents is not optimal. Nonspecific queries have to be effected, resulting in large numbers of irrelevant abstracts that have to be manually scanned To facilitate this analysis, we have developed a system that compiles a summary of subjects and related documents on the results of a MEDLINE query. For this, we have applied a fuzzy binary relation formalism that deduces relations between words present in a set of abstracts preprocessed with a standard grammatical tagger. Those relations are used to derive ensembles of related words and their associated subsets of abstracts. The algorithm can be used publicly at http:// www.bork.embl-heidelberg.de/xplormed/.

Information extraction from full text scientific articles: Where are the keywords?

“…To simplify matters, and following our previous work [4], we focused on the extraction of relevant words (keywords) regarding objects, detected as nouns from natural text by a standard grammatical tagger (TreeTagger, Helmut Schmid, IMS, Stuttgart University, ). In order to derive keywords from the section of an article, we first compute the associations between the words in the section.…”

“…We only computed associations between the words identified from the tagging as nouns. Following [4], the association between two words ( w i , w j ) (for example, "cell" and "cycle") can be modeled as the degree of inclusion of one word into the other () which can defined as the fuzzy binary relation given by: , that is, the ratio of the number sentences where both words w i and w j co-occur to the number of sentences the word w i occurs. This is an asymmetric relation very appropriate to model hierarchical relations between words as they happen in natural text.…”

“…We identify a word as relevant for the text analyzed if it establishes many and strong relations to other words (following [4]). Therefore, in a given section, we define a score for a word w i that is equal to , normalized to the maximum value found for K of any word in that section.…”

Ranking the whole MEDLINE database according to a large training set using text indexing

“…Based on our previous experience in the classification of abstracts, we registered the presence of words in the abstract (and title), ignoring the cardinality of words within a single abstract as done in [11], such that a word appearing many times within one abstract would not carry additional weight over a word appearing only once [12]. Additionally, we restricted the analysis to words which commonly convey meaning, that is, nouns, verbs, and adjectives, and not adverbs or conjunctions which would be more appropriate for style studies than for information extraction purposes [13].…”

eFIP: A Tool for Mining Functional Impact of Phosphorylation from Literature

“…For example, given the protein BAD and its literature identified by eGRAB, eGIFT focuses on the abstracts that are mainly about BAD, and identify concepts, such as “apoptosis,” “cell death,” and “dephosphorylation” as highly relevant to this gene. Although different in the overall approach, scoring formula, redundancy detection, multiword concept retrieval, and evaluation technique, eGIFT can be compared with methods described by Andrade and Valencia (16), XplorMed (17, 18), Liu et al (19), and Shatkay and Wilbur (20). …”