Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. Our approach to abstract classification shows that a simple linear model, using relatively few features, is capable of generalizing and uncovering the conceptual nature of protein-protein interaction from the bibliome. Since the novel approach is based on a very lightweight linear model, it can be easily ported and applied to similar problems. In full text problems, the expansion of word features with word-proximity networks is shown to be useful, though the need for some improvements is discussed

Protein annotation as term categorization in the gene ontology using word proximity networks

We addressed BioCreAtIvE Task 2, the problem of annotation of a protein with a node in the Gene Ontology (GO). We approached the task as a problem of categorizing terms derived from the document neighborhood of the given protein in the given document into nodes in the GO based on the lexical overlaps with terms on GO nodes and terms identified as related to those nodes. The system incorporates NLP components such as a morphological normalizer, a named entity recognizer, a statistical term frequency analyzer, and an unsupervised method for expanding words associated with GO ids based on a probability measure that captures word proximity (Rocha, 2002). The categorization methodology uses our novel Gene Ontology Categorizer (GOC) methodology (Joslyn et al. 2004) to select GO nodes as cluster heads for the terms in the input set based on the structure of the GO. Pre-processing Swiss-Prot and TrEMBL IDs were provided as input identifiers for the protein, so we needed to establish a set of names by which that protein could be referenced in the text. We made use of both the gene name and protein names that are in Swiss-Prot itself, when available, and a collection of synonyms constructed by Procter & Gamble Company. The fallback case was to us

The textual characteristics of traditional and Open Access scientific journals are similar

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an increased amount of natural language processing (NLP) work on full text biomedical journal publications. Much of this work is done with Open Access journal articles. Such work assumes that Open Access articles are representative of biomedical publications in general and that methods developed for analysis of Open Access full text publications will generalize to the biomedical literature as a whole. If this assumption is wrong, the cost to the community will be large, including not just wasted resources, but also flawed science. This paper examines that assumption.</p> <p>Results</p> <p>We collected two sets of documents, one consisting only of Open Access publications and the other consisting only of traditional journal publications. We examined them for differences in surface linguistic structures that have obvious consequences for the ease or difficulty of natural language processing and for differences in semantic content as reflected in lexical items. Regarding surface linguistic structures, we examined the incidence of conjunctions, negation, passives, and pronominal anaphora, and found that the two collections did not differ. We also examined the distribution of sentence lengths and found that both collections were characterized by the same mode. Regarding lexical items, we found that the Kullback-Leibler divergence between the two collections was low, and was lower than the divergence between either collection and a reference corpus. Where small differences did exist, log likelihood analysis showed that they were primarily in the area of formatting and in specific named entities.</p> <p>Conclusion</p> <p>We did not find structural or semantic differences between the Open Access and traditional journal collections.</p

Text Mining Improves Prediction of Protein Functional Sites

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

Non peer reviewe

Recognition of social health: A conceptual framework in the context of dementia research

Objective: The recognition of dementia as a multifactorial disorder encourages the exploration of new pathways to understand its origins. Social health might play a role in cognitive decline and dementia, but conceptual clarity is lacking and this hinders investigation of associations and mechanisms. The objective is to develop a conceptual framework for social health to advance conceptual clarity in future studies. Process: We use the following steps: underpinning for concept advancement, concept advancement by the development of a conceptual model, and exploration of its potential feasibility. An iterative consensus-based process was used within the international multidisciplinary SHARED project. Conceptual framework: Underpinning of the concept drew from a synthesis of theoretical, conceptual and epidemiological work, and resulted in a definition of social health as wellbeing that relies on capacities both of the individual and the social environment. Consequently, domains in the conceptual framework are on both the individual (e.g., social participation) and the social environmental levels (e.g., social network). We hypothesize that social health acts as a driver for use of cognitive reserve which can then slow cognitive impairment or maintain cognitive functioning. The feasibility of the conceptual framework is demonstrated in its practical use in identifying and structuring of social health markers within the SHARED project. Discussion: The conceptual framework provides guidance for future research and facilitates identification of modifiable risk and protective factors, which may in turn shape new avenues for preventive interventions. We highlight the paradigm of social health in dementia as a priority for dementia research

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe

The structural and content aspects of abstracts versus bodies of full text journal articles are different

<p>Abstract</p> <p>Background</p> <p>An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research.</p> <p>Results</p> <p>We examined the structural and linguistic aspects of abstracts and bodies of full text articles, the performance of text mining tools on both, and the distribution of a variety of semantic classes of named entities between them. We found marked structural differences, with longer sentences in the article bodies and much heavier use of parenthesized material in the bodies than in the abstracts. We found content differences with respect to linguistic features. Three out of four of the linguistic features that we examined were statistically significantly differently distributed between the two genres. We also found content differences with respect to the distribution of semantic features. There were significantly different densities per thousand words for three out of four semantic classes, and clear differences in the extent to which they appeared in the two genres. With respect to the performance of text mining tools, we found that a mutation finder performed equally well in both genres, but that a wide variety of gene mention systems performed much worse on article bodies than they did on abstracts. POS tagging was also more accurate in abstracts than in article bodies.</p> <p>Conclusions</p> <p>Aspects of structure and content differ markedly between article abstracts and article bodies. A number of these differences may pose problems as the text mining field moves more into the area of processing full-text articles. However, these differences also present a number of opportunities for the extraction of data types, particularly that found in parenthesized text, that is present in article bodies but not in article abstracts.</p