10,195 research outputs found
Do peers see more in a paper than its authors?
Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances
A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature
We participated, in the Article Classification and the Interaction Method
subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of
the BioCreative III Challenge. For the ACT, we pursued an extensive testing of
available Named Entity Recognition and dictionary tools, and used the most
promising ones to extend our Variable Trigonometric Threshold linear
classifier. For the IMT, we experimented with a primarily statistical approach,
as opposed to employing a deeper natural language processing strategy. Finally,
we also studied the benefits of integrating the method extraction approach that
we have used for the IMT into the ACT pipeline. For the ACT, our linear article
classifier leads to a ranking and classification performance significantly
higher than all the reported submissions. For the IMT, our results are
comparable to those of other systems, which took very different approaches. For
the ACT, we show that the use of named entity recognition tools leads to a
substantial improvement in the ranking and classification of articles relevant
to protein-protein interaction. Thus, we show that our substantially expanded
linear classifier is a very competitive classifier in this domain. Moreover,
this classifier produces interpretable surfaces that can be understood as
"rules" for human understanding of the classification. In terms of the IMT
task, in contrast to other participants, our approach focused on identifying
sentences that are likely to bear evidence for the application of a PPI
detection method, rather than on classifying a document as relevant to a
method. As BioCreative III did not perform an evaluation of the evidence
provided by the system, we have conducted a separate assessment; the evaluators
agree that our tool is indeed effective in detecting relevant evidence for PPI
detection methods.Comment: BMC Bioinformatics. In Pres
Classification of protein interaction sentences via gaussian processes
The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption
Chi-square-based scoring function for categorization of MEDLINE citations
Objectives: Text categorization has been used in biomedical informatics for
identifying documents containing relevant topics of interest. We developed a
simple method that uses a chi-square-based scoring function to determine the
likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our
procedure requires construction of a genetic and a nongenetic domain document
corpus. We used MeSH descriptors assigned to MEDLINE citations for this
categorization task. We compared frequencies of MeSH descriptors between two
corpora applying chi-square test. A MeSH descriptor was considered to be a
positive indicator if its relative observed frequency in the genetic domain
corpus was greater than its relative observed frequency in the nongenetic
domain corpus. The output of the proposed method is a list of scores for all
the citations, with the highest score given to those citations containing MeSH
descriptors typical for the genetic domain. Results: Validation was done on a
set of 734 manually annotated MEDLINE citations. It achieved predictive
accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method
by comparing it to three machine learning algorithms (support vector machines,
decision trees, na\"ive Bayes). Although the differences were not statistically
significantly different, results showed that our chi-square scoring performs as
good as compared machine learning algorithms. Conclusions: We suggest that the
chi-square scoring is an effective solution to help categorize MEDLINE
citations. The algorithm is implemented in the BITOLA literature-based
discovery support system as a preprocessor for gene symbol disambiguation
process.Comment: 34 pages, 2 figure
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Complex Network Analysis for Scientific Collaboration Prediction and Biological Hypothesis Generation
With the rapid development of digitalized literature, more and more knowledge has been discovered by computational approaches. This thesis addresses the problem of link prediction in co-authorship networks and protein--protein interaction networks derived from the literature. These networks (and most other types of networks) are growing over time and we assume that a machine can learn from past link creations by examining the network status at the time of their creation. Our goal is to create a computationally efficient approach to recommend new links for a node in a network (e.g., new collaborations in co-authorship networks and new interactions in protein--protein interaction networks).
We consider edges in a network that satisfies certain criteria as training instances for the machine learning algorithms. We analyze the neighborhood structure of each node and derive the topological features. Furthermore, each node has rich semantic information when linked to the literature and can be used to derive semantic features. Using both types of features, we train machine learning models to predict the probability of connection for the new node pairs.
We apply our idea of link prediction to two distinct networks: a co-authorship network and a protein--protein interaction network. We demonstrate that the novel features we derive from both the network topology and literature content help improve link prediction accuracy. We also analyze the factors involved in establishing a new link and recurrent connections
A Relevance Feedback-Based System For Quickly Narrowing Biomedical Literature Search Result
The online literature is an important source that helps people find the information. The quick increase of online literature makes the manual search process for the most relevant information a very time-consuming task and leads to sifting through many results to find the relevant ones. The existing search engines and online databases return a list of results that satisfy the user\u27s search criteria. The list is often too long for the user to go through every hit if he/she does not exactly know what he/she wants or/and does not have time to review them one by one. My focus is on how to find biomedical literature in a fastest way. In this dissertation, I developed a biomedical literature search system that uses relevance feedback mechanism, fuzzy logic, text mining techniques and Unified Medical Language System. The system extracts and decodes information from the online biomedical documents and uses the extracted information to first filter unwanted documents and then ranks the related ones based on the user preferences. I used text mining techniques to extract PDF document features and used these features to filter unwanted documents with the help of fuzzy logic. The system extracts meaning and semantic relations between texts and calculates the similarity between documents using these relations. Moreover, I developed a fuzzy literature ranking method that uses fuzzy logic, text mining techniques and Unified Medical Language System. The ranking process is utilized based on fuzzy logic and Unified Medical Language System knowledge resources. The fuzzy ranking method uses semantic type and meaning concepts to map the relations between texts in documents. The relevance feedback-based biomedical literature search system is evaluated using a real biomedical data that created using dobutamine (drug name). The data set contains 1,099 original documents. To obtain coherent and reliable evaluation results, two physicians are involved in the system evaluation. Using (30-day mortality) as specific query, the retrieved result precision improves by 87.7% in three rounds, which shows the effectiveness of using relevance feedback, fuzzy logic and UMLS in the search process. Moreover, the fuzzy-based ranking method is evaluated in term of ranking the biomedical search result. Experiments show that the fuzzy-based ranking method improves the average ranking order accuracy by 3.35% and 29.55% as compared with UMLS meaning and semantic type methods respectively
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