Biomedical subjects

Jorma Boberg

Publications and source records attributed to Jorma Boberg.

3 recordsLinked to original sources

PubMed2005

Evaluation of two dependency parsers on biomedical corpus targeted at protein-protein interactions.

We present an evaluation of Link Grammar and Connexor Machinese Syntax, two major broad-coverage dependency parsers, on a custom hand-annotated corpus consisting of sentences regarding protein-protein interactions. In the evaluation, we apply the notion of an interaction subgraph, which is the subgraph of a dependency graph expressing a protein-protein interaction. We measure the performance of the parsers for recovery of individual dependencies, fully correct parses, and interaction subgraphs. For Link Grammar, an open system that can be inspected in detail, we further perform a comprehensive failure analysis, report specific causes of error, and suggest potential modifications to the grammar. We find that both parsers perform worse on biomedical English than previously reported on general English. While Connexor Machinese Syntax significantly outperforms Link Grammar, the failure analysis suggests specific ways in which the latter could be modified for better performance in the domain.

Abstracting and Indexing↗

PubMed2005

Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation.

BACKGROUND: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task. RESULTS: We incorporated into the conventional SVM a weighting scheme based on distances of context words from the word to be disambiguated. This weighting scheme increased the performance of SVMs by five percentage points giving performance better than 85% as measured by the area under ROC curve and outperformed the Weighted Additive Classifier, which also incorporates the weighting, and the Naive Bayes classifier. CONCLUSION: We show that the performance of SVMs can be improved by the proposed weighting scheme. Furthermore, our results suggest that in this study the increase of the classification performance due to the weighting is greater than that obtained by selecting the underlying classifier or the kernel part of the SVM.

Algorithms↗

PubMed2002

Mobile access to biological databases on the Internet.

We have developed a new way of accessing biological databases and bioinformatics applications on the Internet. This new service, bioinformatics wireless application protocol (BioWAP) service, which is accessible by mobile devices makes it possible to access bioinformatics services, where normal PC or personal digital assistant (PDA) connections are not feasible. The BioWAP service includes major biological databases and applications demonstrating a simple method of implementing WAP interfaces to uncompliant applications, i.e. the applications that are not WAP or Internet based. The BioWAP service can be browsed with any WAP terminal.

Computational Biology↗