2010 | OriginalPaper | Buchkapitel
Mining Protein Interactions from Text Using Convolution Kernels
verfasst von : Ramanathan Narayanan, Sanchit Misra, Simon Lin, Alok Choudhary
Erschienen in: New Frontiers in Applied Data Mining
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
As the sizes of biomedical literature databases increase, there is an urgent need to develop intelligent systems that automatically discover Protein-Protein interactions from text. Despite resource-intensive efforts to create manually curated interaction databases, the sheer volume of biological literature databases makes it impossible to achieve significant coverage. In this paper, we describe a scalable hierarchical Support Vector Machine(SVM) based framework to efficiently mine protein interactions with high precision. In addition, we describe a convolution tree-vector kernel based on syntactic similarity of natural language text to further enhance the mining process. By using the inherent syntactic similarity of interaction phrases as a kernel method, we are able to significantly improve the classification quality. Our hierarchical framework allows us to reduce the search space dramatically with each stage, while sustaining a high level of accuracy. We test our framework on a corpus of over 10000 manually annotated phrases gathered from various sources. The convolution kernel technique identifies sentences describing interactions with a precision of 95% and a recall of 92%, yielding significant improvements over previous machine learning techniques.