Mining Host-Pathogen Interactions

containing HPI information and another abstract that does not. Using a training set of pre-annotated abstracts, the system can then learn how to efficiently discriminate between these two abstract types. Moreover, the same characteristic features can be calculated for the individual sentences in the abstract. Thus, we can use the same supervised-learning approach to solve Tasks 1 and 2. Finally, to solve Task 3 one can use a simple dictionary-based search for each sentence classified as containing HPI information. Our feature-based approach consists of four basic stages (Fig. 1C). First, each abstract is preprocessed to find each protein/gene in the abstract and identify its organism name. Second, for each abstract a feature vector is generated. Third, our supervised learning system is trained by providing the feature vectors generated from the positive and negative sets. Finally, the trained system is used on an independent testing set of HPI and non-HPI abstracts to assess the approach.s to assess the approach. Text preprocessing. We first add the publication title to the abstract as its first sentence. The abstract is then further split into individual sentences by detecting the sentence terminationis then further split into individual sentences by detecting the sentence termination patterns. A basic pattern of a period (.), followed by a space and capitalized letter can be directly used to distinguish sentences in a standard text. However, there are known challenges when preprocessing a biomedical (or any scientific) publication. For instance, the above simple approach is not always applicable, since the periods are often used in the names of proteins, abbreviations such as “i.e.”, “e.g.”, “vs.”, and others. We first identify such cases using a predefined dictionary, replace periods in these words by spaces, and then apply the above basic pattern. The next steps of the preprocessing stage concerns with detecting the organism and protein/gene names using the entity tagging software NLProt (Mika and Rost 2004). Support vector machines in text categorization. The problem of detecting whether an abstract contains HPI information can be formulated as a problem of supervised text categorization, with the goal of classifying abstracts into one of the selected categories. In our case, two categories can be naturally defined: (i) abstracts containing HPI information and (ii) abstracts without HPI information. Formally, given a training set of n objects, each represented as a vector of N numerical features, xi = (x1, x2, ..., xN), and their classification into one of the two classes y{-1,1}, the goal is to train a feature-based classifier based on the training set. After the training stage is completed, the classifier can assign a class label from y for any new abstract x. In our approach, we use support vector machines (SVM) (Vapnik 1998), a supervised learning method, which is well established in bioinformatics and has been recently applied to identify abstracts containing host-bacteria interaction