An improved hidden vector state model approach and its adaptation in extracting protein interaction information from biomedical literature

Large quantity of knowledge, which is important for biological researchers to unveil the mechanism of life, often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and full parsing, have been proposed especially for biomedical applications. In this paper, we present an information extraction system employing a semantic parser using the Hidden Vector State (HVS) model for protein-protein interactions. We found that it performed better than other established statistical methods and achieved 58.3% and 76.8% in recall and precision respectively. Moreover, the pure data-driven HVS model can be easily adapted to other domains, which is rarely mentioned and possessed by other approaches. Experimental results prove that the model trained on one domain can still generate satisfactory results when shifting to another domain with a small amount of adaptation training data

Extracting protein-protein interaction based on discriminative training of the Hidden Vctor State model

The knowledge about gene clusters and protein interactions is important for biological researchers to unveil the mechanism of life. However, large quantity of the knowledge often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and deep parsing, have been proposed especially for extracting protein-protein interactions (Zhou and He, 2008). A semantic parser based on the Hidden Vector State (HVS) model for extracting protein-protein interactions is presented in (Zhou et al., 2008). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. Maximum Likelihood estimation (MLE) is used to derive the parameters of the HVS model. In this paper, we propose a discriminative approach based on parse error measure to train the HVS model. To adjust the HVS model to achieve minimum parse error rate, the generalized probabilistic descent (GPD) algorithm (Kuo et al., 2002) is used. Experiments have been conducted on the GENIA corpus. The results demonstrate modest improvements when the discriminatively trained HVS model outperforms its MLE trained counterpart by 2.5% in F-measure on the GENIA corpus

Extracting human protein information from MEDLINE using a full-sentence parser

Today, a fair number of systems are available for the task of processing biological data. The development of effective systems is of great importance since they can support both the research and the everyday work of biologists. It is well known that biological databases are large both in size and number, hence data processing technologies are required for the fast and effective management of the contents stored in databases like MEDLINE. A possible solution for content management is the application of natural language processing methods to help make this task easier. With our approach we would like to learn more about the interactions of human genes using full-sentence parsing. Given a sentence, the syntactic parser assigns to it a syntactic structure, which consists of a set of labelled links connecting pairs of words. The parser also produces a constituent representation of a sentence (showing noun phrases, verb phrases, and so on). Here we show experimentally that using the syntactic information of each abstract, the biological interactions of genes can be predicted. Hence, it is worth developing the kind of information extraction (IE) system that can retrieve information about gene interactions just by using syntactic information contained in these text. Our IE system can handle certain types of gene interactions with the help of machine learning (ML) methodologies (Hidden Markov Models, Artificial Neural Networks, Decision Trees, Support Vector Machines). The experiments and practical usage show clearly that our system can provide a useful intuitive guide for biological researchers in their investigations and in the design of their experiments

Effective reranking for extracting protein-protein interactions from biomedical literature

A semantic parser based on the hidden vector state (HVS) model has been proposed for extracting protein-protein interactions. The HVS model is an extension of the basic discrete hidden Markov model, in which context is encoded as a stack-oriented state vector and state transitions are factored into a stack shift operation followed by the push of a new preterminal category label. In this paper, we investigate three different models, log-linear regression (LLR), neural networks (NNs) and support vector machines (SVMs), to rerank parses generated by the HVS model for protein-protein interactions extraction. Features used for reranking are manually defined which include the parse information, the structure information, and the complexity information. The experimental results show that reranking can indeed improve the performance of protein-protein interactions extraction, and reranking based on SVM gives more stable performance than LLR and NN

Extracting protein-protein interactions from text using rich feature vectors and feature selection

Because of the intrinsic complexity of natural language, automatically extracting accurate information from text remains a challenge. We have applied rich featurevectors derived from dependency graphs to predict protein-protein interactions using machine learning techniques. We present the first extensive analysis of applyingfeature selection in this domain, and show that it can produce more cost-effective models. For the first time, our technique was also evaluated on several large-scalecross-dataset experiments, which offers a more realistic view on model performance. During benchmarking, we encountered several fundamental problems hindering comparability with other methods. We present a set of practical guidelines to set up ameaningful evaluation. Finally, we have analysed the feature sets from our experiments before and after feature selection, and evaluated the contribution of both lexical and syntacticinformation to our method. The gained insight will be useful to develop better performing methods in this domain

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences

Background: Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. Methods: We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. Results: By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Conclusions: Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.X116sciescopu

Extraction and Classification of Drug-Drug Interaction from Biomedical Text Using a Two-Stage Classifier

One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, mechanism, and int. To find the set of best features for classification, we explored many features, including stemmed words, bigrams, part of speech tags, verb lists, parse tree information, mutual information, and similarity measures, among others. As the system faced two different classification tasks, binary and multi-class, we also explored various classifiers in each stage. Our results show that the best performing classifier in both stages was Support Vector Machines, and the best performing features were 1000 top informative words and part of speech tags between two main drugs. We obtained an F-Measure of 0.64, showing a 12% improvement over our submitted system to the DDIExtraction 2013 competition