PPLook: an automated data mining tool for protein-protein interaction

<p>Abstract</p> <p>Background</p> <p>Extracting and visualizing of protein-protein interaction (PPI) from text literatures are a meaningful topic in protein science. It assists the identification of interactions among proteins. There is a lack of tools to extract PPI, visualize and classify the results.</p> <p>Results</p> <p>We developed a PPI search system, termed PPLook, which automatically extracts and visualizes protein-protein interaction (PPI) from text. Given a query protein name, PPLook can search a dataset for other proteins interacting with it by using a keywords dictionary pattern-matching algorithm, and display the topological parameters, such as the number of nodes, edges, and connected components. The visualization component of PPLook enables us to view the interaction relationship among the proteins in a three-dimensional space based on the OpenGL graphics interface technology. PPLook can also provide the functions of selecting protein semantic class, counting the number of semantic class proteins which interact with query protein, counting the literature number of articles appearing the interaction relationship about the query protein. Moreover, PPLook provides heterogeneous search and a user-friendly graphical interface.</p> <p>Conclusions</p> <p>PPLook is an effective tool for biologists and biosystem developers who need to access PPI information from the literature. PPLook is freely available for non-commercial users at <url>http://meta.usc.edu/softs/PPLook</url>.</p

Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles

<p>Abstract</p> <p>Background</p> <p>Experimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles' relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles.</p> <p>Results</p> <p>To evaluate the performance of our PPI text classifier, we conducted experiments based on the BioCreAtIvE-II IAS dataset. Our results show that adding likely-labeled data generally increases AUC by 3~6%, indicating better ranking ability. Our experiments also show that our newly-proposed term-weighting scheme has the highest AUC among all common weighting schemes. Our final model achieves an F-measure and AUC 2.9% and 5.0% higher than those of the top-ranking system in the IAS challenge.</p> <p>Conclusion</p> <p>Our experiments demonstrate the effectiveness of integrating unlabeled and likely labeled data to augment a PPI text classification system. Our mixed model is suitable for ranking purposes whereas our hierarchical model is better for filtering. In addition, our results indicate that supervised weighting schemes outperform unsupervised ones. Our newly-proposed weighting scheme, TFBRF, which considers documents that do not contain the target word, avoids some of the biases found in traditional weighting schemes. Our experiment results show TFBRF to be the most effective among several other top weighting schemes.</p

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

BioInfer: a corpus for information extraction in the biomedical domain

BACKGROUND: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. RESULTS: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. CONCLUSION: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at

Simultaneous Induction of Non-Canonical Autophagy and Apoptosis in Cancer Cells by ROS-Dependent ERK and JNK Activation

Background: Chemotherapy-induced reduction in tumor load is a function of apoptotic cell death, orchestrated by intracellular caspases. However, the effectiveness of these therapies is compromised by mutations affecting specific genes, controlling and/or regulating apoptotic signaling. Therefore, it is desirable to identify novel pathways of cell death, which could function in tandem with or in the absence of efficient apoptotic machinery. In this regard, recent evidence supports the existence of a novel cell death pathway termed autophagy, which is activated upon growth factor deprivation or exposure to genotoxic compounds. The functional relevance of this pathway in terms of its ability to serve as a stress response or a truly death effector mechanism is still in question; however, reports indicate that autophagy is a specialized form of cell death under certain conditions. Methodology/Principal Findings: We report here the simultaneous induction of non-canonical autophagy and apoptosis in human cancer cells upon exposure to a small molecule compound that triggers intracellular hydrogen peroxide (H2O2) production. Whereas, silencing of beclin1 neither inhibited the hallmarks of autophagy nor the induction of cell death, Atg 7 or Ulk1 knockdown significantly abrogated drug-induced H2O2-mediated autophagy. Furthermore, we provide evidence that activated extracellular regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) are upstream effectors controlling both autophagy and apoptosis in response to elevated intracellular H2O2. Interestingly, inhibition of JNK activity reversed the increase in Atg7 expression in this system, thus indicating that JNK may regulate autophagy by activating Atg7. Of note, the small molecule compound triggered autophagy and apoptosis in primary cells derived from patients with lymphoma, but not in non-transformed cells. Conclusions/Significance: Considering that loss of tumor suppressor beclin 1 is associated with neoplasia, the ability of this small molecule compound to engage both autophagic and apoptotic machineries via ROS production and subsequent activation of ERK and JNK could have potential translational implications.Singapore. Biomedical Research CouncilSingapore. Ministry of Educatio

A systematic review of different models of home and community care services for older persons

<p>Abstract</p> <p>Background</p> <p>Costs and consumer preference have led to a shift from the long-term institutional care of aged older people to home and community based care. The aim of this review is to evaluate the outcomes of case managed, integrated or consumer directed home and community care services for older persons, including those with dementia.</p> <p>Methods</p> <p>A systematic review was conducted of non-medical home and community care services for frail older persons. MEDLINE, PsycINFO, CINAHL, AgeLine, Scopus and PubMed were searched from 1994 to May 2009. Two researchers independently reviewed search results.</p> <p>Results</p> <p>Thirty five papers were included in this review. Evidence from randomized controlled trials showed that case management improves function and appropriate use of medications, increases use of community services and reduces nursing home admission. Evidence, mostly from non-randomized trials, showed that integrated care increases service use; randomized trials reported that integrated care does not improve clinical outcomes. The lowest quality evidence was for consumer directed care which appears to increase satisfaction with care and community service use but has little effect on clinical outcomes. Studies were heterogeneous in methodology and results were not consistent.</p> <p>Conclusions</p> <p>The outcomes of each model of care differ and correspond to the model's focus. Combining key elements of all three models may maximize outcomes.</p

Benchmarking natural-language parsers for biological applications using dependency graphs

BACKGROUND: Interest is growing in the application of syntactic parsers to natural language processing problems in biology, but assessing their performance is difficult because differences in linguistic convention can falsely appear to be errors. We present a method for evaluating their accuracy using an intermediate representation based on dependency graphs, in which the semantic relationships important in most information extraction tasks are closer to the surface. We also demonstrate how this method can be easily tailored to various application-driven criteria. RESULTS: Using the GENIA corpus as a gold standard, we tested four open-source parsers which have been used in bioinformatics projects. We first present overall performance measures, and test the two leading tools, the Charniak-Lease and Bikel parsers, on subtasks tailored to reflect the requirements of a system for extracting gene expression relationships. These two tools clearly outperform the other parsers in the evaluation, and achieve accuracy levels comparable to or exceeding native dependency parsers on similar tasks in previous biological evaluations. CONCLUSION: Evaluating using dependency graphs allows parsers to be tested easily on criteria chosen according to the semantics of particular biological applications, drawing attention to important mistakes and soaking up many insignificant differences that would otherwise be reported as errors. Generating high-accuracy dependency graphs from the output of phrase-structure parsers also provides access to the more detailed syntax trees that are used in several natural-language processing techniques

Non-Antioxidant Properties of α-Tocopherol Reduce the Anticancer Activity of Several Protein Kinase Inhibitors In Vitro

The antioxidant properties of α-tocopherol have been proposed to play a beneficial chemopreventive role against cancer. However, emerging data also indicate that it may exert contrasting effects on the efficacy of chemotherapeutic treatments when given as dietary supplement, being in that case harmful for patients. This dual role of α-tocopherol and, in particular, its effects on the efficacy of anticancer drugs remains poorly documented. For this purpose, we studied here, using high throughput flow cytometry, the direct impact of α-tocopherol on apoptosis and cell cycle arrest induced by different cytotoxic agents on various models of cancer cell lines in vitro. Our results indicate that physiologically relevant concentrations of α-tocopherol strongly compromise the cytotoxic and cytostatic action of various protein kinase inhibitors (KI), while other classes of chemotherapeutic agents or apoptosis inducers are unaffected by this vitamin. Interestingly, these anti-chemotherapeutic effects of α-tocopherol appear to be unrelated to its antioxidant properties since a variety of other antioxidants were completely neutral toward KI-induced cell cycle arrest and cell death. In conclusion, our data suggest that dietary α-tocopherol could limit KI effects on tumour cells, and, by extent, that this could result in a reduction of the clinical efficacy of anti-cancer treatments based on KI molecules