BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature

<p>Abstract</p> <p>Background</p> <p>The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities.</p> <p>Results</p> <p>We developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted.</p> <p>Conclusion</p> <p>These experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at <url>http://edelman.dia.fi.upm.es/biri/</url>.</p

Using Text Mining of PubMed Abstracts As An Evidence Source in Computational Predictions of WW Domain-Mediated Protein-Protein Interactions

Protein-protein interactions (PPIs) are a key regulatory mechanism in coordinating a multitude of processes vital to normal cellular function. There exist a number of wet-lab small-scale and high-throughput methods for accurately identifying PPIs; however, despite their accuracy, these methods are expensive both in terms of time and finances. Complementing experimental methods with computational predictions increases the effectiveness of wet-lab small scale methodologies in identifying high quality protein interaction networks. Computational predictions are made by applying bioinformatics and machine-learning algorithms to large-scale training sets obtained from wet-lab experiments, or by extracting information on PPIs from high volumes of published data that do not directly identify protein interactions but are nonetheless correlated with them. A disadvantage of computational predictions is their high degree of inaccuracy, namely too many false positives and false negatives. To improve the accuracy of computational predictions, it is important to consider interactions that are likely to occur in vivo under certain biological conditions, termed context. One technique for improving prediction accuracy is analyzing data obtained via different types of experiments that consider different features of the co-occurring proteins, such as co-localization, co-expression, correlated mutations, or semantic similarity. These experimental sources and their resulting data are called sources of evidence. Integrating data from multiple independent supporting evidence sources improves prediction accuracy. In this work, I used text mining of PubMed abstracts as an evidence source for protein interactions. I hypothesized that proteins whose names are frequently mentioned in the same abstract are more likely to interact in vivo compared to randomly chosen proteins. A comparison of three text mining techniques gene name co-occurrence, MeSH term indexing, and co-occurrence with a controlled vocabulary shows that co-occurrence with a controlled vocabulary yields the highest precision and recall. I concluded that gene name co-occurrence with a controlled vocabulary can, therefore, be used as a novel evidence source for prediction of WW domain-mediated PPIs

Towards a Protein-Protein Interaction information extraction system: recognizing named entities

[EN] The majority of biological functions of any living being are related to Protein Protein Interactions (PPI). PPI discoveries are reported in form of research publications whose volume grows day after day. Consequently, automatic PPI information extraction systems are a pressing need for biologists. In this paper we are mainly concerned with the named entity detection module of PPIES (the PPI information extraction system we are implementing) which recognizes twelve entity types relevant in PPI context. It is composed of two sub-modules: a dictionary look-up with extensive normalization and acronym detection, and a Conditional Random Field classifier. The dictionary look-up module has been tested with Interaction Method Task (IMT), and it improves by approximately 10% the current solutions that do not use Machine Learning (ML). The second module has been used to create a classifier using the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA 04) data set. It does not use any external resources, or complex or ad hoc post-processing, and obtains 77.25%, 75.04% and 76.13 for precision, recall, and F1-measure, respectively, improving all previous results obtained for this data set.This work has been funded by MICINN, Spain, as part of the "Juan de la Cierva" Program and the Project DIANA-Applications (TIN2012-38603-C02-01), as well as the by the European Commission as part of the WIQ-EI IRSES Project (Grant No. 269180) within the FP 7 Marie Curie People Framework.Danger Mercaderes, RM.; Pla Santamaría, F.; Molina Marco, A.; Rosso, P. (2014). Towards a Protein-Protein Interaction information extraction system: recognizing named entities. Knowledge-Based Systems. 57:104-118. https://doi.org/10.1016/j.knosys.2013.12.010S1041185

Development of a framework for the classification of antibiotics adjuvants

Dissertação de mestrado em BioInformaticsThroughout the last decades, bacteria have become increasingly resistant to available antibiotics, leading to a growing need for new antibiotics and new drug development methodologies. In the last 40 years, there are no records of the development of new antibiotics, which has begun to shorten possible alternatives. Therefore, finding new antibiotics and bringing them to market is increasingly challenging. One approach is finding compounds that restore or leverage the activity of existing antibiotics against biofilm bacteria. As the information in this field is very limited and there is no database regarding this theme, machine learning models were used to predict the relevance of the documents regarding adjuvants. In this project, the BIOFILMad - Catalog of antimicrobial adjuvants to tackle biofilms application was developed to help researchers save time in their daily research. This application was constructed using Django and Django REST Framework for the backend and React for the frontend. As for the backend, a database needed to be constructed since no database entirely focuses on this topic. For that, a machine learning model was trained to help us classify articles. Three different algorithms were used, Support-Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR), combined with a different number of features used, more precisely, 945 and 1890. When analyzing all metrics, model LR-1 performed the best for classifying relevant documents with an accuracy score of 0.8461, a recall score of 0.6170, an f1-score of 0.6904, and a precision score of 0.7837. This model is the best at correctly predicting the relevant documents, as proven by the higher recall score compared to the other models. With this model, our database was populated with relevant information. Our backend has a unique feature, the aggregation feature constructed with Named Entity Recognition (NER). The goal is to identify specific entity types, in our case, it identifies CHEMICAL and DISEASE. An association between these entities was made, thus delivering the user the respective associations, saving researchers time. For example, a researcher can see with which compounds "pseudomonas aeruginosa" has already been tested thanks to this aggregation feature. The frontend was implemented so the user could access this aggregation feature, see the articles present in the database, use the machine learning models to classify new documents, and insert them in the database if they are relevant.Ao longo das últimas décadas, as bactérias tornaram-se cada vez mais resistentes aos antibióticos disponíveis, levando a uma crescente necessidade de novos antibióticos e novas metodologias de desenvolvimento de medicamentos. Nos últimos 40 anos, não há registos do desenvolvimento de novos antibióticos, o que começa a reduzir as alternativas possíveis. Portanto, criar novos antibióticos e torna-los disponíveis no mercado é cada vez mais desafiante. Uma abordagem é a descoberta de compostos que restaurem ou potencializem a atividade dos antibióticos existentes contra bactérias multirresistentes. Como as informações neste campo são muito limitadas e não há uma base de dados sobre este tema, modelos de Machine Learning foram utilizados para prever a relevância dos documentos acerca dos adjuvantes. Neste projeto, foi desenvolvida a aplicação BIOFILMad - Catalog of antimicrobial adjuvants to tackle biofilms para ajudar os investigadores a economizar tempo nas suas pesquisas. Esta aplicação foi construída usando o Django e Django REST Framework para o backend e React para o frontend. Quanto ao backend, foi necessário construir uma base de dados, pois não existe nenhuma que se concentre inteiramente neste tópico. Para isso, foi treinado um modelo machine learning para nos ajudar a classificar os artigos. Três algoritmos diferentes foram usados: Support-Vector Machine (SVM), Random Forest (RF) e Logistic Regression (LR), combinados com um número diferente de features, mais precisamente, 945 e 1890. Ao analisar todas as métricas, o modelo LR-1 teve o melhor desempenho para classificar artigos relevantes com uma accuracy de 0,8461, um recall de 0,6170, um f1-score de 0,6904 e uma precision de 0,7837. Este modelo foi o melhor a prever corretamente os artigos relevantes, comprovado pelo alto recall em comparação com os outros modelos. Com este modelo, a base de dados foi populda com informação relevante. O backend apresenta uma caracteristica particular, a agregação construída com Named-Entity-Recognition (NER). O objetivo é identificar tipos específicos de entidades, no nosso caso, identifica QUÍMICOS e DOENÇAS. Esta classificação serviu para formar associações entre entidades, demonstrando ao utilizador as respetivas associações feitas, permitindo economizar o tempo dos investigadores. Por exemplo, um investigador pode ver com quais compostos a "pseudomonas aeruginosa" já foi testada graças à funcionalidade de agregação. O frontend foi implementado para que o utilizador possa ter acesso a esta funcionalidade de agregação, ver os artigos presentes na base de dados, utilizar o modelo de machine learning para classificar novos artigos e inseri-los na base de dados caso sejam relevantes

BioEve Search: A Novel Framework to Facilitate Interactive Literature Search

Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named “BioEve”) that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease

Shakespeare in the Eighteenth Century: Algorithm for Quotation Identification

Quoting a borrowed excerpt of text within another literary work was infrequently done prior to the beginning of the eighteenth century. However, quoting other texts, particularly Shakespeare, became quite common after that. Our work develops automatic approaches to identify that trend. Initial work focuses on identifying exact and modified sections of texts taken from works of Shakespeare in novels spanning the eighteenth century. We then introduce a novel approach to identifying modified quotes by adapting the Edit Distance metric, which is character based, to a word based approach. This paper offers an introduction to previous uses of this metric within a multitude of fields, describes the implementation of the different methodologies used for quote identification and then shows how a combination of both Edit Distance methods can help achieve a higher accuracy in quote identification than any one method implemented alone with an overall increase of 10%: from 0.638 and 0.609 to 0.737. Although we demonstrate our approach using Shakespeare quotes in eighteenth century novels, the techniques can be generalized to locate exact and/or partial matches between any set of text targets in any corpus. This work would be of value to literary scholars who want to track quotations over time and could also be applied to other languages

Extraction and Classification of App Features from App Reviews

Aasta aastalt on kasvanud bioinformaatikas kasutatavate rakenduste arv.Selle tulemusena on konkreetse ülesande lahendamiseks sobiliku rakenduse leidmine muutunud keerukaks ülesandeks.Rakenduste kirjelduste paremaks süstematiseerimiseks ja otsitavaks muutmiseks on kasutusele võetud erinevaid märksõnade ontoloogiaid. Hetkel annoteeritakse kirjeldusi käsitsi, mis on ajamahukas ning ei anna alati õigeid tulemusi.Antud töös kirjeldame uut annoteerimismeetodit, mis pakub automaatselt välja ühe või mitu märksõna kasutades selleks vaid tööriista vabatekstilist kirjeldust.Selleks kasutab meie meetod uusimaid loomuliku keele töötlemise meetodeid nagu Dirichlet' peitlahutus (latent Dirichlet allocation) ja sõnade vektoresitust (word2vec).Esmane võrdlus meie poolt välja pakutud algoritmi ja käsitsi saadud märgendusega näitab, et tulemused on paljulubavad.The number of tools for bioinformatics is constantly increasing. To organize the available information and to facilitate the search, different ontologies are used. Today annotation of new descriptions is done manually, which is time-consuming and not always correct. We proposed a new annotation method, which, based on the description of the tool, offers one or more annotation labels in accordance with the ontology. In our method, we applied modern methods of natural language processing, such as latent Dirichlet allocation and word2vec. We compared the manual annotation labels with the labels obtained by using our algorithm and the first results look auspicious

SparkIR: a Scalable Distributed Information Retrieval Engine over Spark

Search engines have to deal with a huge amount of data (e.g., billions of documents in the case of the Web) and find scalable and efficient ways to produce effective search results. In this thesis, we propose to use Spark framework, an in memory distributed big data processing framework, and leverage its powerful capabilities of handling large amount of data to build an efficient and scalable experimental search engine over textual documents. The proposed system, SparkIR, can serve as a research framework for conducting information retrieval (IR) experiments. SparkIR supports two indexing schemes, document-based partitioning and term-based partitioning, to adopt document-at-a-time (DAAT) and term-at-a-time (TAAT) query evaluation methods. Moreover, it offers static and dynamic pruning to improve the retrieval efficiency. For static pruning, it employs champion list and tiering, while for dynamic pruning, it uses MaxScore top k retrieval. We evaluated the performance of SparkIR using ClueWeb12-B13 collection that contains about 50M English Web pages. Experiments over different subsets of the collection and compared the Elasticsearch baseline show that SparkIR exhibits reasonable efficiency and scalability performance overall for both indexing and retrieval. Implemented as an open-source library over Spark, users of SparkIR can also benefit from other Spark libraries (e.g., MLlib and GraphX), which, therefore, eliminates the need of usin