MiSearch adaptive pubMed search tool

Summary: MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model for the likelihood that a user will choose to view them. Citation selections are automatically acquired during browsing and used to dynamically update a likelihood model that includes authorship, journal and PubMed indexing information. The user can optionally elect to include or exclude specific features and vary the importance of timeliness in the ranking

PubMed and beyond: a survey of web tools for searching biomedical literature

The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature. This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters. However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth. In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement. Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications. These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed. In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development. Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search. Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field

A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder

<p>Abstract</p> <p>Background</p> <p>Lithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania.</p> <p>Results</p> <p>Using microarray and rt-QPCR assays, we identified candidate genes that are differentially expressed with lithium treatment. We used a systems biology approach to identify interactions among these candidate genes and develop a network of genes that interact with the differentially expressed candidates. Notably, we also identified cocaine as having a potential influence on the network, consistent with the observed high rate of comorbidity for BD and cocaine abuse. The resulting network represents a novel hypothesis on how multiple genetic influences on bipolar disorder are impacted by both lithium treatment and cocaine use. Testing this network for association with BD and related phenotypes, we find that it is significantly over-represented for genes that participate in signal transduction, consistent with our hypothesized-gene-by environment interaction. In addition, it models related pharmacogenomic, psychiatric, and chemical dependence phenotypes.</p> <p>Conclusions</p> <p>We offer a network model of gene-by-environment interaction associated with lithium's effectiveness in treating BD mania, as well as the observed high rate of comorbidity of BD and cocaine abuse. We identified drug targets within this network that represent immediate candidates for therapeutic drug testing. Posing novel hypotheses for validation in future work, we prioritized SNPs near genes in the network based on functional annotation. We also developed a "concept signature" for the genes in the network and identified additional candidate genes that may influence the system because they are significantly associated with the signature.</p

Prediction of Relevant Biomedical Documents: a Human Microbiome Case Study

Background: Retrieving relevant biomedical literature has become increasingly difficult due to the large volume and rapid growth of biomedical publication. A query to a biomedical retrieval system often retrieves hundreds of results. Since the searcher will not likely consider all of these documents, ranking the documents is important. Ranking by recency, as PubMed does, takes into account only one factor indicating potential relevance. This study explores the use of the searcher’s relevance feedback judgments to support relevance ranking based on features more general than recency. Results: It was found that the researcher’s relevance judgments could be used to accurately predict the relevance of additional documents: both using tenfold cross-validation and by training on publications from 2008–2010 and testing on documents from 2011. Conclusions: This case study has shown the promise for relevance feedback to improve biomedical document retrieval. A researcher’s judgments as to which initially retrieved documents are relevant, or not, can be leveraged to predict additional relevant documents

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user&apos;s specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user&apos;s feedback and efficiently processes the function to return relevant articles in real time.1114Nsciescopu

A Relevance Feedback-Based System For Quickly Narrowing Biomedical Literature Search Result

The online literature is an important source that helps people find the information. The quick increase of online literature makes the manual search process for the most relevant information a very time-consuming task and leads to sifting through many results to find the relevant ones. The existing search engines and online databases return a list of results that satisfy the user\u27s search criteria. The list is often too long for the user to go through every hit if he/she does not exactly know what he/she wants or/and does not have time to review them one by one. My focus is on how to find biomedical literature in a fastest way. In this dissertation, I developed a biomedical literature search system that uses relevance feedback mechanism, fuzzy logic, text mining techniques and Unified Medical Language System. The system extracts and decodes information from the online biomedical documents and uses the extracted information to first filter unwanted documents and then ranks the related ones based on the user preferences. I used text mining techniques to extract PDF document features and used these features to filter unwanted documents with the help of fuzzy logic. The system extracts meaning and semantic relations between texts and calculates the similarity between documents using these relations. Moreover, I developed a fuzzy literature ranking method that uses fuzzy logic, text mining techniques and Unified Medical Language System. The ranking process is utilized based on fuzzy logic and Unified Medical Language System knowledge resources. The fuzzy ranking method uses semantic type and meaning concepts to map the relations between texts in documents. The relevance feedback-based biomedical literature search system is evaluated using a real biomedical data that created using dobutamine (drug name). The data set contains 1,099 original documents. To obtain coherent and reliable evaluation results, two physicians are involved in the system evaluation. Using (30-day mortality) as specific query, the retrieved result precision improves by 87.7% in three rounds, which shows the effectiveness of using relevance feedback, fuzzy logic and UMLS in the search process. Moreover, the fuzzy-based ranking method is evaluated in term of ranking the biomedical search result. Experiments show that the fuzzy-based ranking method improves the average ranking order accuracy by 3.35% and 29.55% as compared with UMLS meaning and semantic type methods respectively

Query Based Sampling and Multi-Layered Semantic Analysis to find Robust Network of Drug-Disease Associations

This thesis presents the design and implementation of a system to discover the semantically related networks of drug-disease associations, called DDNet, from medical literature. A fully functional DDNet can be transformative in identification of drug targets and may new avenues for drug repositioning in clinical and translational research. In particular, a Local Latent Semantic Analysis (LLSA) was introduced to implement a system that is efficient, scalalble and relatively free from systemi bias. In addition, a query-based sampling was introduced to find representative samples from the ocean of data to build model that is relatively free from garbage-in garbage-out syndrome. Also, the concept of mapping ontologies was adopted to determine the relevant results and reverse ontology mapping were used to create a network of associations. In addition, a web service application was developed to query the system and visualize the computed network of associations in a form that is easy to interact. A pilot study was conducted to evaluate the performance of the system using both subjective and objective measures. The PahrmGKB was used as the gold standard and the PR curve was obtained from a large number of queries at different recall points. Empirical analyses suggest that DDNet is robust, relatively stable and scalable over traditional Global LSA model

The Bioinformationista: New Roles and Responsibilities for a Bioinformationist

http://deepblue.lib.umich.edu/bitstream/2027.42/94429/1/the_bioinformationista_new_roles_and_responsibilities_for_a_bioinformationist.pd

Extracting knowledge from documents related with invasive fungal infections in iron overload context

Dissertação de Mestrado em BioinformáticaInvasive fungal infections caused by Candida are associated with high mortality and morbidity rates in hospitalized patients. Iron plays a major role in these infections, as they are exacerbated under iron overload conditions. In this context, it is important to understand the association between iron levels and invasive fungal infections, as it can serve as an indicator of the severity of the disease, and eventually it can help establish measures to improve treatment efficacy. Nowadays, manually inferring these associations from biomedical documents is a time consuming task, due to the high amount of available scientific text data. As such, these tasks naturally benefit from the Biomedical Text Mining field, which includes a wide variety of methods for automatic extraction of high-quality information from biomedical text documents. In this work, relevant documents related to iron overload and fungal infections were retrieved from PubMed to build a corpus. Then, both Named Entity Recognition and Relation Extraction processes were executed using the @Note text mining tool. Finally, relevant sentences were manually extracted and a curated dataset with documents containing those sentences was created. Since the number of publications obtained about Candida and iron overload was very low, the analysis was made taking into account all fungi. A total of 15 publications were considered relevant and 168 relevant associations were extracted. Although associations of iron levels with both severity of infection and treatment efficacy were not extracted, it was possible to conclude that, in many cases, iron overload is a predictor for fungal infections, and patients’ iron levels highly affect treatment efficacy. The Biomedical Text Mining process described in the present thesis enabled the creation of a dataset of relevant biomedical publications containing interesting associations between fungal infections, drugs and associated diseases in a clinical context of iron overload, although in the future this process could be improved, especially regarding dictionaries, in order to obtain a higher number of relevant publications.As infeções fúngicas invasivas causadas por Candida estão associadas a elevadas taxas de mortalidade e morbilidade em doentes hospitalizados. O ferro tem um papel importante neste tipo de infeções, visto que estas são exacerbadas em condições de excesso de ferro. Neste contexto, é extremamente importante compreender a associação entre os níveis de ferro e infeções fúngicas invasivas, pois pode servir como indicador da severidade da doença e, eventualmente, ajudar a estabelecer medidas para melhorar a eficácia de tratamento. Atualmente, inferir manualmente este tipo de associações de documentos biomédicos revela-se uma tarefa bastante demorada, devido ao elevado volume de dados de texto científico disponíveis. Como tal, estas tarefas beneficiam claramente da área da mineração de textos biomédicos, que inclui uma ampla variedade de métodos para extração de informação de alta qualidade de documentos de texto biomédicos. No presente trabalho, foram identificados, inicialmente, documentos relevantes que associam o ferro com infeções fúngicas invasivas para construir um corpus. De seguida, os processos de Reconhecimento de entidades nomeadas e Extração de relações foram realizados usando a ferramenta de mineração de textos @Note. Finalmente, as frases mais relevantes foram extraídas e foi criado um corpus curado de documentos contendo essas mesmas frases. Visto que o número de publicações obtidas relacionadas com Candida e excesso de ferro foi muito baixo, a análise foi feita tendo em conta todos os fungos. Um total de 15 publicações foram consideradas relevantes e 168 associações foram extraídas. Embora não tivesse sido possível extrair associações entre níveis de ferro e a eficácia do tratamento/severidade da infeção, foi possível concluir que o excesso de ferro prevê o surgimento de infeções fúngicas em muitos casos, e que os níveis de ferro dos pacientes afetam fortemente a eficácia do tratamento. O processo de mineração de textos biomédicos no presente trabalho possibilitou a criação de um corpus de publicações biomédicas relevantes contendo associações interessantes entre infeções fúngicas, fármacos e doenças associadas, no contexto clínico de excesso de ferro, embora este processo pudesse ser melhorado no futuro, especialmente no que diz respeito aos dicionários, para que seja possível a obtenção de um maior número de publicações relevantes