Evaluation of biomedical text-mining systems: lessons learned from information retrieval

Biomedical text-mining systems have great promise for improving the efficiency and productivity of biomedical researchers. However, such systems are still not in routine use. One impediment to their development is the lack of systematic and rigorous evaluation, comparable to the approaches developed for information retrieval systems. The developers of text-mining systems need to improve both test collections for system-oriented evaluation and undertake user-oriented evaluations to determine the most effective use of their systems for their intended audience.     

VTEC: lessons learned from British outbreaks

Important Escherichia coli O157 outbreaks in England and Scotland since 1982-83 are reviewed. The scientific lessons learned from them are described and their legal consequences outlined. The light shed by them on relationships between law and science is discussed, and questions of blame are analysed in the context of Reason's 'resident pathogen' metaphor and Vaughan's study of the 1986 Challenger Space Shuttle disaster.     

PepBank - a database of peptides based on sequence text mining and public peptide data sources

Background  

Peptides are important molecules with diverse biological functions and biomedical uses. To date, there does not exist a single, searchable archive for peptide sequences or associated biological data. Rather, peptide sequences still have to be mined from abstracts and full-length articles, and/or obtained from the fragmented public sources.     

Mitochondrial efficiency: lessons learned from transgenic mice

Metabolic research has, like most areas of research in the life sciences, been affected dramatically by the application of transgenic technologies. Within the specific area of bioenergetics it has been thought that transgenic approaches in mice would provide definitive proof for some longstanding metabolic theories and assumptions. Here we review a number of transgenic approaches that have been used in mice to address theories of mitochondrial efficiency. The focus is largely on genes that affect the coupling of energy substrate oxidation to ATP synthesis, and thus, mice in which the uncoupling protein (Ucp) genes are modified are discussed extensively. Transgenic approaches have indeed provided proof-of-concept in some instances, but in many other instances they have yielded results that are in contrast to initial hypotheses. Many studies have also shown that genetic background can affect phenotypic outcomes, and that the upregulated expression of genes that are related to the modified gene often complicates the interpretation of findings.     

Dissecting phosphorylation networks: lessons learned from yeast

Protein phosphorylation continues to be regarded as one of the most important post-translational modifications found in eukaryotes and has been implicated in key roles in the development of a number of human diseases. In order to elucidate roles for the 518 human kinases, phosphorylation has routinely been studied using the budding yeast Saccharomyces cerevisiae as a model system. In recent years, a number of technologies have emerged to globally map phosphorylation in yeast. In this article, we review these technologies and discuss how these phosphorylation mapping efforts have shed light on our understanding of kinase signaling pathways and eukaryotic proteomic networks in general.     

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.     

Clique-based data mining for related genes in a biomedical database

Background  

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph.     

Immunology of fungal infections: lessons learned from animal models

The continuing AIDS epidemic coupled with increased usage of immunosuppressive drugs to prevent organ rejection or treat autoimmune diseases has resulted in an increase in individuals at risk for acquiring fungal diseases. These concerns highlight the need to elucidate mechanisms of inducing protective immune responses against fungal pathogens. Consequently, several experimental models of human mycoses have been developed to study these diseases. The availability of transgenic animal models allows for in-depth analysis of specific components, receptors, and signaling pathways that elicit protection against fungal diseases. This review focuses on recent advances in our understanding of immune responses to fungal infections gained using animal models.     

Protozoa and pathogenic bacteria: lessons learned from Legionella pneumophila

Bacterivorous protozoa and bacteria have been in co-existence since the origin of life. This co-existence has led unequivocally to the evolution of many different co-interactions. Most bacteria are ingested and digested, but many escape ingestion for various reasons. Others are ingested but evade digestion, and a few, notoriously Legionella pneumophila , even have the capacity of multiplying within the protozoan host. The aims of this study were to elucidate the interactions of various multi-drug-resistant Staphylococcus aureus (MRSA) strains, Listeria monocytogenes sv4b, and Escherichia coli K12 with the amoeba, Acanthamoeba polyphaga . To evaluate the interactions, we set up co-cultures in Neffs' amoebic saline, at a multiplicity of invasion (MOI) of 1:100 of amoeba to bacteria, and a temperature of 37°C, although the effects of MOI and temperature were also assessed. Survival of bacteria and amoeba was checked at regular intervals, coupled with microscopy. It was discovered under our test conditions, that E. coli was ingested and digested by A. polyphaga , but in contrast, L. monocytogenes , had the capacity to flourish in the presence of A. polyphaga . We also report, for the first time, that all six MRSA isolates tested, survived and replicated in association with A. polyphaga , in comparison to conditions where amoebae were absent. Indeed, we also have evidence suggesting that increases in MRSA, in the presence of A. polyphaga , may be attributable to intracellular survival and replication. These findings have profound implications for the hospital environment, where Acanthamoeba sp., are also commonly isolated. In conclusion, this study illustrates the significance of protozoa as vehicles augmenting the survival of MRSA and L. monocytogenes in the environment.     

Protein folding diseases and neurodegeneration: lessons learned from yeast

Budding yeast Saccharomyces cerevisiae has proven to be a valuable model organism for studying fundamental cellular processes across the eukaryotic kingdom including man. In this respect, complementation assays, in which the yeast protein is replaced by a homologous protein from another organism, have been very instructive. A newer trend is to use the yeast cell factory as a toolbox to understand cellular processes controlled by proteins for which the yeast lacks functional counterparts. An increasing number of studies have indicated that S. cerevisiae is a suitable model system to decipher molecular mechanisms involved in a variety of neurodegenerative disorders caused by aberrant protein folding. Here we review the current knowledge gained by the use of so-called humanized yeasts in the field of Huntington's, Parkinson's and Alzheimer's diseases.     

Introduction: microbiology and immunology: lessons learned from Salmonella.

Salmonella enterica, a Gram-negative bacterium, causes significant morbidity and mortality worldwide, and is an excellent model to study bacterial pathogenesis and cellular immune responses. With the development of powerful new technologies, there has been a fusion of research on immunology, molecular biology and cellular microbiology of S. enterica infections. This multidisciplinary research will enhance our understanding of the basic mechanisms of bacterial infections and immunity; it also provides new approaches towards therapeutic and control measures.     

Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation

Background  

Manual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data extraction and entry. Text mining software that can semi- or fully automate information retrieval from the literature would thus provide a significant boost to manual curation efforts.     

Dengue deaths in Puerto Rico: lessons learned from the 2007 epidemic

Background

The incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases. Successful clinical management of severe dengue requires early recognition and supportive care.

Methods

Fatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates. To be included, fatal cases had to have specimen submitted for dengue diagnostic testing including nucleic acid amplification for dengue virus (DENV) in serum or tissue, immunohistochemical testing of tissue, and immunoassay detection of anti-DENV IgM from serum. Medical records from laboratory-positive dengue fatal case-patients were reviewed to identify possible determinants for death.

Results

Among 10,576 reported dengue cases, 40 suspect fatal cases were identified, of which 11 were laboratory-positive, 14 were laboratory-negative, and 15 laboratory-indeterminate. The median age of laboratory-positive case-patients was 26 years (range 5 months to 78 years), including five children aged <15 years; 7 sought medical care at least once prior to hospital admission, 9 were admitted to hospital and 2 died upon arrival. The nine hospitalized case-patients stayed a mean of 15 hours (range: 3–48 hours) in the emergency department (ED) before inpatient admission. Five of the nine case-patients received intravenous methylprednisolone and four received non-isotonic saline while in shock. Eight case-patients died in the hospital; five had their terminal event on the inpatient ward and six died during a weekend. Dengue was listed on the death certificate in only 5 instances.

Conclusions

During a dengue epidemic in an endemic area, none of the 11 laboratory-positive case-patients who died were managed according to current WHO Guidelines. Management issues identified in this case-series included failure to recognize warning signs for severe dengue and shock, prolonged ED stays, and infrequent patient monitoring.     

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.     

Severe malaria: lessons learned from the management of critical illness in children

Two hypotheses have recently been raised to explain the metabolic acidosis (increased blood acidity) of severe malaria, and both are relevant to treatment. The first suggests that a decreased blood volume (hypovolaemia) has an important role in severe malaria; following this, treatment should be based on the current standard paediatric management of acidosis in children with features of cardiovascular compromise. The second hypothesis contends that acidosis in malaria has a metabolic cause and proposes treatment with dichloroacetate. Both hypotheses are plausible and are not mutually exclusive. In truth, the risks and benefits of either treatment are uncertain, and will remain so until large multicentre, randomised controlled trials provide appropriate supportive evidence. As both views involve complex physiological rationales, beyond the usual scope of this journal, I attempt here to present the largely academic aspects of these hypotheses within the practical and contextual aspects of childhood severe malaria.