Annotation Terms Research Articles

The top skin-associated genes: a comparative analysis of human and mouse skin transcriptomes

The mouse represents a key model system for the study of the physiology and biochemistry of skin. Comparison of skin between mouse and human is critical for interpretation and application of data from mouse experiments to human disease. Here, we review the current knowledge on structure and immunology of mouse and human skin. Moreover, we present a systematic comparison of human and mouse skin transcriptomes. To this end, we have recently used a genome-wide database of human gene expression to identify genes highly expressed in skin, with no, or limited expression elsewhere - human skin-associated genes (hSAGs). Analysis of our set of hSAGs allowed us to generate a comprehensive molecular characterization of healthy human skin. Here, we used a similar database to generate a list of mouse skin-associated genes (mSAGs). A comparative analysis between the top human (n=666) and mouse (n=873) skin-associated genes (SAGs) revealed a total of only 30.2% identity between the two lists. The majority of shared genes encode proteins that participate in structural and barrier functions. Analysis of the top functional annotation terms revealed an overlap for morphogenesis, cell adhesion, structure, and signal transduction. The results of this analysis, discussed in the context of published data, illustrate the diversity between the molecular make up of skin of both species and grants a probable explanation, why results generated in murine in vivo models often fail to translate into the human.

Augmenting multi-instance multilabel learning with sparse bayesian models for skin biopsy image analysis.

Skin biopsy images can reveal causes and severity of many skin diseases, which is a significant complement for skin surface inspection. Automatic annotation of skin biopsy image is an important problem for increasing efficiency and reducing the subjectiveness in diagnosis. However it is challenging particularly when there exists indirect relationship between annotation terms and local regions of a biopsy image, as well as local structures with different textures. In this paper, a novel method based on a recent proposed machine learning model, named multi-instance multilabel (MIML), is proposed to model the potential knowledge and experience of doctors on skin biopsy image annotation. We first show that the problem of skin biopsy image annotation can naturally be expressed as a MIML problem and then propose an image representation method that can capture both region structure and texture features, and a sparse Bayesian MIML algorithm which can produce probabilities indicating the confidence of annotation. The proposed algorithm framework is evaluated on a real clinical dataset containing 12,700 skin biopsy images. The results show that it is effective and prominent.

Mining Association Rules from Gene Ontology and Protein Networks: Promises and Challenges.

In biology, the accumulation of raw experimental data has been accompanied by the accumulation of functional information encoded by using annotation terms. Such annotations are organized into ontologies such as Gene Ontology. Each annotation may be derived using different methods that yield to different reliability of such information. The analysis of this annotated data using association rules may evidence the co-occurrence of annotations improving for instance annotation quality. Here we give a short survey of these methods discussing possible future directions of research. We considered in particular the impact of the nature of annotations on algorithms performances by discussing two case studies and evidencing the impact of quality of annotations.

AIM: a comprehensive Arabidopsis interactome module database and related interologs in plants.

Systems biology analysis of protein modules is important for understanding the functional relationships between proteins in the interactome. Here, we present a comprehensive database named AIM for Arabidopsis (Arabidopsis thaliana) interactome modules. The database contains almost 250 000 modules that were generated using multiple analysis methods and integration of microarray expression data. All the modules in AIM are well annotated using multiple gene function knowledge databases. AIM provides a user-friendly interface for different types of searches and offers a powerful graphical viewer for displaying module networks linked to the enrichment annotation terms. Both interactive Venn diagram and power graph viewer are integrated into the database for easy comparison of modules. In addition, predicted interologs from other plant species (homologous proteins from different species that share a conserved interaction module) are available for each Arabidopsis module. AIM is a powerful systems biology platform for obtaining valuable insights into the function of proteins in Arabidopsis and other plants using the modules of the Arabidopsis interactome.Database URL:http://probes.pw.usda.gov/AIM

Co-expression Network Analysis of Human lncRNAs and Cancer Genes.

We used gene co-expression network analysis to functionally annotate long noncoding RNAs (lncRNAs) and identify their potential cancer associations. The integrated microarray data set from our previous study was used to extract the expression profiles of 1,865 lncRNAs. Known cancer genes were compiled from the Catalogue of Somatic Mutations in Cancer and UniProt databases. Co-expression analysis identified a list of previously uncharacterized lncRNAs that showed significant correlation in expression with core cancer genes. To further annotate the lncRNAs, we performed a weighted gene co-expression network analysis, which resulted in 37 co-expression modules. Three biologically interesting modules were analyzed in depth. Two of the modules showed relatively high expression in blood and brain tissues, whereas the third module was found to be downregulated in blood cells. Hub lncRNA genes and enriched functional annotation terms were identified within the modules. The results suggest the utility of this approach as well as potential roles of uncharacterized lncRNAs in leukemia and neuroblastoma.

Genome-wide transcriptome analysis of Chinese pollination-constant nonastringent persimmon fruit treated with ethanol

BackgroundThe persimmon Diospyros kaki Thunb. is an important commercial and deciduous fruit tree. The fruits have proanthocyanidin (PA) content of >25% of the dry weight and are astringent. PAs cause astringency that is often undesirable for human consumption; thus, the removal of astringency is an important practice in the persimmon industry. Soluble PAs can be converted to insoluble PAs by enclosing the fruit in a polyethylene bag containing diluted ethanol. The genomic resource development of the persimmon is delayed because of its large and complex genome. Second-generation sequencing is an efficient technique for generating huge sequences that can represent a large number of genes and their expression levels.ResultsWe used 454 sequencing for the de novo transcriptome assembly of persimmon fruit treated with 5% ethanol (Tr library) and without treatment as the control (Co library) to investigate the genes and pathways that control PA biosynthesis and other secondary metabolites. We obtained 374.6 Mb in clean nucleotides comprising 624,690 and 626,203 clean sequencing reads from the Tr and Co libraries, respectively. We also identified 83,898 unigenes; 54,719 (~65.2%) unigenes were annotated based on similarity searches with known proteins. Up to 14,954 of the unigenes were assigned to the protein database Clusters of Orthologous Groups (COG), 24,337 were assigned to the term annotation database of Gene Ontology (GO), and 45,506 were assigned to 200 pathways in the database of Kyoto Encyclopedia of Genes and Genomes (KEGG). The two libraries were compared to identify the differentially expressed unigenes. The expression levels of genes involved in PA biosynthesis and tannin coagulation were analysed, and some of them were verified using quantitative real time PCR (qRT-PCR).ConclusionsThis study provides abundant genomic data for persimmon and offers comprehensive sequence resources for persimmon research. The transcriptome dataset will improve our understanding of the molecular mechanisms of tannin coagulation and other biochemical processes in persimmons.

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

BackgroundThe number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown.ResultsWe carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably.ConclusionsThis approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.

Predicting and Analyzing Lipid-Binding Proteins Using an Efficient Physicochemical Property Mining Method

Lipid-binding proteinsjoin many important biological processes. Lipid-binding proteins are highly related to diseases, such as metabolic diseases, cancer and autoimmune diseases. The existed studies of predictinglipid-binding functions or predictinglipid-binding sites, but notidentify the lipid-binding proteins ornot lipid-binding proteins.This study purpose a systematic approach to identify a small set of physicochemical and biochemical properties in AAindex database to design support vector machine (SVM) based classifier for predicting and analyzing lipid-binding proteins. The merits of this study are three-fold: First, we establish a data set of lipid-binding proteins collected from SwissProt utilizing the gene ontology (GO) annotation terms. Secondly, utilize an efficient genetic algorithm based optimization method IBCGA to select an informative set of feature vectors of representing sequences from the viewpoint of machine learning. Thirdly, analyze the selected feature vectors to identify the related physicochemical properties which may affect the binding mechanism oflipid-binding proteins. In this study, to overcome the unbalanced dataset problem caused from the number of putative negative dataset (537,346) being almost 530 times to that of positive dataset (1,053), a dataset determining technique is proposed.Then the dataset is applied to make a high performance classifier. The prediction accuracy of independent test is 77.75% using 18 properties. The selected 18 properties may divide into 6 groupings:alpha and turn propensities, beta propensity, Composition, Hydrophobicity, Physicochemical properties and other properties.Hydrophobicity and alpha-helix are most relative to lipid-binding protein.

Assessing the impact of human genome annotation choice on RNA-seq expression estimates

BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

Creating a semantically-enhanced cloud services environment through ontology evolution

Currently, the availability of Web resources has grown enormously to the point that whatever a user needs at a given moment can potentially be found on the Internet. These resources are not limited to data items anymore, functionality delivered through some sort of service architectural model is also offered on the Internet. In the last few years, cloud computing has emerged as one of the most popular computing models to provide services over the Internet. However, as the number of available cloud services increases, the problem of service discovery and selection arises. Experience indicates that semantic technologies can provide the basis for enhanced and more precise search processes. In this paper, we present a platform that makes use of semantic technologies and techniques to facilitate the discovery of cloud resources meeting the users’ needs. We propose an architecture that puts together semantic annotation techniques, ontology evolution, term extraction and indexing resources to semantically annotate cloud services, and a semantic search engine that leverages the semantic description of the cloud resources to find them from keyword-based searches. A comprehensive evaluation of the tool in the ICT domain has produced very promising results and is also presented in this article.

De Novo Assembly and Characterization of the Transcriptome, and Development of SSR Markers in Wax Gourd (Benicasa hispida)

BackgroundWax gourd is a widely used vegetable of Cucuribtaceae, and also has important medicinal and health values. However, the genomic resources of wax gourd were scarcity, and only a few nucleotide sequences could be obtained in public databases.Methodology/Principal FindingsIn this study, we examined transcriptome in wax gourd. More than 44 million of high quality reads were generated from five different tissues of wax gourd using Illumina paired-end sequencing technology. Approximately 4 Gbp data were generated, and de novo assembled into 65,059 unigenes, with an N50 of 1,132 bp. Based on sequence similarity search with known protein database, 36,070 (55.4%) showed significant similarity to known proteins in Nr database, and 24,969 (38.4%) had BLAST hits in Swiss-Prot database. Among the annotated unigenes, 14,994 of wax gourd unigenes were assigned to GO term annotation, and 23,977 were found to have COG classifications. In addition, a total of 18,713 unigenes were assigned to 281 KEGG pathways. Furthermore, 6,242 microsatellites (simple sequence repeats) were detected as potential molecular markers in wax gourd. Two hundred primer pairs for SSRs were designed for validation of the amplification and polymorphism. The result showed that 170 of the 200 primer pairs were successfully amplified and 49 (28.8%) of them exhibited polymorphisms.Conclusion/SignificanceOur study enriches the genomic resources of wax gourd and provides powerful information for future studies. The availability of this ample amount of information about the transcriptome and SSRs in wax gourd could serve as valuable basis for studies on the physiology, biochemistry, molecular genetics and molecular breeding of this important vegetable crop.

Transcriptome Analysis in Sheepgrass (Leymus chinensis): A Dominant Perennial Grass of the Eurasian Steppe

BackgroundSheepgrass [Leymus chinensis (Trin.) Tzvel.] is an important perennial forage grass across the Eurasian Steppe and is known for its adaptability to various environmental conditions. However, insufficient data resources in public databases for sheepgrass limited our understanding of the mechanism of environmental adaptations, gene discovery and molecular marker development.ResultsThe transcriptome of sheepgrass was sequenced using Roche 454 pyrosequencing technology. We assembled 952,328 high-quality reads into 87,214 unigenes, including 32,416 contigs and 54,798 singletons. There were 15,450 contigs over 500 bp in length. BLAST searches of our database against Swiss-Prot and NCBI non-redundant protein sequences (nr) databases resulted in the annotation of 54,584 (62.6%) of the unigenes. Gene Ontology (GO) analysis assigned 89,129 GO term annotations for 17,463 unigenes. We identified 11,675 core Poaceae-specific and 12,811 putative sheepgrass-specific unigenes by BLAST searches against all plant genome and transcriptome databases. A total of 2,979 specific freezing-responsive unigenes were found from this RNAseq dataset. We identified 3,818 EST-SSRs in 3,597 unigenes, and some SSRs contained unigenes that were also candidates for freezing-response genes. Characterizations of nucleotide repeats and dominant motifs of SSRs in sheepgrass were also performed. Similarity and phylogenetic analysis indicated that sheepgrass is closely related to barley and wheat.ConclusionsThis research has greatly enriched sheepgrass transcriptome resources. The identified stress-related genes will help us to decipher the genetic basis of the environmental and ecological adaptations of this species and will be used to improve wheat and barley crops through hybridization or genetic transformation. The EST-SSRs reported here will be a valuable resource for future gene-phenotype studies and for the molecular breeding of sheepgrass and other Poaceae species.

Identification of Differentially Expressed Genes of Trichinella spiralis Larvae after Exposure to Host Intestine Milieu

Although it has been known for many years that T. spiralis muscle larvae (ML) can not invade intestinal epithelial cells unless they are exposed to the intestinal milieu and activated into intestinal infective larvae (IIL), which genes in IIL are involved in the process of invasion is still unknown. In this study, suppression subtractive hybridization (SSH) was performed to identify differentially expressed genes between IIL and ML. SSH library was constructed using cDNA generated from IIL as the ‘tester’. About 110 positive clones were randomly selected from the library and sequenced, of which 33 T. spiralis genes were identified. Thirty encoded proteins were annotated according to Gene Ontology Annotation in terms of molecular function, biological process, and cellular localization. Out of 30 annotated proteins, 16 proteins (53.3%) had binding activity and 12 proteins (40.0%) had catalytic activity. The results of real-time PCR showed that the expression of nine genes (Ts7, Ndr family protein; Ts8, serine/threonine-protein kinase polo; Ts11, proteasome subunit beta type-7; Ts17, nudix hydrolase; Ts19, ovochymase-1; Ts22, fibronectin type III domain protein; Ts23, muscle cell intermediate filament protein OV71; Ts26, neutral and basic amino acid transport protein rBAT and Ts33, FACT complex subunit SPT16) from 33 T. spiralis genes in IIL were up-regulated compared with that of ML. The present study provide a group of the potential invasion-related candidate genes and will be helpful for further studies of mechanisms by which T. spiralis infective larvae recognize and invade the intestinal epithelial cells.

Interpreting Motion: Grounded Representations for Spatial Language Inderjeet Mani* and James Pustejovsky‡ (*Children's Organization of Southeast Asia and ‡Brandeis University) Oxford University Press (Explorations in Language and Space series, edited by Emile Van Der Zee), 2012, xiii+166 pp; hardbound, ISBN 978-0-19-960124-0, £60.00

<b>Interpreting Motion: Grounded Representations for Spatial Language</b> <b>Inderjeet Mani</b>* <b>and James Pustejovsky</b>^‡ (*Children's Organization of Southeast Asia and ^‡Brandeis University) Oxford University Press (Explorations in Language and Space series, edited by Emile Van Der Zee), 2012, xiii+166 pp; hardbound, ISBN 978-0-19-960124-0, £60.00

Disambiguation of PharmGKB drug–disease relations with NDF-RT and SPL

PharmGKB is a leading resource of high quality pharmacogenomics data that provides information about how genetic variations modulate an individual’s response to drugs. PharmGKB contains information about genetic variations, pharmacokinetic and pharmacodynamic pathways, and the effect of variations on drug-related phenotypes. These relationships are represented using very general terms, however, and the precise semantic relationships among drugs, and diseases are not often captured. In this paper we develop a protocol to detect and disambiguate general clinical associations between drugs and diseases using more precise annotation terms from other data sources. PharmGKB provides very detailed clinical associations between genetic variants and drug response, including genotype-specific drug dosing guidelines, and this procedure will armGKB. The availability of more detailed data will help investigators to conduct more precise queries, such as finding particular diseases caused or treated by a specific drug.We first mapped drugs extracted from PharmGKB drug–disease relationships to those in the National Drug File Reference Terminology (NDF-RT) and to Structured Product Labels (SPLs). Specifically, we retrieved drug and disease role relationships describing and defining concepts according to their relationships with other concepts from NDF-RT. We also used the NCBO (National Center for Biomedical Ontology) annotator to annotate disease terms from the free text extracted from five SPL sections (indication, contraindication, ADE, precaution, and warning). Finally, we used the detailed drug and disease relationship information from NDF-RT and the SPLs to annotate and disambiguate the more general PharmGKB drug and disease associations.

Modular Biological Function Is Most Effectively Captured by Combining Molecular Interaction Data Types

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

Epstein–Barr virus-positive nodal peripheral T cell lymphomas: Clinicopathologic and gene expression profiling study

Epstein–Barr virus-positive peripheral T cell lymphoma, not otherwise specified (EBV+ PTCL-NOS), in which virtually all neoplastic T cells harbor EBV, is a very rare disease with poor prognosis. To analyze the clinicopathologic characteristics and gene expression profile, we retrospectively collected six cases of EBV+ PTCL-NOS with no known primary immunodeficiency. The patients were 5 men and 1 woman, their age ranging from 48 years to 88 years (median 61.5 years). Lymphadenopathy was the most common presentation. Four patients had underlying disease, including HBV carrier, HCV infection, diabetes mellitus, and prostate cancer. All patients showed fatal clinical course in spite of chemotherapy. Histopathologically, monotonous infiltration of atypical lymphocytes of small to medium size was shown in four patients and medium to large tumor cells in two patients. Five patients showed CD4−/CD8+/bF-1+ phenotype with TIA-1 expression. In gene expression analysis using mRNA microarray, genes differentially expressed in EBV+ PTCL-NOS compared to normal reactive lymph nodes included 1515 genes (Mann–Whitney U-test p<0.05, folder change ≥4 times). Enriched functional annotation terms by DAVID were mostly related to immune response, defense response, cell-to-cell signaling, and membrane signaling. Especially, the genes involved in B cell differentiation or activation were mostly down-regulated, and T cell activation was mostly suppressed by down-regulation of activation genes and up-regulation of regulatory genes. Genes associated with cytotoxic activity were mostly up-regulated. Based on its peculiar clinical, histopathologic, and gene expression findings in EBV+ PTCL-NOS, we suggest EBV+ PTCL-NOS as a distinct disease entity from PTCL-NOS. In this study, the finding that most significantly enriched the functional term was immune response, suggesting a specific relation between EBV infection and alteration of immune response in the patients with EBV+ PTCL-NOS.

Artistic Image Analysis Using Graph-Based Learning Approaches

We introduce a new methodology for the problem of artistic image analysis, which among other tasks, involves the automatic identification of visual classes present in an art work. In this paper, we advocate the idea that artistic image analysis must explore a graph that captures the network of artistic influences by computing the similarities in terms of appearance and manual annotation. One of the novelties of our methodology is the proposed formulation that is a principled way of combining these two similarities in a single graph. Using this graph, we show that an efficient random walk algorithm based on an inverted label propagation formulation produces more accurate annotation and retrieval results compared with the following baseline algorithms: bag of visual words, label propagation, matrix completion, and structural learning. We also show that the proposed approach leads to a more efficient inference and training procedures. This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where we show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures.

An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain

In automotive domain, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis (FD) process. Here, the aim of knowledge discovery using text mining (KDT) task is to discover the best-practice repair knowledge from millions of repair verbatim enabling accurate FD. However, the complexity of KDT problem is largely due to the fact that a significant amount of relevant knowledge is buried in noisy and unstructured verbatim. In this paper, we propose a novel ontology-based text mining system, which uses the diagnosis ontology for annotating key terms recorded in the repair verbatim. The annotated terms are extracted in different tuples, which are used to identify the field anomalies. The extracted tuples are further used by the frequently co-occurring clustering algorithm to cluster the repair verbatim data such that the best-practice repair actions used to fix commonly observed symptoms associated with the faulty parts can be discovered. The performance of our system has been validated by using the real world data and it has been successfully implemented in a web based distributed architecture in real life industry.

BioAnnote: A software platform for annotating biomedical documents with application in medical learning environments

Automatic term annotation from biomedical documents and external information linking are becoming a necessary prerequisite in modern computer-aided medical learning systems. In this context, this paper presents BioAnnote, a flexible and extensible open-source platform for automatically annotating biomedical resources. Apart from other valuable features, the software platform includes (i) a rich client enabling users to annotate multiple documents in a user friendly environment, (ii) an extensible and embeddable annotation meta-server allowing for the annotation of documents with local or remote vocabularies and (iii) a simple client/server protocol which facilitates the use of our meta-server from any other third-party application. In addition, BioAnnote implements a powerful scripting engine able to perform advanced batch annotations.