Literature Data Mining Research Articles

Primary Drug Resistance in South Africa: Data from 10 Years of Surveys

HIV-1 transmitted drug resistance (TDR) could reverse the gains of antiretroviral rollout. To ensure that current first-line therapies remain effective, TDR levels in recently infected treatment-naive patients need to be monitored. A literature review and data mining exercise was carried out to determine the temporal trends in TDR in South Africa. In addition, 72 sequences from seroconvertors identified from Africa Centre's 2010 HIV surveillance round were also examined for TDR. Publicly available data on TDR were retrieved from GenBank, curated in RegaDB, and analyzed using the Calibrated Population Resistance Program. There was no evidence of TDR from the 2010 rural KwaZulu Natal samples. Ten datasets with a total of 1618 sequences collected between 2000 and 2010 were pooled to provide a temporal analysis of TDR. The year with the highest TDR rate was 2002 [6.67%, 95% confidence interval (CI): 3.09-13.79%; n=6/90]. After 2002, TDR levels returned to <5% (WHO low-level threshold) and showed no statistically significant increase in the interval between 2002 and 2010. The most common mutations were associated with NNRTI resistance, K103N, followed by Y181C and Y188C/L. Five sequences had multiple resistance mutations associated with NNRTI resistance. There is no evidence of TDR in rural KwaZulu-Natal. TDR levels in South Africa have remained low following a downward trend since 2003. Continuous vigilance in monitoring of TDR is needed as more patients are initiated and maintained onto antiretroviral therapy.

DrugPred: A Structure-Based Approach To Predict Protein Druggability Developed Using an Extensive Nonredundant Data Set

Judging if a protein is able to bind orally available molecules with high affinity, i.e. if a protein is druggable, is an important step in target assessment. In order to derive a structure-based method to predict protein druggability, a comprehensive, nonredundant data set containing crystal structures of 71 druggable and 44 less druggable proteins was compiled by literature search and data mining. This data set was subsequently used to train a structure-based druggability predictor (DrugPred) using partial least-squares projection to latent structures discriminant analysis (PLS-DA). DrugPred performed well in discriminating druggable from less druggable binding sites for both internal and external predictions. The method is robust against conformational changes in the binding site and outperforms previously published methods. The superior performance of DrugPred is likely due to the size and composition of the training set which, in contrast to most previously developed methods, only contains cavities that have evolved to bind a natural ligand.

Gene-chip studies of adipogenesis-regulated microRNAs in mouse primary adipocytes and human obesity

BackgroundAdipose tissue abundance relies partly on the factors that regulate adipogenesis, i.e. proliferation and differentiation of adipocytes. While components of the transcriptional program that initiates adipogenesis is well-known, the importance of microRNAs in adipogenesis is less well studied. We thus set out to investigate whether miRNAs would be actively modulated during adipogenesis and obesity.MethodsSeveral models exist to study adipogenesis in vitro, of which the cell line 3T3-L1 is the most well known, albeit not the most physiologically appropriate. Thus, as an alternative, we produced EXIQON microarray of brown and white primary murine adipocytes (prior to and following differentiation) to yield global profiles of miRNAs.ResultsWe found 65 miRNAs regulated during in vitro adipogenesis in primary adipocytes. We evaluated the similarity of our responses to those found in non-primary cell models, through literature data-mining. When comparing primary adipocyte profiles, with those of cell lines reported in the literature, we found a high degree of difference in 'adipogenesis' regulated miRNAs suggesting that the model systems may not be accurately representing adipogenesis. The expression of 10 adipogenesis-regulated miRNAs were studied using real-time qPCR and then we selected 5 miRNAs, that showed robust expression, were profiled in subcutaneous adipose tissue obtained from 20 humans with a range of body mass indices (BMI, range = 21-48, and all samples have U133+2 Affymetrix profiles provided). Of the miRNAs tested, mir-21 was robustly expressed in human adipose tissue and positively correlated with BMI (R2 = 0.49, p < 0.001).ConclusionIn conclusion, we provide a preliminary analysis of miRNAs associated with primary cell in vitro adipogenesis and demonstrate that the inflammation-associated miRNA, mir-21 is up-regulated in subcutaneous adipose tissue in human obesity. Further, we provide a novel transcriptomics database of EXIQON and Affymetrix adipocyte profiles to facilitate data mining.

Surveying Robot Routing Algorithms With Data Mining Approach

Data mining knowledge in response to technological advances in various Rmynh, foot arena is built there. Data Mining face a different situation that the data size is large and we want to build a small model and not too complicated and yet the data as well as describe. Necessity to use data analysis to reduce the amount and the huge volume of information. One important and practical issues in the world of machine intelligence and is robotics robots routing. Robot router has obstacle detection and how to deal with the decision with obstacle. For routing, algorithms including probabilistic methods (filtering particulate), evolutionary algorithms such as genetic, ants social and optimization particle mass, neural methods - Fuzzy, inequality of matrix method based on gradient methods combined sensor information, etc. There are data mining methods in the years 2010-2008 as a technique for routing and a complete robot has been used and still is in progress. Overview of the methods in the paper mentioned in various articles since 2000 has so far. Although many data mining methods include, but mentioned in this article with specific literature data mining will deal with the routing problem.

Crosstalk between viruses and PML nuclear bodies: a network-based approach

Due to the recent advances in instrumental and scientific methods, cell biology data are generated with increasing speed and quantity. One of these fast developing fields is the crosstalk between promyelocytic leukemia protein nuclear bodies (PML-NBs) and viruses. PML-NBs are dynamic nuclear protein aggregates which are targeted by entire viral particles, viral proteins or viral nucleic acids. Their possible anti-viral properties motivated researchers to investigate the interaction between PML-NBs and viruses in depth. Based on extensive literature data mining, we created a comprehensive PML-NB/virus crosstalk Cytoscape network, which groups not only the most common relations but also less well described findings. The network is easy to navigate and provides a biologically relevant overview which can help finding interesting case studies.

Gel-based mass spectrometric and computational approaches to the mitochondrial proteome of Neurospora

We have used gel electrophoretic techniques including isoelectric focusing, blue native, acid–urea, 16-benzyldimethyl-n-hexadecylammonium chloride or SDS first dimensions and SDS Laemmli or tricine second dimensions to separate the proteins from highly-purified Neurospora mitochondria and sub-mitochondrial fractions (membrane, soluble, protein complexes and ribonucleoproteins). The products of 260 genes, many of them in multiple processed or modified forms, were identified by MALDI-TOF mass spectrometry. This work confirms the existence, expression, and mitochondrial localization of the products of 55 Neurospora genes previously annotated only as predicted or hypothetical, and of 101 genes not identified in previous mass spectrometry studies. Combined with previous mass spectrometry studies, and re-evaluation of genome annotations, we have compiled a curated list of 358 proteins identified in proteomic studies that are likely to be bona fide mitochondrial proteins plus 80 other identified proteins that may be mitochondrial. Literature data mining and computational predictions suggest that Neurospora mitochondria also contain another 299 proteins not yet identified in proteomics projects. Taken together, these data suggest that the products of at least 738 genes comprise the Neurospora mitochondrial proteome.

A survey of data mining and knowledge discovery process models and methodologies

AbstractUp to now, many data mining and knowledge discovery methodologies and process models have been developed, with varying degrees of success. In this paper, we describe the most used (in industrial and academic projects) and cited (in scientific literature) data mining and knowledge discovery methodologies and process models, providing an overview of its evolution along data mining and knowledge discovery history and setting down the state of the art in this topic. For every approach, we have provided a brief description of the proposed knowledge discovery in databases (KDD) process, discussing about special features, outstanding advantages and disadvantages of every approach. Apart from that, a global comparative of all presented data mining approaches is provided, focusing on the different steps and tasks in which every approach interprets the whole KDD process. As a result of the comparison, we propose a new data mining and knowledge discovery process namedrefined data mining processfor developing any kind of data mining and knowledge discovery project. The refined data mining process is built on specific steps taken from analyzed approaches.

A novel semantic framework for toxicogenomics

Complex biological data stored in bioinformatics databases often require additional knowledge to specify and constrain the values held in a particular database. One method of capturing knowledge within bioinformatics applications and databases is the use of ontologies. Existing ontologies can model components of environmental risk and corresponding events, but cannot synthesize them or describe the broader framework of ideas used to conceptualize and study environmental risk. Environmental Risk Ontology (ERO) aims to synthesize and describe a framework for investigating environmental risk. ERO is intended as a metadata framework for data and literature curation and data mining. In this review, we summarize and describe the basic public databases available for toxicogenomics and present a novel approach that uses a semantic knowledge base — which also organizes the literature in a growing, comprehensive bibliographic database — concerning environmental risk and toxicogenomics.

Early over expression of messenger RNA for multiple genes, including insulin, in the Pancreatic Lymph Nodes of NOD mice is associated with Islet Autoimmunity

BackgroundAutoimmune diabetes (T1D) onset is preceded by a long inflammatory process directed against the insulin-secreting β cells of the pancreas. Deciphering the early autoimmune mechanisms represents a challenge due to the absence of clinical signs at early disease stages. The aim of this study was to identify genes implicated in the early steps of the autoimmune process, prior to inflammation, in T1D. We have previously established that insulin autoantibodies (E-IAA) predict early diabetes onset delineating an early phenotypic check point (window 1) in disease pathogenesis. We used this sub-phenotype and applied differential gene expression analysis in the pancreatic lymph nodes (PLN) of 5 weeks old Non Obese Diabetic (NOD) mice differing solely upon the presence or absence of E-IAA. Analysis of gene expression profiles has the potential to provide a global understanding of the disease and to generate novel hypothesis concerning the initiation of the autoimmune process.MethodsAnimals have been screened weekly for the presence of E-IAA between 3 and 5 weeks of age. E-IAA positive or negative NOD mice at least twice were selected and RNAs isolated from the PLN were used for microarray analysis. Comparison of transcriptional profiles between positive and negative animals and functional annotations of the resulting differentially expressed genes, using software together with manual literature data mining, have been performed.ResultsThe expression of 165 genes was modulated between E-IAA positive and negative PLN. In particular, genes coding for insulin and for proteins known to be implicated in tissue remodelling and Th1 immunity have been found to be highly differentially expressed. Forty one genes showed over 5 fold differences between the two sets of samples and 30 code for extracellular proteins. This class of proteins represents potential diagnostic markers and drug targets for T1D.ConclusionOur data strongly suggest that the immune related mechanisms taking place at this early age in the PLN, correlate with homeostatic changes influencing tissue integrity of the adjacent pancreatic tissue. Functional analysis of the identified genes suggested that similar mechanisms might be operating during pre-inflammatory processes deployed in tissues i) hosting parasitic microorganisms and ii) experiencing unrestricted invasion by tumour cells.

Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation

BackgroundThe cell cycle is a complex process that allows eukaryotic cells to replicate chromosomal DNA and partition it into two daughter cells. A relevant regulatory step is in the G0/G1 phase, a point called the restriction (R) point where intracellular and extracellular signals are monitored and integrated.Subcellular localization of cell cycle proteins is increasingly recognized as a major factor that regulates cell cycle transitions. Nevertheless, current mathematical models of the G1/S networks of mammalian cells do not consider this aspect. Hence, there is a need for a computational model that incorporates this regulatory aspect that has a relevant role in cancer, since altered localization of key cell cycle players, notably of inhibitors of cyclin-dependent kinases, has been reported to occur in neoplastic cells and to be linked to cancer aggressiveness.ResultsThe network of the model components involved in the G1 to S transition process was identified through a literature and web-based data mining and the corresponding wiring diagram of the G1 to S transition drawn with Cell Designer notation. The model has been implemented in Mathematica using Ordinary Differential Equations. Time-courses of level and of sub-cellular localization of key cell cycle players in mouse fibroblasts re-entering the cell cycle after serum starvation/re-feeding have been used to constrain network design and parameter determination. The model allows to recapitulate events from growth factor stimulation to the onset of S phase. The R point estimated by simulation is consistent with the R point experimentally determined.ConclusionThe major element of novelty of our model of the G1 to S transition is the explicit modeling of cytoplasmic/nuclear shuttling of cyclins, cyclin-dependent kinases, their inhibitor and complexes. Sensitivity analysis of the network performance newly reveals that the biological effect brought about by Cki overexpression is strictly dependent on whether the Cki is promoting nuclear translocation of cyclin/Cdk containing complexes.

An approach to handling and interpretation of ambiguous data in transcriptome and proteome comparisons

A major challenge towards a comprehensive analysis of biological systems is the integration of data from different "omics" sources and their interpretation at a functional level. Here we address this issue by analysing transcriptomic and proteomic datasets from mouse brain tissue at embryonic days 9.5 and 13.5. We observe a high concordance between transcripts and their corresponding proteins when they were compared at the level of expression ratios between embryonic stages. Absolute expression values show marginal correlation. We show in examples, that poor concordance between protein and transcript expression is in part explained by the fact, that single genes give rise to multiple transcripts and protein variants. The integration of transcriptomic and proteomic data therefore requires proper handling of such ambiguities. A closer inspection of such cases in our datasets suggests, that comparing gene expression at exon level instead of gene level could improve the comparability. To address the biological relevance of differences in expression profiles, literature-data mining and analysis of gene ontology terms are widely used. We show here, that this can be complemented by the inspection of physical properties of genes, transcripts, and proteins.

A systems biology analysis of protein–protein interactions between yeast superoxide dismutases and DNA repair pathways

Superoxide dismutases (SODs) are widely distributed in eukaryotic and prokaryotic species and are responsible for O 2 − scavenging and dismutation to H 2O 2 and O 2. Mutations in the cytoplasmic (Sod1p) or mitochondrial (Sod2p) form of SODs result in aging, neurodegenerative diseases, and carcinogenesis. Diminished activity of SODs leads to reduced activity of DNA repair pathways, and overexpression of SODs in cells defective for DNA repair increases their level of chromatin damage. Unfortunately, little is understood regarding the interplay between SODs and DNA repair proteins and their role in protecting the genome from oxidative damage. To elucidate the association between yeast SODs and DNA repair mechanisms, a systems biology study was performed employing algorithms of literature data mining and the construction of physical protein–protein interactions from large yeast protein databases. The results obtained in this work allow us to draw two models suggesting that yeast SODs act as O 2 − sensors under conditions of redox imbalance, activating and controlling specific DNA repair mechanisms (e.g., recombinational and excision repair pathways), chromatin remodeling, and synthesis of dNTPs.

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

BackgroundCurrent advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC).ResultsOur goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure.ConclusionWe propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models.

A comparative study of cells in inflammation, EAE and MS using biomedical literature data mining

Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average gamma = 2.1) that are commonly found in biological networks.

Upregulation of the tumor suppressor gene menin in hepatocellular carcinomas and its significance in fibrogenesis

The molecular mechanisms underlying the progression of cirrhosis toward hepatocellular carcinoma were investigated by a combination of DNA microarray analysis and literature data mining. By using a microarray screening of suppression subtractive hybridization cDNA libraries, we first analyzed genes differentially expressed in tumor and nontumor livers with cirrhosis from 15 patients with hepatocellular carcinomas. Seventy-four genes were similarly recovered in tumor (57.8% of differentially expressed genes) and adjacent nontumor tissues (64% of differentially expressed genes) compared with histologically normal livers. Gene ontology analyses revealed that downregulated genes (n = 35) were mostly associated with hepatic functions. Upregulated genes (n = 39) included both known genes associated with extracellular matrix remodeling, cell communication, metabolism, and post-transcriptional regulation gene (e.g., ZFP36L1), as well as the tumor suppressor gene menin (multiple endocrine neoplasia type 1; MEN1). MEN1 was further identified as an important node of a regulatory network graph that integrated array data with array-independent literature mining. Upregulation of MEN1 in tumor was confirmed in an independent set of samples and associated with tumor size (P = .016). In the underlying liver with cirrhosis, increased steady-state MEN1 mRNA levels were correlated with those of collagen alpha2(I) mRNA (P < .01). In addition, MEN1 expression was associated with hepatic stellate cell activation during fibrogenesis and involved in transforming growth factor beta (TGF-beta)-dependent collagen alpha2(I) regulation. In conclusion, menin is a key regulator of gene networks that are activated in fibrogenesis associated with hepatocellular carcinoma through the modulation of TGF-beta response.

A Role for Information Collection, Management, and Integration in Structure-Function Studies of G-Protein Coupled Receptors

Elucidation of protein function is greatly facilitated by the availability of an atomic resolution structure or a reliable molecular model. The difficulty of obtaining atomic resolution structures of membrane proteins in general, and of G-protein coupled receptors (GPCRs) in particular, has made the information available from sequence analysis, mutagenesis, and the literature on related GPCRs exceptionally important. Here, we review previous studies of GPCR structure-function from the perspectives of sequence analysis, management of mutagenesis and ligand binding data, and literature data mining. The knowledge derived from these information resources not only constitutes the prerequisites for reliable molecular modeling, but also can provide other insights into GPCR functions. Finally, we review approaches for information integration and applying knowledge discovery techniques to structure-function studies of GPCRs, including molecular modeling itself.

Building a protein name dictionary from full text: a machine learning term extraction approach

BackgroundThe majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature.ResultsWe present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text.ConclusionThis dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

KIDNEY GENE DATABASE: A CURATED AND INTEGRATED DATABASE OF GENES INVOLVED IN KIDNEY DISEASE

We have created a curated and integrated database, the Kidney Gene Database (KGDB) (http://www.urogene.org/kgdb) that contains current information about genes or genomic loci involved in human kidney disease. Genes that undergo molecular, genetic or epigenetic events that affect kidney function are identified through a biomedical literature search and catalogued in the database. Events that are currently screened for are gene amplification, mutation, deletion, polymorphism, loss of heterozygosity, DNA methylation and DNA hypomethylation. In addition, genes that are uniquely expressed in the kidney, as determined by analyzing the expressed sequence tags database and the Serial Analysis of Gene Expression database, are also included in KGDB. For each gene KGDB provides basic information about the gene product, a tissue type gene expression profile, links to protein, mRNA and genomic DNA sequence information, relevant literature citations and cross-references to other databases. We present KGDB, which is to our knowledge the first curated and integrated database of genes involved in human kidney disease. KGDB is free, widely accessible and easy to use, and it provides a wealth of relevant information. In addition, KGDB will be continuously updated every 6 months to include new information published in the biomedical literature or in gene expression databases. We envision that KGDB will serve as a valuable resource for scientists and clinicians.

MedlineR: an open source library in R for Medline literature data mining.

We describe an open source library written in the R programming language for Medline literature data mining. This MedlineR library includes programs to query Medline through the NCBI PubMed database; to construct the co-occurrence matrix; and to visualize the network topology of query terms. The open source nature of this library allows users to extend it freely in the statistical programming language of R. To demonstrate its utility, we have built an application to analyze term-association by using only 10 lines of code. We provide MedlineR as a library foundation for bioinformaticians and statisticians to build more sophisticated literature data mining applications. The library is available from http://dbsr.duke.edu/pub/MedlineR.

Accomplishments and challenges in literature data mining for biology.

We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.