The Open Applied Informatics Journal最新文献

{"title":"Nomen Est Omen: Quantitative Prediction of Molecular Properties Directly from IUPAC Names","authors":"M. Thormann, D. Vidal, Michael Almstetter, M. Pons","doi":"10.2174/1874136300701010028","DOIUrl":"https://doi.org/10.2174/1874136300701010028","url":null,"abstract":"The International Union of Pure and Applied Chemistry (IUPAC) was formed in 1919 by chemists from indus- try and academia (1). Over nearly nine decades the Union has succeeded in fostering worldwide communications in the chemical sciences and in uniting chemistry - academic, industrial and government - in a common language. As one of the results of the Union, IUPAC names nowadays serve as a commonly agreed text representation of chemical structures in patents, publications and databases. In public databases of chemical compounds, like PubChem with more than 12 million entries, chemical structures are identified by default using their IUPAC names (2). We report a very fast linguistic method to extract the implicit information contained in IUPAC names to statistically predict pharmacologically relevant proper- ties. This provides an efficient annotation tool that can be used to assess the likelihood of a given compound as a drug candidate and renders the entire chemical literature a searchable database for virtual screening experiments and data mining.","PeriodicalId":148247,"journal":{"name":"The Open Applied Informatics Journal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESTHER: A “R Package” Implementing a Novel Approach to Bidimen-sional Display of Multidimensional Binary Data","authors":"G. Cardinali, Livio Antonielli, Paolo Rellini, F. Fatichenti","doi":"10.2174/1874136300701010020","DOIUrl":"https://doi.org/10.2174/1874136300701010020","url":null,"abstract":"The R package ESTHER implements two novel algorithms designed to dispose in a reduced space multidimen- sional objects defined by binary descriptors. The approach is to assign discrete and fix positions to all possible combina- tions obtained with the descriptors employed. One of the two algorithms, called clock, position objects on a circle at regu- lar intervals, whereas the other, star, maintains the angular position as in clock, but defines the distance of the object from the center of the circle proportionally to the number of descriptor in state\"1\". Comparisons with Principal Coordinate Analysis (PCoA) showed that the three methods perform differently according to the number of objects and descriptors and to the distance method employed to carry out the PCoA. The algorithm clock produced the best object clustering in a validation carried out with a matrix generated by molecular fingerprint of yeast isolates. An additional problem, which is at the origin of the algo- rithm proposed in this paper, is the impossibility to show in the PCoA graph the proportion of the descriptors in use. In fact, n binary variables can describe a maximum of 2n possi- ble objects, although the number of biological objects stud- ied is normally well below 2n. In normal conditions, all ob- jects (excluding the repetitions) are represented by one of the 2n combinations obtained with the n descriptors in use, meaning that only a few combinations are actually present in the matrix. The relationships among these few combinations and their positioning within the complex of all possible combinations are further aims of the present algorithms, named ESTHER after the ancient Persian word, meaning star, for some similarity between the point scattering and the stylized depiction of the star. METHODS General Presentation of the Algorithms Included in ESTHER ESTHER includes seven functions, two of them (clock and star) are designed to position in a binary space object described by several descriptors, both of them include an option to evaluate the quality of the point scattering in the bidimensional graph. The other four functions are auxiliary; one, import, is designed to directly import binary matrices, three to produce binary matrices: fullmat generates matrices with all 2n combinations, partmat yields only a defined por- tion of all combinations and finally ranmat generates random combinations with the desired number of objects and de- scriptors. The function shepard returns a double plot with the Shepard diagrams of ESTHER and PCoA.","PeriodicalId":148247,"journal":{"name":"The Open Applied Informatics Journal","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122609269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Iterative Nonlinear Regression Method for Microarray Data Normalization","authors":"J. Xuan, Y. Wang, R. Clarke, E. Hoffman","doi":"10.2174/1874136300701010011","DOIUrl":"https://doi.org/10.2174/1874136300701010011","url":null,"abstract":"Normalization is a prerequisite for almost all follow-up steps in microarray data analysis. Accurate normaliza- tion across different experiments and phenotypes assures a common base for comparative yet quantitative studies using gene expression data. In this paper, we report a novel normalization approach, namely iterative nonlinear regression (INR) method, which exploits concurrent identification of invariantly expressed genes (IEGs) and implementation of nonlinear regression normalization. The INR scheme features an iterative process that performs the following two steps alterna- tively: (1) selection of IEGs and (2) estimation of nonlinear regression function for normalization. We demonstrate the principle and performance of the INR approach on two real microarray data sets. As compared to major peer methods (e.g., linear regression method, Loess method and iterative ranking method), INR method shows an improved perform- ance in achieving low expression variance across replicates and excellent fold-change preservation for differently ex- pressed genes.","PeriodicalId":148247,"journal":{"name":"The Open Applied Informatics Journal","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics Study of Functional Associations Observed in Multiple Sources of Human Genome Data","authors":"Seungwoo Hwang, I. B. Kuznetsov","doi":"10.2174/1874136300701010001","DOIUrl":"https://doi.org/10.2174/1874136300701010001","url":null,"abstract":"High-throughput genome analysis techniques produce the ever increasing number of heterogeneous large-scale datasets. Studies of these mutually complementary sources of data promise insights into a global picture of the living cell. Here, we present a simple bioinformatics methodology for the analysis of multiple heterogeneous sources of 'omic' (ge- nomic, proteomic, etc) data. We apply this methodology to study associations among four types of human 'omic' data: protein-protein interactions, gene expression, transcription factor binding sites, and functional pathways. The results of our study indicate that the proposed approach can be used to identify and rank statistically significant functional associa- tions among genes. We show that combinations of multiple data types provide additional insights into the properties of functional pathways. The proposed methodology can also be used as a quantitative procedure for evaluating the quality of 'omic' datasets.","PeriodicalId":148247,"journal":{"name":"The Open Applied Informatics Journal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132785023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Silico Evidence of the Relationship Between miRNAs and siRNAs","authors":"Ludovica Montanucci, P. Fariselli, P. Martelli, I. Rossi, R. Casadio","doi":"10.2174/1874136300802010009","DOIUrl":"https://doi.org/10.2174/1874136300802010009","url":null,"abstract":"Both short interfering RNAs (siRNAs) and microRNAs (miRNAs) mediate the repression of specific sequences of mRNA through the RNA interference pathway. In the last years several experiments have supported the hypothesis that siRNAs and miRNAs may be functionally interchangeable, at least in cultured cells. In this work we verify that this hy- pothesis is also supported by a computational evidence. We show that a method specifically trained to predict the activity of the exogenous siRNAs assigns a high silencing level to experimentally determined human miRNAs. This result not only supports the idea of the biological interchangeability of siRNAs and miRNAs but indicates that it is possible to use computational tools developed using synthetic small interference RNAs to study endogenous miRNAs.","PeriodicalId":148247,"journal":{"name":"The Open Applied Informatics Journal","volume":"159 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114124776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}