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PubMed1998

Different derivations of knowledge-based potentials and analysis of their robustness and context-dependent predictive power.

The possibility of defining effective potentials from known protein structures, which are sufficiently accurate to be used for protein-structure-prediction purposes, is investigated. Three types of distance potentials and three types of backbone torsion potentials are defined, based on propensities of amino acid pairs to be separated by a given spatial distance or to be associated to a backbone torsion angle domain. Their differences reside in the way the physical correlations between the amino acids and the conformational states are extracted from the bulk interactions due to the presence of many residues in a protein. For the distance potentials, a physical meaning can be associated to the different definitions, given that some of the potentials favor hydrophobic interactions and others favor interactions between oppositely charged residues. The performance of the different torsion and distance potentials in structure prediction procedures, in particular native-fold recognition and evaluation of protein stability changes upon point mutations, is analyzed. It appears to differ according to the specific proteins and protein environments. In particular, one of the distance potentials performs better than the others for membrane proteins and in protein regions involving charged residues, but less well in other protein regions. Furthermore, the dependence of the potentials on the characteristics of the proteins from which they are derived is analyzed. It is shown that the dependence of the potentials on the length, amino acid composition and secondary-structure content of the proteins from the dataset is either very limited or rather strong, according to the type of potential. The results obtained suggest that the main problem limiting the performance of database-derived potentials is their lack of universality: each potential describes with satisfactory accuracy only the interactions present in certain protein environments.

Algorithms↗

PubMedDate not supplied

Comparison of MLR, PLS and GA-MLR in QSAR analysis.

The use of the internet has evolved in quantitative structure-activity relationship (QSAR) over the past decade with the development of web based activities like the availability of numerous public domain software tools for descriptor calculation and chemometric toolboxes. The importance of chemometrics in QSAR has accelerated in recent years for processing the enormous amount of information in form of predictive mathematical models for large datasets of molecules. With the availability of huge numbers of physicochemical and structural parameters, variable selection became crucial in deriving interpretable and predictive QSAR models. Among several approaches to address this problem, the principle component regression (PCR) and partial least squares (PLS) analyses provide highly predictive QSAR models but being more abstract, they are difficult to understand and interpret. Genetic algorithm (GA) is a stochastic method well suited to the problem of variable selection and to solve optimization problems. Consequently the hybrid approach (GA-MLR) combining GA with multiple linear regression (MLR) may be useful in derivation of highly predictive and interpretable QSAR models. In view of the above, a comparative study of stepwise-MLR, PLS and GA-MLR in deriving QSAR models for datasets of alpha1-adrenoreceptor antagonists and beta3-adrenoreceptor agonists has been carried out using the public domain software Dragon for computing descriptors and free Matlab codes for data modeling.

Adrenergic alpha-Agonists↗

PubMed2006

Ribosomal RNA as molecular barcodes: a simple correlation analysis without sequence alignment.

MOTIVATION: We explored the feasibility of using unaligned rRNA gene sequences as DNA barcodes, based on correlation analysis of composition vectors (CVs) derived from nucleotide strings. We tested this method with seven rRNA (including 12, 16, 18, 26 and 28S) datasets from a wide variety of organisms (from archaea to tetrapods) at taxonomic levels ranging from class to species. RESULT: Our results indicate that grouping of taxa based on CV analysis is always in good agreement with the phylogenetic trees generated by traditional approaches, although in some cases the relationships among the higher systemic groups may differ. The effectiveness of our analysis might be related to the length and divergence among sequences in a dataset. Nevertheless, the correct grouping of sequences and accurate assignment of unknown taxa make our analysis a reliable and convenient approach in analyzing unaligned sequence datasets of various rRNAs for barcoding purposes. AVAILABILITY: The newly designed software (CVTree 1.0) is publicly available at the Composition Vector Tree (CVTree) web server http://cvtree.cbi.pku.edu.cn.

Algorithms↗

PubMed2001

Statistics review 1: presenting and summarising data.

The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data.

Analysis of Variance↗

PubMed2000

Human BAC ends quality assessment and sequence analyses.

End sequences from bacterial artificial chromosomes (BACs) provide highly specific sequence markers in large-scale sequencing projects. To date, we have generated >300,000 end sequences from >186,000 human BAC clones with an average read length of >460 bp for a total of 141 Mb covering approximately 4.7% of the genome. Over 60% of the clones have BAC end sequences (BESs) from both ends representing more than fivefold coverage of the human genome by the paired-end clones. Our quality assessments and sequence analyses indicate that BESs from human BAC libraries developed at The California Institute of Technology (CalTech) and Roswell Park Cancer Institute have similar properties. The analyses have highlighted differences in insert size for different segments of the CalTech library. Problems with the fidelity of tracking of sequence data back to physical clones have been observed in some subsets of the overall BES dataset. The annotation results of BESs for the contents of available genomic sequences, sequence tagged sites, expressed sequence tags, protein encoding regions, and repeats indicate that this resource will be valuable in many areas of genome research.

Chromosome Mapping↗

PubMed2005

[Multislice CT urography Aspects for technical management and clinical application].

The introduction of multislice computed tomography with its well-known advantages has made it possible to visualize the entire urinary tract with thin collimation during a breath-holding phase. CT data acquisition during urographic contrast enhancement for contiguous imaging of the entire upper urinary tract is termed "multislice CT urography" (MSCTU). Multiplanar reconstructions, maximum intensity projections, and average intensity projections can be rendered from the volume datasets to view the urogenital tract. MSCTU will play an important role in the future of modern uroradiology. This article describes the technical aspects involved in the course of the MSCTU examination and identifies additional potential indications for clinical application.

Anatomy, Cross-Sectional↗

PubMed2004

A comparison of three different sources of data in assessing the frequencies of adverse reactions to amiodarone.

AIMS: To compare the frequencies of adverse drug reactions (ADRs) to amiodarone from three separate datasets: (i) a meta-analysis of clinical trials, (ii) spontaneous reports published in medical journals, and (iii) spontaneous reports sent to the World Health Organization (WHO). METHODS: We classified the ADRs into eight categories, based on the site involved, and built a rank order of the ADRs by category (from most to least commonly reported) for each dataset. We also calculated the relative proportions for all eight ADR categories within each dataset, in order to be able to compare the distributions of ADR frequencies: we assigned an index value of 1.0 to the frequency of respiratory toxicity in each set and calculated values for the other ADRs relative to respiratory toxicity. RESULTS: Thyroid disorders were the most commonly reported ADRs in the WHO dataset. In contrast, published case reports showed a preponderance of respiratory disorders, while in the meta-analysis cardiac conduction problems were the most frequent. The rank orders of ADRs differed among the three datasets, as did the index values of specific ADR categories with respect to the respiratory category. CONCLUSIONS: The distributions of ADR rank order and relative frequencies are dissimilar among the three datasets, as each dataset compiles information in a different way. Nevertheless, each dataset has its own specific strengths, and all three should be used together in obtaining a complete picture of a drug's safety profile. Important therapeutic and regulatory decisions should not simply be based on one source of data.

Adverse Drug Reaction Reporting Systems↗

PubMed2003

Multiscale analysis of long time-series medical databases.

Data mining in time-series medical databases has been receiving considerable attention since it provides a way of revealing useful information hidden in the database; for example relationships between the temporal course of examination results and onset time of diseases. This paper presents a new method for finding similar patterns in temporal sequences based on multiscale matching. Multiscale matching enables us the cross-scale comparison of sequences, namely, it enable us to compare temporal patterns by partially changing observation scales. We examined the usefulness of the method on the chronic hepatitis dataset and found some interesting patterns. On GPT sequences, we found patterns that may represent the effectiveness of interferon (IFN) treatment. On platelet count sequences, we found that, if IFN treatment was ineffective, platelet count kept decreasing following the progress of liver fibrosis, while it started increasing if the treatment was effective.

Antiviral Agents↗

PubMed2007

Structural analysis and prediction of protein mutant stability using distance and torsion potentials: role of secondary structure and solvent accessibility.

Analyzing the factors behind protein stability is a key research topic in molecular biology, and has direct implications on protein structure prediction and protein-protein interactions. We have analyzed protein stability upon point mutations using a distance-dependant pair potential representing mainly through-space interactions, and torsion angle potential representing mainly neighboring effects as a basic statistical mechanical setup for the analysis. The synergetic effect of accessible surface area and secondary structure preferences was used as a classifier for the potentials. In addition, short-, medium-, and long-range interactions of the protein environment were also analyzed. Two datasets of point mutations were taken for the comparison of theoretically predicted stabilizing energy values with experimental DeltaDeltaG and DeltaDeltaGH(2)O from thermal and chemical denaturation experiments. These include 1538 and 1603 mutations, respectively, and contain 101 proteins that share a wide range of sequence identity. The resulting force fields were carefully evaluated with different statistical tests. Results show a maximum correlation of 0.87 with a standard error of 0.71 kcal/mol between predicted and measured DeltaDeltaG values and a prediction accuracy of 85.3% (stabilizing or destabilizing) for all mutations together. A correlation of 0.77 (more than 80% prediction accuracy with a standard error of 0.95 kcal/mol) each for the test dataset of split-sample validation and fivefold crossvalidation was obtained and a correlation of 0.70 (77.4% prediction accuracy with a standard error of 1.17 kcal/mol) was shown by the jackknife test. The same model was implemented, and the results were analyzed for mutations with DeltaDeltaGH(2)O. A correlation of 0.78 (standard error 0.96 kcal/mol) was observed with a prediction efficiency of 84.65%. This model can be used for the future prediction of protein structural stability together with various experimental techniques.

Databases, Protein↗

PubMed1989

Genetic analysis of IDDM: the GAW5 multiplex family dataset.

In a collaborative effort by 12 centers from Europe and North America, data were assembled from 94 multiplex families with insulin-dependent diabetes mellitus (IDDM) for analysis of genetic and other factors of possible etiological importance. The dataset contains information on the following genetic markers: HLA-DR beta and -DQ beta restriction fragment length polymorphisms (RFLPs), three RFLPs detected with two probes that map 5' to the insulin gene, the serologically defined HLA loci, and the immunoglobulin allotypes. Data also were included for auto-antibodies to insulin and pancreatic islet cells as possible indicators of pathogenesis and for antibodies to certain viruses that have been implicated as "triggering" agents in IDDM. Medical history of family members was obtained by means of a uniform questionnaire. Identical copies of the dataset were distributed to anyone wishing to participate in the analysis for the IDDM component of GAW5. The multiplex IDDM family dataset is now available on request for further analysis.

Adolescent↗

PubMed2026

A Guide for Exploring Pleiotropic Associations in Genome-Wide Association Studies Using Summary Statistics.

Genome-wide association studies (GWAS) have shown that pleiotropy, whereby a single genetic variant or gene influences multiple traits, is common in complex human diseases. Detecting cross-phenotype associations from GWAS summary statistics remains challenging because of small effect sizes, extensive multiple testing, heterogeneous effects, and possible differences in effect direction across traits. Methods that jointly analyze multiple traits can improve the ability to detect pleiotropic signals while retaining the practical advantages of summary statistic-based analyses. Although a range of statistical approaches has been developed for this purpose, practical guidance on their application, assumptions, and interpretation remains limited. This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets. We also highlight the importance of accounting for effect heterogeneity, correlation, and biological group structure at the gene and pathway levels in the detection and interpretation of pleiotropic association signals.

Genome-Wide Association Study↗

PubMed2005

Data-partitioning using the Hilbert space filling curves: effect on the speed of convergence of Fuzzy ARTMAP for large database problems.

The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having to a priori specify its network size. On the other hand, it has the undesirable side effect that large databases might produce a large network size (node proliferation) that can dramatically slow down the training speed of the algorithm. To address the slow convergence speed of Fuzzy ARTMAP for large database problems, we propose the use of space-filling curves, specifically the Hilbert space-filling curves (HSFC). Hilbert space-filling curves allow us to divide the problem into smaller sub-problems, each focusing on a smaller than the original dataset. For learning each partition of data, a different Fuzzy ARTMAP network is used. Through this divide-and-conquer approach we are avoiding the node proliferation problem, and consequently we speedup Fuzzy ARTMAP's training. Results have been produced for a two-class, 16-dimensional Gaussian data, and on the Forest database, available at the UCI repository. Our results indicate that the Hilbert space-filling curve approach reduces the time that it takes to train Fuzzy ARTMAP without affecting the generalization performance attained by Fuzzy ARTMAP trained on the original large dataset. Given that the resulting smaller datasets that the HSFC approach produces can independently be learned by different Fuzzy ARTMAP networks, we have also implemented and tested a parallel implementation of this approach on a Beowulf cluster of workstations that further speeds up Fuzzy ARTMAP's convergence to a solution for large database problems.

Algorithms↗

PubMed2002

Prediction of Golgi Type II membrane proteins based on their transmembrane domains.

MOTIVATION: A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches. RESULTS: A new method has been developed to predict Golgi membrane proteins based on their transmembrane domains. To establish the prediction procedure, we took the hydrophobicity values and frequencies of different residues within the transmembrane domains into consideration. A simple linear discriminant function was developed with a small number of parameters derived from a dataset of Type II transmembrane proteins of known localization. This can discriminate between proteins destined for Golgi apparatus or other locations (post-Golgi) with a success rate of 89.3% or 85.2%, respectively on our redundancy-reduced data sets. AVAILABILITY: See http://microarray.imb.uq.edu.au/golgi/

Algorithms↗

PubMed2004

The MIPS mammalian protein-protein interaction database.

SUMMARY: The MIPS mammalian protein-protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. We provide the full dataset for download and a flexible and powerful web interface for users with various requirements.

Animals↗

PubMed2003

The effect of concurvity in generalized additive models linking mortality to ambient particulate matter.

In recent years, a number of studies have applied generalized additive models to time series data to estimate associations between exposure to air pollution and cardiorespiratory morbidity and mortality. If concurvity, the nonparametric analogue of multicollinearity, is present in the data, statistical software such as S-plus can seriously underestimate the variance of fitted model parameters, leading to significance tests with inflated type 1 error. This paper uses computer simulation and analyses of actual epidemiologic data to explore this underestimation of standard errors. We provide a method for assessing concurvity in data and an alternate class of models that is unaffected by concurvity. We argue that some degree of concurvity is likely to be present in all epidemiologic time series datasets and we explore through the use of meta-analysis the possible impact of concurvity on the existing body of work relating ambient levels of sulfate particles to mortality.

Air Pollutants↗

PubMed2003

Internal correspondence analysis of codon and amino-acid usage in thermophilic bacteria.

Starting from two datasets of codon usage in coding sequences from mesophilic and thermophilic bacteria, we used internal correspondence analysis to study the variability of codon usage within and between species, and within and between amino acids. The first dataset included 18,958,458 codons from 58,482 coding sequences from completely sequenced genomes of 25 species, along with 6,793,581 dinucleotides from 21,876 intergenic spaces. The second dataset, with partially sequenced genomes, included 97,095,873 codons from 293 bacterial species. Results were consistent between the two datasets. The trend for the amino-acid composition of thermophilic proteins was found to be under the control of a pressure at the nucleic acid level, not a selection at the protein level. This effect was not present in intergenic spaces, ruling out a pressure at the DNA level. The pattern at the mRNA level was more complex than a simple purine enrichment of the sense strand of coding sequences. Outliers in the partial genome dataset introduced a note of caution about the interpretation of temperature as the direct determinant of the trend observed in thermophiles. The surprising lack of selection on the amino-acid content of thermophilic proteins suggests that the amino-acid repertoire was set up in a hot environment.

Bacteria↗

PubMed2003

Coverage of patient safety terms in the UMLS metathesaurus.

The integration and large-scale analyses of medical error databases would be greatly facilitated by the use of a standard terminology. We investigated the availability in the UMLS metathesaurus of concepts that are required for coding patient safety data. Terms from three proprietary patient safety terminologies were mapped to the concepts in UMLS by an automated mapping program developed by us. From these candidate mappings, the concept that matched its corresponding term was selected manually. The reliability of the mapping procedure was verified by manually searching for terms in the UMLS Knowledge Source Server. Matching concepts in UMLS were identified for less than 27% of the terms in the study dataset. The matching rates of terms that describe the type of error and the causes of errors were even lower. The lack of such terms in the existing standard terminologies underscores the need for development of a standard patient safety terminology.

Humans↗

PubMed2005

Large-scale morphometric analysis of neuroanatomy and neuropathology.

Brain imaging research with MRI spans a wide area, covering both structure and function, and ranging from basic research through clinical research to drug design and clinical trials. In recent years there has been a trend towards the collection of very large MRI databases which can allow for the detection of very small group-dependent effects. However, the logistical challenges of analysing such large datasets presents new challenges. This paper describes the "pipeline" framework developed at the Montreal Neurological Institute for the fully automated morphometric analysis of large brain imaging databases. The potential use of these techniques is illustrated by examples of their applications in multiple sclerosis, Alzheimer's disease, and pediatric development.

Alzheimer Disease↗