Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.
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The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA. (+info)
Speech recognition interface to a hospital information system using a self-designed visual basic program: initial experience.
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Speech recognition (SR) in the radiology department setting is viewed as a method of decreasing overhead expenses by reducing or eliminating transcription services and improving care by reducing report turnaround times incurred by transcription backlogs. The purpose of this study was to show the ability to integrate off-the-shelf speech recognition software into a Hospital Information System in 3 types of military medical facilities using the Windows programming language Visual Basic 6.0 (Microsoft, Redmond, WA). Report turnaround times and costs were calculated for a medium-sized medical teaching facility, a medium-sized nonteaching facility, and a medical clinic. Results of speech recognition versus contract transcription services were assessed between July and December, 2000. In the teaching facility, 2042 reports were dictated on 2 computers equipped with the speech recognition program, saving a total of US dollars 3319 in transcription costs. Turnaround times were calculated for 4 first-year radiology residents in 4 imaging categories. Despite requiring 2 separate electronic signatures, we achieved an average reduction in turnaround time from 15.7 hours to 4.7 hours. In the nonteaching facility, 26600 reports were dictated with average turnaround time improving from 89 hours for transcription to 19 hours for speech recognition saving US dollars 45500 over the same 6 months. The medical clinic generated 5109 reports for a cost savings of US dollars 10650. Total cost to implement this speech recognition was approximately US dollars 3000 per workstation, mostly for hardware. It is possible to design and implement an affordable speech recognition system without a large-scale expensive commercial solution. (+info)
The metric space of proteins-comparative study of clustering algorithms.
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MOTIVATION: A large fraction of biological research concentrates on individual proteins and on small families of proteins. One of the current major challenges in bioinformatics is to extend our knowledge to very large sets of proteins. Several major projects have tackled this problem. Such undertakings usually start with a process that clusters all known proteins or large subsets of this space. Some work in this area is carried out automatically, while other attempts incorporate expert advice and annotation. RESULTS: We propose a novel technique that automatically clusters protein sequences. We consider all proteins in SWISSPROT, and carry out an all-against-all BLAST similarity test among them. With this similarity measure in hand we proceed to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a hierarchy of clusters of varying degrees of granularity. Here we compare the clusters that result from alternative merging rules, and validate the results against InterPro. Our preliminary results show that clusters that are consistent with several rather than a single merging rule tend to comply with InterPro annotation. This is an affirmation of the view that the protein space consists of families that differ markedly in their evolutionary conservation. (+info)
Beyond tandem repeats: complex pattern structures and distant regions of similarity.
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MOTIVATION: Tandem repeats (TRs) are associated with human disease, play a role in evolution and are important in regulatory processes. Despite their importance, locating and characterizing these patterns within anonymous DNA sequences remains a challenge. In part, the difficulty is due to imperfect conservation of patterns and complex pattern structures. We study recognition algorithms for two complex pattern structures: variable length tandem repeats (VLTRs) and multi-period tandem repeats (MPTRs). RESULTS: We extend previous algorithmic research to a class of regular tandem repeats (RegTRs). We formally define RegTRs, as well as two important subclasses: VLTRs and MPTRs. We present algorithms for identification of TRs in these classes. Furthermore, our algorithms identify degenerate VLTRs and MPTRs: repeats containing substitutions, insertions and deletions. To illustrate our work, we present results of our analysis for two difficult regions in cattle and human data which reflect practical occurrences of these subclasses in GenBank sequence data. In addition, we show the applicability of our algorithmic techniques for identifying Alu sequences, gene clusters and other distant regions of similarity. We illustrate this with an example from yeast chromosome I. (+info)
Inferring sub-cellular localization through automated lexical analysis.
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MOTIVATION: The SWISS-PROT sequence database contains keywords of functional annotations for many proteins. In contrast, information about the sub-cellular localization is available for only a few proteins. Experts can often infer localization from keywords describing protein function. We developed LOCkey, a fully automated method for lexical analysis of SWISS-PROT keywords that assigns sub-cellular localization. With the rapid growth in sequence data, the biochemical characterisation of sequences has been falling behind. Our method may be a useful tool for supplementing functional information already automatically available. RESULTS: The method reached a level of more than 82% accuracy in a full cross-validation test. Due to a lack of functional annotations, we could infer localization for fewer than half of all proteins in SWISS-PROT. We applied LOCkey to annotate five entirely sequenced proteomes, namely Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Arabidopsis thaliana (plant) and a subset of all human proteins. LOCkey found about 8000 new annotations of sub-cellular localization for these eukaryotes. (+info)
Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns.
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MOTIVATION: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila. RESULTS: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm. (+info)
A tree kernel to analyse phylogenetic profiles.
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MOTIVATION: The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often 'similar' or at least 'related' to each other. The question we address in this paper is the following: how to measure the 'similarity' between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods? RESULTS: We show how the profiles can be mapped to a high-dimensional vector space which incorporates evolutionarily relevant information, and we provide an algorithm to compute efficiently the inner product in that space, which we call the tree kernel. The tree kernel can be used by any kernel-based analysis method for classification or data mining of phylogenetic profiles. As an application a Support Vector Machine (SVM) trained to predict the functional class of a gene from its phylogenetic profile is shown to perform better with the tree kernel than with a naive kernel that does not include any information about the phylogenetic relationships among species. Moreover a kernel principal component analysis (KPCA) of the phylogenetic profiles illustrates the sensitivity of the tree kernel to evolutionarily relevant variations. (+info)
Finding composite regulatory patterns in DNA sequences.
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Pattern discovery in unaligned DNA sequences is a fundamental problem in computational biology with important applications in finding regulatory signals. Current approaches to pattern discovery focus on monad patterns that correspond to relatively short contiguous strings. However, many of the actual regulatory signals are composite patterns that are groups of monad patterns that occur near each other. A difficulty in discovering composite patterns is that one or both of the component monad patterns in the group may be 'too weak'. Since the traditional monad-based motif finding algorithms usually output one (or a few) high scoring patterns, they often fail to find composite regulatory signals consisting of weak monad parts. In this paper, we present a MITRA (MIsmatch TRee Algorithm) approach for discovering composite signals. We demonstrate that MITRA performs well for both monad and composite patterns by presenting experiments over biological and synthetic data. (+info)