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Results for “biological data”

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At least 73 records · Page 4Linked to original sources
PubMed1993

Analytical and biological data of banked samples--a requirement to interpret accumulation and leaching of pollutants in biological specimens.

The accumulation of organic and inorganic pollutants in biological specimens depends, in many cases, not only on the atmospheric deposition or immission and contamination of the biotop, but also on the dynamic properties of the indicator. Thus the analytical data assume properties of process variables. In order to compare them in relation to space and time, it is often practical to refer them to defined conditions of the indicator (stationary phases, time cycles of low dynamics). If the accumulation varies consistently under identical conditions of contamination and if clear functional relations exist to the features of the indicator, for instance age, weight or ecological processes, it is necessary to normalize the analytical data, i.e. to refer them to a condition of the indicator which is defined meaningfully with regard to the ecology. Interactions with other inputs of substances may result in a differentiation of accumulation and leaching processes which is more or less specific for each element. Such knowledge is important for specifying the standard operation procedures and the evaluation as well as interpretation of analytic data, in particular, in the clarification of the ecotoxicological relations between the pollutant accumulation and the reaction of populations and ecosystems.

Animals↗
PubMed2000

Mining of biological data II: assessing data structure and class homogeneity by cluster analysis.

An important step in data analysis is class assignment which is usually done on the basis of a macroscopic phenotypic or bioprocess characteristic, such as high vs low growth, healthy vs diseased state, or high vs. low productivity. Unfortunately, such an assignment may lump together samples, which when derived from a more detailed phenotypic or bioprocess description are dissimilar, giving rise to models of lower quality and predictive power. In this paper we present a clustering algorithm for data preprocessing which involves the identification of fundamentally similar lots on the basis of the extent of similarity among the system variables. The algorithm combines aspects of cluster analysis and principal component analysis by applying agglomerative clustering methods to the first principal component of the system data matrix. As part of a rational strategy for developing empirical models, this technique selects lots (samples) which are most appropriate for inclusion in a training set by analyzing multivariate data homogeneity. Samples with similar data structures are identified and grouped together into distinct clusters. This knowledge is used in the formation of potential training sets. Additionally, this technique can identify atypical lots, i.e., samples that are not simply outliers but exhibit the general properties of one class but have been given the assignment of the other. The method is presented along with examples from its application to fermentation data sets.

Algorithms↗
PubMed2000

Mining of biological data I: identifying discriminating features via mean hypothesis testing.

Large volumes of data are routinely collected during bioprocess operations and, more recently, in basic biological research using genomics-based technologies. While these data often lack sufficient detail to be used for mechanism identification, it is possible that the underlying mechanisms affecting cell phenotype or process outcome are reflected as specific patterns in the overall or temporal sensor logs. This raises the possibility of identifying outcome-specific fingerprints that can be used for process or phenotype classification and the identification of discriminating characteristics, such as specific genes or process variables. The aim of this work is to provide a systematic approach to identifying and modeling patterns in historical records and using this information for process classification. This approach differs from others in that emphasis is placed on analyzing the data structure first and thereby extracting potentially relevant features prior to model creation. The initial step in this overall approach is to first identify the discriminating features of the relevant measurements and time windows, which can then be subsequently used to discriminate among different classes of process behavior. This is achieved via a mean hypothesis testing algorithm. Next, the homogeneity of the multivariate data in each class is explored via a novel cluster analysis technique called PC1 Time Series Clustering to ensure that the data subsets used accurately reflect the variability displayed in the historical records. This will be the topic of the second paper in this series. We present here the method for identifying discriminating features in data via mean hypothesis testing along with results from the analysis of case studies from industrial fermentations

Algorithms↗
PubMed2002

Molecular mechanisms of apoptosis and chemosensitivity to platinum and paclitaxel in ovarian cancer: biological data and clinical implications.

Apoptosis is a genetically regulated biological process that plays a major role in chemotherapy-induced tumor cell killing. It may be triggered by two major intracellular signaling cascades, the mitochondrial pathway and the death receptor pathway, both leading to caspase activation and cleavage of specific cellular substrates. The p53 gene is involved in the regulation of apoptosis. Caspase activation following wild-type p53 induction is associated with the release of the apoptogenic factors cytochrome c and Smac/DIABLO from the mitochondria, that is in turn controlled by the pro-apoptotic and anti-apoptotic Bcl-2 family proteins. In ovarian cancer p53 status is a strong predictor of response to platinum-based chemotherapy. Patients whose tumors have p53 mutations experience a lower chance of achieving a complete response following platinum-based regimens when compared to patients without p53 mutations. Conversely, experimental and clinical data seem to show that paclitaxel enhances apoptosis through a p53-independent pathway, that probably involves the Bax gene. Whereas patients with wild-type p53 tumors have a good chance to respond to platinum, patients with mutant p53 tumors may have a clinical benefit from the addition of paclitaxel to platinum-based chemotherapy. Therefore determining p53 status can be useful in predicting therapeutic response to specific drugs. Moreover the understanding of cellular mechanisms regulating apoptosis might offer a strong rationale for the combination of chemotherapy with other biological treatments.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1996

The stage-conversion time of Toxoplasma gondii: interpretation of chemical-biologic data out of parasitologic or host context.

Several published biologic times for the stage conversion of Toxoplasma gondii from tachyzoite to bradyzoite in mice are critically examined. There are several reports of 3 days and many of longer times. Possible errors, related to delay from dissemination of the infection, are pointed out. The time to the appearance of the 36-kDa surface antigen, sometimes used for the diagnosis of bradyzoites, should be compared with the biologic attribute of bradyzoites, inducing the short prepatent period in cats. It is recalled that the development of acid pepsin resistance is not exclusively correlated with the biologic definition of bradyzoites. Reactivation of toxoplasmosis in mice does not necessarily follow the administration of a corticosteroid. The daily dose, the type of corticosteroid, its solubility, whether alcohol or ester, the route of administration, the host, and, probably, other factors are important in giving rise to sufficient immunosuppression for reactivation to occur. The plea is made to examine biologic measurements in the context of parasitologic and host factors.

Animals↗
PubMed2002

Biological data becomes computer literate: new advances in bioinformatics.

Bioinformatics is an art and science concerned with the use of computing in biological research areas such as genomics, transcriptomics, proteomics, genetics, and evolution. This review paints a broad picture of bioinformatics, drawing examples from genomic sequencing and microarray analysis. I highlight the role of bioinformatics at multiple points along the path from high-tech data generation to biological discovery.

Computational Biology↗
PubMed1995

On the use of molecular biology data in occupational and environmental epidemiology.

Major advances in molecular biology during recent years have helped to identify genes and enzymes that play an important role in many diseases. While not ruling out traditional forms of epidemiology, the application of epidemiologic study designs in molecular biological studies may provide possibilities of disclosing disease mechanisms at the gene level and identifying more specific indicators of disease. Data on adducts to DNA or protein usually lead to cross-sectional studies or cohorts. Genetically determined susceptibility or mutations in tumor cells might be available and possibly reflect etiologically important determinants. The case-control design is applicable and cost-effective for the analysis of such data.

Biomarkers↗
PubMed2005

The Mouse Tumor Biology Database: integrated access to mouse cancer biology data.

Mice have long been used as models for the study of human cancer. The National Cancer Institute has included among its research areas of extraordinary opportunity the development of new mouse genetic models of human cancer and the exploration of cancer imaging as a research tool. Because of the volume and interconnectedness of relevant data, the creation and maintenance of bioinformatics resources of mouse tumor biology is necessary to facilitate current and future cancer research. The Mouse Tumor Biology (MTB) Database provides electronic access to data generated through the study of spontaneous and induced tumors in genetically defined mice (inbred, hybrid, spontaneous and induced mutant, and genetically engineered strains of mice).

Animals↗
PubMed2006

Path-based systems to guide scientists in the maze of biological data sources.

Fueled by novel technologies capable of producing massive amounts of data for a single experiment, scientists are faced with an explosion of information which must be rapidly analyzed and combined with other data to form hypotheses and create knowledge. Today, numerous biological questions can be answered without entering a wet lab. Scientific protocols designed to answer these questions can be run entirely on a computer. Biological resources are often complementary, focused on different objects and reflecting various experts' points of view. Exploiting the richness and diversity of these resources is crucial for scientists. However, with the increase of resources, scientists have to face the problem of selecting sources and tools when interpreting their data. In this paper, we analyze the way in which biologists express and implement scientific protocols, and we identify the requirements for a system which can guide scientists in constructing protocols to answer new biological questions. We present two such systems, BioNavigation and BioGuide dedicated to help scientists select resources by following suitable paths within the growing network of interconnected biological resources.

Cell Physiological Phenomena↗
PubMed2001

Exploiting big biology: integrating large-scale biological data for function inference.

The amount of data produced by molecular biologists is growing at an exponential rate. Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature. Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers. Therefore, much effort is being focused on integrating these large data sets and combining them with all available functional data to draw inferences about the functions of uncharacterised genes. This review discusses the most pertinent functional data for genome-wide functional inference and describes several methods by which these disparate data types are being integrated.

Data Collection↗