1,721,078 research outputs found
A Bayesian Hierarchical Model Approach to Summarize Expression values of Exon and Affymetrix arrays
No full text
A Bayesian Hierarchical Model Approach to Summarize Expression values of Exon and Affymetrix arrays
No full textBayesian variable selection method for modeling dose-response microarray data under simple order restrictions
No full textThe aim of the analysis presented below is to investigate dose-response relationship in a microarray setting. Typically, in dose-response experiments the outcome of interest is measured in several (increasing)
dose levels and the goal of the analysis is to establish the relationship which represents the dependency of
the response on dose.
Bayesian modeling of dose-response microarray data offers the possibility to jointly establish the dose
response relationships between gene expression and increasing doses of therapeutic compound and to
determine the nature of the relationships wherever it exists.
The Bayesian variable selection approach provides a modeling framework that allows estimating the posterior probabilities for a given set of pre-specified models and in particular the posterior probability of
the model estimated under the null hypothesis of no dose effect. The posterior probabilities are used for
multiplicity adjustment using the direct posterior probability approach
Bayesian variable selection method for modeling dose-response microarray data under simple order restrictions
No full textThe aim of the analysis presented below is to investigate dose-response relationship in a microarray setting. Typically, in dose-response experiments the outcome of interest is measured in several (increasing)
dose levels and the goal of the analysis is to establish the relationship which represents the dependency of
the response on dose.
Bayesian modeling of dose-response microarray data offers the possibility to jointly establish the dose
response relationships between gene expression and increasing doses of therapeutic compound and to
determine the nature of the relationships wherever it exists.
The Bayesian variable selection approach provides a modeling framework that allows estimating the posterior probabilities for a given set of pre-specified models and in particular the posterior probability of
the model estimated under the null hypothesis of no dose effect. The posterior probabilities are used for
multiplicity adjustment using the direct posterior probability approach
Prediction of gene expression in human using rat in vivo gene expression in Japanese Toxicogenomics Project
Full text linkThe Japanese Toxicogenomics Project (TGP) provides large amount of data for the toxicology and safety framework. We focus on gene expression data of rat in vivo and human in vitro. We consider two different analyses for the TGP data. The first analysis is based on two-way analysis of variance model and the goal is to detect genes with significant dose-response relationship in both humans and rats. The second analysis consists of a trend analysis at each time point and the goal is to detect genes in the rat in order to predict gene expression in humans. The first analysis leads us to conclusions about the heterogeneity of the compound set and will suggest how to address this issue to improve future analyses. In the second part, we identify, for particular compounds, groups of genes that are translatable from rats to humans, so they can be used for prediction of human in vitro data based on rat in vivo data
The Usage of Exon-Exon Splice Junctions for the Detection of Alternative Splicing using the REIDS model
Full text linkAbstractAlternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events have been linked to genetic disorders. Therefore, understanding mechanisms of alternative splicing regulation and differences in splicing events between diseased and healthy tissues is crucial in advancing personalized medicine and drug developments. We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events using Human Transcriptome Arrays (HTA). For each exon, a splicing score is calculated based on two scores, an exon score and an array score. The junction information is used to rank the identified exons from strongly confident to less confident candidates for alternative splicing. The design of junctions was also discussed to highlight the complexity of exon-exon and exon-junction interactions. Based on a list of Rt-PCR validated probe sets, REIDS outperforms AltAnalyze and iGems in the % recall rate.</jats:p
Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery
No full textThe modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.Nolen Perualila-Tan would like to thank Janssen Pharmaceutica NV for funding a part of her PhD project. The authors would like to gratefully acknowledge the
Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT) for providing them with the O&O grant 100988: QSTAR Quantitative structure transcriptional activity relationship. Ziv Shkedy and Nolen Perualila-Tan also gratefully acknowledge the support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy)
aBioMarVsuit: A Biomarker Validation Suit for predicting Survival using gene signature
No full textaBioMarVsuit can be used to discover predictive gene signature for predicting survival and dividing patients into low or high risk groups. Classifiers are constructed as linear combination of important genes and prognostic factors and treatment effects can be incorporated, if necessary. Several classifiers are implemented along with the validation procedures: majority votes technique and LASSO and Elastic net based classifiers and as function of scores of first PCA or PLS methods. Gene expression matrix is reduced using the dimension reduction methods PLS and PCA,
when only scores of the first component are used in the classifier. Sensitivity analysis on the cutoff values used for the classifiers which are based on PCA and PLS can be carried out. Large scale cross validation can be performed in order to investigate the mostly selected genes during the evaluation process and therefore distributions for hazard ratios (HR) for the low risk group can be approximated
both on test and training data. The inference is based on resampling methods, permutations in which null distribution of the estimated HR is approximated. Package depends on several other packages mainly, superpc and glmnet
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