Bayesian approach to transforming public gene expression repositories into disease diagnosis databases

Huang, Haiyan; Liu, Chun-Chi; Zhou, Xianghong Jasmine

doi:10.1073/pnas.0912043107

Cited by 51 publications

(66 citation statements)

References 28 publications

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“…There are also many other functional phenotype-based approaches that use the CMap resource to understand MoA [7,[78][79][80]. It is widely known that many drugs with therapeutic targets in cancer prognosis and diagnosis have been identified using CMap.…”

Section: Cmap-based Elucidation Of Drug Moamentioning

confidence: 99%

A review of connectivity map and computational approaches in pharmacogenomics

et al. 2017

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Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

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Section: Cmap-based Elucidation Of Drug Moamentioning

confidence: 99%

A review of connectivity map and computational approaches in pharmacogenomics

et al. 2017

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“…Instead, most approaches choose either a custom (e.g. standardized differential vectors as in 27,28 ) or a popular standard (e.g. SVMs 29,30 ).…”

Section: Discussionmentioning

confidence: 99%

One-Class Detection of Cell States in Tumor Subtypes

2015

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The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.

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“…For example, indexes of differentiation in the thyroid can be derived from the reuse of public datasets [5], and general models of disease classification built [6]. Also, genome-wide data analysis methodologies can be tested comprehensively on a large scale [7].…”

Section: Rationalementioning

confidence: 99%

InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor

et al. 2012

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Genomics datasets are increasingly useful for gaining biomedical insights, with adoption in the clinic underway. However, multiple hurdles related to data management stand in the way of their efficient large-scale utilization. The solution proposed is a web-based data storage hub. Having clear focus, flexibility and adaptability, InSilico DB seamlessly connects genomics dataset repositories to state-of-the-art and free GUI and command-line data analysis tools. The InSilico DB platform is a powerful collaborative environment, with advanced capabilities for biocuration, dataset sharing, and dataset subsetting and combination. InSilico DB is available from https://insilicodb.org.

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Bayesian approach to transforming public gene expression repositories into disease diagnosis databases

Cited by 51 publications

References 28 publications

A review of connectivity map and computational approaches in pharmacogenomics

A review of connectivity map and computational approaches in pharmacogenomics

One-Class Detection of Cell States in Tumor Subtypes

InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor

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