A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge

Tian, Ze; Hwang, Taesung; Kuang, Rui

doi:10.1093/bioinformatics/btp467

Cited by 95 publications

(59 citation statements)

References 29 publications

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“…Rather, our intent is to stimulate and encourage the use of hypergraph-based methods in contexts such as signaling pathways where their unique capabilities will have considerable impact. In the future, we hope that hypergraph-based analyses can encompass other types of cellular processes in conjunction with methods that infer hypergraphs directly from systems biology datasets [48–51]. …”

Section: Discussionmentioning

confidence: 99%

Signaling hypergraphs

Ritz¹,

Tegge²,

Kim³

et al. 2014

Trends in Biotechnology

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Signaling pathways function as information-passing mechanisms of the cell. A number of extensively manually curated databases maintain the current knowledge-base for signaling pathways, inviting computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight an underutilized representation for signaling pathways: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.

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Section: Discussionmentioning

confidence: 99%

Signaling hypergraphs

Ritz¹,

Tegge²,

Kim³

et al. 2014

Trends in Biotechnology

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“…The work in [27] cast the image matching problem to a hypergraphbased convex optimization problem. Tian et al [28] introduced a hypergraph-based learning algorithm to classify arrayCGH data with spatial priors modeled as correlations among variables for cancer classification and biomarker identification. Huang et al [29] formulated the task of image clustering as a problem of hypergraph partition, where image and its nearest neighbors form two kinds of hyperedges based on the descriptors of shape or appearance.…”

Section: B Hypergraph-based Learningmentioning

confidence: 99%

Multiview Physician-Specific Attributes Fusion for Health Seeking

Nie¹,

Zhang

Yan

et al. 2017

IEEE Trans. Cybern.

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Abstract-Community-based health services have risen as important online resources for resolving users health concerns. Despite the value, the gap between what health seekers with specific health needs and what busy physicians with specific attitudes and expertise can offer is being widened. To bridge this gap, we present a question routing scheme that is able to connect health seekers to the right physicians. In this scheme, we first bridge the expertise matching gap via a probabilistic fusion of the physician-expertise distribution and the expertise-question distribution. The distributions are calculated by hypergraphbased learning and kernel density estimation. We then measure physicians attitudes toward answering general questions from the perspectives of activity, responsibility, reputation, and willingness. At last, we adaptively fuse the expertise modeling and attitude modeling by considering the personal needs of the health seekers. Extensive experiments have been conducted on a real-world dataset to validate our proposed scheme.Index Terms-Adaptive fusion, attitude modeling, communitybased health services (CHSs), expertise modeling, question routing.

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“…Each of these are individually providing insights into how cells work from a system-level perspective with a high level of molecular detail [5]. Integrating all this valuable knowledge together in an appropriate way could provide useful prior information for detecting noise and removing confounding factors from biological data [6,7]. Moreover, this can lead to the discovery of a stable link between genes, proteins, drugs, and disease.…”

Section: Introductionmentioning

confidence: 99%

“…The network-constrained strategy integrates biological prior interaction knowledge, like gene or protein regulatory networks with gene expression data. Such a strategy can identify biomarkers that only have weak effects but often play a significant role in diverse biological processes [6,7,16,17]. We then applied the proposed method to a logistic regression model to build two drug response predictors, erlotinib and sorafenib for non-small cell lung cancer (NSCLC).…”

Section: Introductionmentioning

confidence: 99%

Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

Huang

Dai

Liang

2018

Cell Physiol Biochem

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Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. Results: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient’s in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. Conclusion: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.

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A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge

Abstract: Supplementary data are available at bioinformatics online.

Cited by 95 publications

References 29 publications

Signaling hypergraphs

Signaling hypergraphs

Multiview Physician-Specific Attributes Fusion for Health Seeking

Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

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