Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction

Hwang, Taesung; Tian, Ze; Kuang, Rui; Kocher, Jean-Pierre

doi:10.1109/icdm.2008.37

Cited by 62 publications

(29 citation statements)

References 20 publications

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“…Among these methods, hypergraph learning [22], [24], as a natural extension of the graph model, has been widely investigated and achieved promising performance in many tasks. This approach is built on a hypergraph structure which contains two parts, a vertex set and a hyperedge set.…”

Section: Introductionmentioning

confidence: 99%

Visual Classification by <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2415497.gif"/></alternatives></inline-formula>-Hypergraph Modeling

Wang

Liu

2015

IEEE Trans. Knowl. Data Eng.

139

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Visual classification has attracted considerable research interests in the past decades. In this paper, a novel ' 1 -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate hyperedges and learns the relationship among hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different hyperedges. We leverage the prior knowledge on the hyperedges so that the hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named ' 1 -hypergraph since the ' 1 sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.

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Section: Introductionmentioning

confidence: 99%

Visual Classification by <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2415497.gif"/></alternatives></inline-formula>-Hypergraph Modeling

Wang

Liu

2015

IEEE Trans. Knowl. Data Eng.

139

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“…Hwang et al [62] propose an algorithm called HyperGene that attempts to minimize the objective function Φ(f, w) in an iterative fashion. In each iteration, it first keeps w fixed and computes arg min f,w Φ(f, w = w t−1 ); then it keeps f fixed and computes arg min f,w Φ(f = f t−1 , w) (here f t−1 and w t−1 are respectively the values of f and w, as determined in the previous iteration).…”

Section: W(e) |{X|x∈e}|mentioning

confidence: 99%

“…Hwang et al [62] represent gene-expression profiles using an undirected hypergraph whose nodes represent samples and whose hyperedges each represents either an up-or a down-regulated gene (but not both). Thus the number of hyperedges is twice the number of genes.…”

Section: Hypergraph From Gene Expressionmentioning

confidence: 99%

Unstable Communities in Network Ensembles

Rahman

Jan

Kim³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble.In this thesis, we seek to find "unstable communities," which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings.Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns.Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.Dedicated to the memory of my spiritual ...

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“…A support vector machine (SVM) classifier with radial basis kernels and an ensemble of templates are used to localize the tumor position in [11]. Also, in [12], a hyper-graph based learning algorithm is proposed to integrate micro array gene expressions and protein-protein interactions for cancer outcome prediction and bio-marker identification.…”

Section: Related Workmentioning

confidence: 99%

Medical Data Mining for Early Deterioration Warning in General Hospital Wards

Mao

Chen

Hackmann

et al. 2011

2011 IEEE 11th International Conference on Data Mining Workshops

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Abstract-Data mining on medical data has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Every year, 4-17% of patients undergo cardiopulmonary or respiratory arrest while in hospitals. Early prediction techniques have become an apparent need in many clinical area. Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, based on data mining technology, we propose an early warning system (EWS) designed to identify the signs of clinical deterioration and provide early warning for serious clinical events.Our EWS is designed to provide reliable early alarms for patients at the general hospital wards (GHWs). EWS automatically identifies patients at risk of clinical deterioration based on their existing electronic medical record. The main task of EWS is a challenging classification problem on highdimensional stream data with irregular, multi-scale data gaps, measurement errors, outliers, and class imbalance. In this paper, we propose a novel data mining framework for analyzing such medical data streams. The framework addresses the above challenges and represents a practical approach for early prediction and prevention based on data that would realistically be available at GHWs.We assess the feasibility of the proposed EWS approach through retrospective study that includes data from 28,927 visits at a major hospital. Finally, we apply our system in a real-time clinical trial and obtain promising results. This project is an example of multidisciplinary cyber-physical systems involving researchers in clinical science, data mining, and nursing staff in the hospital. Our early warning algorithm shows promising result: the transfer of patients to ICU was predicted with sensitivity of 0.4127 and specificity of 0.950 in the real time system.

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Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction

Cited by 62 publications

References 20 publications

Visual Classification by <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2415497.gif"/></alternatives></inline-formula>-Hypergraph Modeling

Visual Classification by <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="wang-ieq1-2415497.gif"/></alternatives></inline-formula>-Hypergraph Modeling

Unstable Communities in Network Ensembles

Medical Data Mining for Early Deterioration Warning in General Hospital Wards

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