A simple and exact Laplacian clustering of complex networking phenomena: Application to gene expression profiles

Kim, Choongrak; Cheon, Mookyung; Kang, Minho; Chang, Iksoo

doi:10.1073/pnas.0708598105

Cited by 15 publications

(14 citation statements)

References 33 publications

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“…A separation between DLBCL and FL/B-CLL is clearly apparent in the first two components, whereas FL and B-CLL can be distinguished in the third component. From this unsupervised KODAMA analysis, we may conclude that the lymphoma data consist primarily of two classes (DLBCL and FL/B-CLL) and that FL and B-CLL are secondary classes, confirming the results obtained in a previous study (5). KODAMA performed with SVM shows a clear separation of the three different malignancies, as does LLE and at variance with PCA and ISOMAP (Fig.…”

Section: Resultssupporting

confidence: 77%

“…Therefore, the use of data mining methods is an intrinsically risky activity that can easily lead to the discovery of meaningless patterns. The reliability of a clustering solution can be verified a posteriori by evaluating the predictive accuracy of a supervised classifier by repeatedly leaving out one or a few randomly selected samples as a "test set," whereas the remaining data objects are used as a "training set" (cross-validation) (4)(5)(6).…”

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confidence: 99%

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Knowledge discovery by accuracy maximization

Cacciatore

Luchinat

Tenori³

2014

Proc. Natl. Acad. Sci. U.S.A.

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Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of crossvalidation of the results. The discovery of a local manifold's topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan's presidency and not from its beginning.

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Section: Resultssupporting

confidence: 77%

mentioning

confidence: 99%

Knowledge discovery by accuracy maximization

Cacciatore

Luchinat

Tenori³

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…We first set a ceiling (16,000) and a floor (100) for the intensities and then filter those genes with

max / min \leq 5

max - min \leq 500

so that the informative genes are retained according to a general procedure and a base 10 logarithmic transformation is applied at the end [26]. Here max and min mean the maximum and minimum gene expression values in all the samples, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Spectral clustering can be generally solved by seeking the Fiedler vector of the Laplacian matrix [22,26]. The resulting vector sums to zero and the norm equals to one.…”

Section: Resultsmentioning

confidence: 99%

A unified computational model for revealing and predicting subtle subtypes of cancers

Ren

Wang

et al. 2012

BMC Bioinformatics

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BackgroundGene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples.ResultsWe propose a novel convex optimization model to perform class discovery and class prediction in a unified framework. An efficient algorithm is designed and software named OTCC (Optimization Tool for Clustering and Classification) is developed. Comparison in a simulated dataset shows that our method outperforms the existing methods. We then applied OTCC to acute leukemia and breast cancer datasets. The results demonstrate that our method not only can reveal the subtle structures underlying those cancer gene expression data but also can accurately predict the class labels of unknown cancer samples. Therefore, our method holds the promise to identify novel cancer subtypes and improve diagnosis.ConclusionsWe propose a unified computational framework for class discovery and class prediction to facilitate the discovery and prediction of subtle subtypes of cancers. Our method can be generally applied to multiple types of measurements, e.g., gene expression profiling, proteomic measuring, and recent next-generation sequencing, since it only requires the similarities among samples as input.

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“…There are numerous methods on clustering and classification; see Speed (2003) among others for the analysis of microarray data. Recently, Kim et al (2008) suggested a simultaneous approach to clustering and classification. In microarray data, one may be interested in two ways of inference.…”

Section: Clustering and Classificationmentioning

confidence: 99%

Recent developments of constructing adjacency matrix in network analysis

Hong¹,

Kim²

2014

Journal of the Korean Data and Information Science Society

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In this paper, we review recent developments in network analysis using the graph theory, and introduce ongoing research area with relevant theoretical results. In specific, we introduce basic notations in graph, and conditional and marginal approach in constructing the adjacency matrix. Also, we introduce the Marcenko-Pastur law, the Tracy-Widom law, the white Wishart distribution, and the spiked distribution. Finally, we mention the relationship between degrees and eigenvalues for the detection of hubs in a network.

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A simple and exact Laplacian clustering of complex networking phenomena: Application to gene expression profiles

Abstract: Unraveling of the unified networking characteristics of complex networking phenomena is of great interest yet a formidable task. There is currently no simple strategy with a rigorous framework.

Cited by 15 publications

References 33 publications

Knowledge discovery by accuracy maximization

Knowledge discovery by accuracy maximization

A unified computational model for revealing and predicting subtle subtypes of cancers

Recent developments of constructing adjacency matrix in network analysis

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