biDCG: A New Method for Discovering Global Features of DNA Microarray Data via an Iterative Re-Clustering Procedure

Chen, Chia-Pei; Hsieh, Fushing; Atwill, Rob; Koehl, Patrice

doi:10.1371/journal.pone.0102445

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Biclustering techniques have become very popular in cancer genetics studies, which are expected to connect phenotypes to genotypes; for example, to identify subgroups of cancer patients based on the fact that they share similar gene expression patterns as well as to identify subgroups of genes that is specific to these subtypes of cancer, and therefore could serve as biomarkers [21]. Nowadays, another new way to get DNA information about tumor tissue is the circulating tumor cell (CTC), which is a general designation of all kinds of tumor cells in peripheral blood [22].…”

Section: Genomicsmentioning

confidence: 99%

Pattern recognition for predictive, preventive, and personalized medicine in cancer

2017

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Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer. A key molecule-panel is necessary for accurate PPPM practice. Pattern recognition is an effective methodology to discover key molecule-panel for cancer. The modern omics, computation biology, and systems biology technologies lead to the possibility in recognizing really reliable molecular pattern for PPPM practice in cancer. The present article reviewed the pathophysiological basis, methodology, and perspective usages of pattern recognition for PPPM in cancer so that our previous opinion on multiparameter strategies for PPPM in cancer is translated into real research and development of PPPM or precision medicine (PM) in cancer.

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Section: Genomicsmentioning

confidence: 99%

Pattern recognition for predictive, preventive, and personalized medicine in cancer

2017

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Data Driven Geometry for Learning

Chou

2015

Machine Learning and Data Mining in Pattern Recognition

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Integrative Inferences on Pattern Geometries of Grapes Grown under Water Stress and Their Resulting Wines

et al. 2016

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Multiple datasets of two consecutive vintages of replicated grape and wines from six different deficit irrigation regimes are characterized and compared. The process consists of four temporal-ordered signature phases: harvest field data, juice composition, wine composition before bottling and bottled wine. A new computing paradigm and an integrative inferential platform are developed for discovering phase-to-phase pattern geometries for such characterization and comparison purposes. Each phase is manifested by a distinct set of features, which are measurable upon phase-specific entities subject to the common set of irrigation regimes. Throughout the four phases, this compilation of data from irrigation regimes with subsamples is termed a space of media-nodes, on which measurements of phase-specific features were recoded. All of these collectively constitute a bipartite network of data, which is then normalized and binary coded. For these serial bipartite networks, we first quantify patterns that characterize individual phases by means of a new computing paradigm called “Data Mechanics”. This computational technique extracts a coupling geometry which captures and reveals interacting dependence among and between media-nodes and feature-nodes in forms of hierarchical block sub-matrices. As one of the principal discoveries, the holistic year-factor persistently surfaces as the most inferential factor in classifying all media-nodes throughout all phases. This could be deemed either surprising in its over-arching dominance or obvious based on popular belief. We formulate and test pattern-based hypotheses that confirm such fundamental patterns. We also attempt to elucidate the driving force underlying the phase-evolution in winemaking via a newly developed partial coupling geometry, which is designed to integrate two coupling geometries. Such partial coupling geometries are confirmed to bear causal and predictive implications. All pattern inferences are performed with respect to a profile of energy distributions sampled from network bootstrapping ensembles conforming to block-structures specified by corresponding hypotheses.

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biDCG: A New Method for Discovering Global Features of DNA Microarray Data via an Iterative Re-Clustering Procedure

Cited by 4 publications

References 46 publications

Pattern recognition for predictive, preventive, and personalized medicine in cancer

Pattern recognition for predictive, preventive, and personalized medicine in cancer

Data Driven Geometry for Learning

Integrative Inferences on Pattern Geometries of Grapes Grown under Water Stress and Their Resulting Wines

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