Data Mining - Concepts and Techniques.

The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine. 

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Hallmarks of Cancer: New Dimensions

50 Years of Image Analysis

The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.

Hallmarks of Cancer: New Dimensions.

From bench to bedside

Among the large number of gene selection algorithms available in literature, the rough set based maximum relevance-maximum significance (RSMRMS) algorithm has been shown to be successful for selecting a set of relevant and significant genes from microarray data. However, the analysis of functional diversity of a gene set is essential to understand the role of genes in a particular disease as well as to evaluate the effectiveness of a gene selection algorithm. In this regard, a gene ontology based quantitative index, termed as degree of functional diversity (DoFD), is proposed to quantify the functional diversity of a set of genes selected by any gene selection algorithm. Moreover, a new gene selection algorithm is presented, integrating judiciously the merits of both DoFD and RSMRMS, to select relevant and significant genes those are also functionally diverse. The performance of the proposed gene selection algorithm, along with a comparison with other gene selection methods, is studied using the proposed DoFD and predictive accuracy of K-nearest neighbor rule and support vector machine on six cancer and one arthritis microarray data sets. An important finding is that the proposed gene ontology based quantitative index can accurately evaluate functional diversity of a set of genes. Also, the proposed gene selection algorithm is shown to be effective for selecting relevant, significant, and functionally diverse genes from microarray data.

Gene ontology based quantitative index to select functionally diverse genes

The microRNAs or miRNAs are short, endogenous RNAs having ability to regulate gene expression at the post-transcriptional level. Various studies have revealed that a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction increase the challenges of comprehending and interpreting the resulting mass of data. In this regard, this paper presents the application of city block distance in order to extract meaningful information from miRNA expression data. The proposed method judiciously integrates the merits of robust rough-fuzzy c-means algorithm and normalized range-normalized city block distance to discover co-expressed miRNA clusters. The city block distance is used to calculate the membership functions of fuzzy sets, and thereby helps to handle minute differences between two miRNA expression profiles. The effectiveness of the proposed approach, along with a comparison with other related methods, is demonstrated on several miRNA expression data sets using different cluster validity indices and gene ontology.

City Block Distance for Identification of Co-expressed MicroRNAs

MicroRNAs (miRNAs) are short, endogenous RNAs having ability to regulate gene expression at the post-transcriptional level. Various studies have revealed that miRNAs tend to cluster on chromosomes. Members of a cluster that are at close proximity on chromosome are highly likely to be processed as cotranscribed units. Therefore, a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction create a big challenge for scientists to decipher this huge expression data. In order to extract meaningful information from expression data, this paper presents the application of robust rough-fuzzy c-means (rRFCM) algorithm to discover co-expressed miRNA clusters. The rRFCM algorithm comprises a judicious integration of rough sets, fuzzy sets, and c-means algorithm. The effectiveness of the rRFCM algorithm and different initialization methods, along with a comparison with other related methods, is demonstrated on three miRNA microarray expression data sets using Silhouette index, Davies-Bouldin index, Dunn index, β index, and gene ontology based analysis.

Robust RFCM algorithm for identification of co-expressed miRNAs

The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.

/pdf/rough-sets-for-in-silico-identification-of-differentially-3g5ihzv0xa.pdf

Rough sets for in silico identification of differentially expressed miRNAs

The microRNAs, also known as miRNAs are, the class of small non-coding RNAs that repress the expression of a gene post-transcriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and utility of miRNAs for the diagnosis of cancer and other diseases. A large number of works have been conducted to identify differentially expressed miRNAs as unlike with mRNA expression, a modest number of miRNAs might be sufficient to classify human cancers. In this regard, this paper presents a rough set based feature selection algorithm to select miRNAs from expression data that can classify tissue samples into their respective category with minimal error rate. It selects a set of miRNAs by maximizing both the relevance and significance of miRNAs. The effectiveness of the rough set based algorithm, along with a comparison with other related algorithms, is demonstrated on three miRNA microarray expression data sets using the B.632+ bootstrap error rate of support vector machine.

Sushmita Paul

Papers

Gene ontology based quantitative index to select functionally diverse genes

City Block Distance for Identification of Co-expressed MicroRNAs

Robust RFCM algorithm for identification of co-expressed miRNAs

Rough sets for in silico identification of differentially expressed miRNAs

Rough sets and support vector machine for selecting differentially expressed miRNAs