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

Microarray technology is one of the important biotechnological means that allows to record the expression levels of thousands of genes simultaneously within a number of different samples. An important application of microarray gene expression data in functional genomics is to classify samples according to their gene expression profiles. Among the large amount of genes present in microarray gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. In this regard, mutual information has been shown to be successful for selecting a set of relevant and nonredundant genes from microarray data. However, information theory offers many more measures such as the f-information measures that may be suitable for selection of genes from microarray gene expression data. This chapter presents different f-information measures as the evaluation criteria for gene selection problem. The performance of different f-information measures is compared with that of mutual information based on the predictive accuracy of naive Bayes classifier, k-nearest neighbor rule, and support vector machine. An important finding is that some f-information measures are shown to be effective for selecting relevant and nonredundant genes from microarray data. The effectiveness of different f-information measures, along with a comparison with mutual information, is demonstrated on several cancer data sets.

f -Information Measures for Selection of Discriminative Genes from Microarray Data

The prediction of functional sites in proteins is another important problem in bioinformatics. It is an important issue in protein function studies and hence, drug design.

Design of String Kernel to Predict Protein Functional Sites Using Kernel-Based Classifiers

Stomach cancer continues to be one of the most common cancer types in the world. Poor prognosis and late detection are major challenges in the diagnosis and treatment of this cancer. Currently, Next-Generation Sequencing (NGS) of genome has revolutionized stomach cancer research by providing multi-view data which can be used for better understanding of the underlying molecular mechanism of this disease. Micro-RNA (miRNA) sequencing data is one such high resolution expression data. MiRNAs are evolutionary conserved, small, non-coding RNAs that play a role in post-transcriptional regulation of gene expression. They are proven to show abnormal expression for a specific biological condition like tumor or age or survival of stomach cancer which makes them potential biomarkers for this cancer type. MiRNA data analysis comes with a challenge of less number of samples as compared to the number of miRNAs. Finding a small set of miRNAs is needed to identify potential biomarkers. In this regard, here, a method for selecting a small subset of miRNAs from the entire miRNA expression data has been proposed that selects miRNAs, common to three different categories of clinical outcomes, viz. condition, age, and survival status. First, three feature selection methods have been used to rank miRNAs individually for different categories. These ranks are then used to compute an ensemble of ranks of each miRNA using adaptive weight method for each category. Second, the top 100 miRNAs from each category have been used to find the miRNAs that are common to all categories. As a result, four miRNAs are found which are validated using classification of subclass under each category, miRNA-Gene-TF network, PPI network, expression analysis using box plots, KEGG pathway and GO enrichment analysis.

Identification of Four miRNAs by Analysing Multi-View miRNA-seq Data of Stomach Cancer

Microarray technology is one of the important biotechnological means that has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples [9, 14, 37].

Sushmita Paul

Papers

f -Information Measures for Selection of Discriminative Genes from Microarray Data

Design of String Kernel to Predict Protein Functional Sites Using Kernel-Based Classifiers

Identification of Four miRNAs by Analysing Multi-View miRNA-seq Data of Stomach Cancer

Grouping Functionally Similar Genes From Microarray Data Using Rough–Fuzzy Clustering

Pattern Recognition Algorithms for Multi-Omics Data Analysis