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. 

/pdf/hallmarks-of-cancer-new-dimensions-fgechgl0.pdf

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

This chapter presents the design of a hybrid learning model, termed as neural network tree (NNTree), for identification of splice-junction and protein coding region in DNA sequences. It incorporates the advantages of both decision tree and neural network. An NNTree is a decision tree, where each nonterminal node contains a neural network. The versatility of this method is illustrated through its application in splice-junction and gene identification problems. Extensive experimental results establish that the NNTree produces more accurate classifier than that have previously been obtained for a range of different sequence lengths; thereby indicating a cost-effective alternative in splice-junction and protein coding region identification problems. .

Neural Network Tree for Identification of Splice Junction and Protein Coding Region in DNA

Abstract Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

/pdf/a-review-on-methods-for-predicting-mirna-mrna-regulatory-2s32c0j3.pdf

A review on methods for predicting miRNA–mRNA regulatory modules

One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new similarity measure to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. A new gene selection algorithm is introduced to identify disease genes, integrating judiciously the information of gene expression profiles and protein-protein interaction networks. The proposed algorithm selects a set of genes from microarray data as disease genes by maximizing the relevance and functional similarity of the selected genes. The performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on colorectal cancer data set.

/pdf/a-new-similarity-measure-for-identification-of-disease-genes-39q8w5olc1.pdf

A New Similarity Measure for Identification of Disease Genes

Feature selection is an important data pre-processing step in pattern recognition and data mining. It is effective in reducing dimensionality and redundancy among the selected features, and increasing the performance of learning algorithm and generating information-rich features subset. In this regard, the chapter reports on a rough set-based feature selection algorithm called maximum relevance-maximum significance (MRMS), and its applications on quantitative structure activity relationship (QSAR) and gene expression data. It selects a set of features from a high-dimensional data set by maximizing the relevance and significance of the selected features. A theoretical analysis is reported to justify the use of both relevance and significance criteria for selecting a reduced feature set with high predictive accuracy. The importance of rough set theory for computing both relevance and significance of the features is also established. The performance of the MRMS algorithm, along with a comparison with other related methods, is studied on three QSAR data sets using the R 2 statistic of support vector regression method, and on five cancer and two arthritis microarray data sets by using the predictive accuracy of the K-nearest neighbor rule and support vector machine.

Rough Set-Based Feature Selection: Criteria of Max-Dependency, Max-Relevance, and Max-Significance

In medical imaging technology, a number of complementary diagnostic tools such as X-ray computer tomography, position emission tomography, and magnetic resonance imaging (MRI) are available.

Sushmita Paul

Papers

Neural Network Tree for Identification of Splice Junction and Protein Coding Region in DNA

A review on methods for predicting miRNA–mRNA regulatory modules

A New Similarity Measure for Identification of Disease Genes

Rough Set-Based Feature Selection: Criteria of Max-Dependency, Max-Relevance, and Max-Significance

Fuzzy Measures and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images