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Your search keyword '"Cava, C"' showing total 2 results
Start Over Author "Cava, C" Journal bmc bioinformatics
2 results on '"Cava, C"'

1. How interacting pathways are regulated by miRNAs in breast cancer subtypes

Author

Cava, Claudia, Colaprico, Antonio, Bertoli, Gloria, Bontempi, Gianluca, Mauri, Giancarlo, Castiglioni, Isabella, Cava, C, Colaprico, A, Bertoli, G, Bontempi, G, Mauri, G, and Castiglioni, I

Subjects
0301 basic medicine, Network of pathway, Biochimie, Informatique appliquée logiciel, Breast Neoplasms, Disease, Computational biology, Biology, Bioinformatics, Biochemistry, ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI, Biological pathway, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Gene interaction, Structural Biology, microRNA, medicine, Humans, Gene Regulatory Networks, Gene, Molecular Biology, Applied Mathematics, Gene Expression Profiling, Research, INF/01 - INFORMATICA, Biologie moléculaire, Computer Science Applications1707 Computer Vision and Pattern Recognition, Genomics, Pathway cross-talk, medicine.disease, Network of pathways, 3. Good health, Computer Science Applications, MicroRNAs, 030104 developmental biology, 030220 oncology & carcinogenesis, miRNAs, Identification (biology), MiRNAs, Female, DNA microarray, MiRNA
Abstract

Background: An important challenge in cancer biology is to understand the complex aspects of the disease. It is increasingly evident that genes are not isolated from each other and the comprehension of how different genes are related to each other could explain biological mechanisms causing diseases. Biological pathways are important tools to reveal gene interaction and reduce the large number of genes to be studied by partitioning it into smaller paths. Furthermore, recent scientific evidence has proven that a combination of pathways, instead than a single element of the pathway or a single pathway, could be responsible for pathological changes in a cell. Results: In this paper we develop a new method that can reveal miRNAs able to regulate, in a coordinated way, networks of gene pathways. We applied the method to subtypes of breast cancer. The basic idea is the identification of pathways significantly enriched with differentially expressed genes among the different breast cancer subtypes and normal tissue. Looking at the pairs of pathways that were found to be functionally related, we created a network of dependent pathways and we focused on identifying miRNAs that could act as miRNA drivers in a coordinated regulation process. Conclusions: Our approach enables miRNAs identification that could have an important role in the development of breast cancer., SCOPUS: cp.j, info:eu-repo/semantics/published

Published
2017
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2. Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression.

Author

Bevilacqua V, Pannarale P, Abbrescia M, Cava C, Paradiso A, and Tommasi S

Subjects
Female, Gene Expression Regulation, Neoplastic, Humans, Neoplasm Metastasis genetics, Oligonucleotide Array Sequence Analysis, Breast Neoplasms genetics, Gene Expression Profiling methods, Support Vector Machine
Abstract

Background: DNA microarray data are used to identify genes which could be considered prognostic markers. However, due to the limited sample size of each study, the signatures are unstable in terms of the composing genes and may be limited in terms of performances. It is therefore of great interest to integrate different studies, thus increasing sample size., Results: In the past, several studies explored the issue of microarray data merging, but the arrival of new techniques and a focus on SVM based classification needed further investigation. We used distant metastasis prediction based on SVM attribute selection and classification to three breast cancer data sets., Conclusions: The results showed that breast cancer classification does not benefit from data merging, confirming the results found by other studies with different techniques.

Published
2012
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