Multistage Carcinogenesis: A Unified Framework for Cancer Data Analysis

2020 ◽  
pp. 117-136
Author(s):  
Suresh Moolgavkar ◽  
Georg Luebeck
Keyword(s):  
Data Analysis ◽  
Unified Framework ◽  
Cancer Data ◽  
Multistage Carcinogenesis

scholarly journals PyGNA: a unified framework for geneset network analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Viola Fanfani ◽  
Fabio Cassano ◽  
Giovanni Stracquadanio
Keyword(s):  
Data Analysis ◽  
Network Analysis ◽  
Biological Networks ◽  
High Performance ◽  
Large Scale ◽  
Sequencing Data ◽  
Unified Framework ◽  
Null Distributions ◽  
Viable Approach ◽  
Easy Integration

Abstract Background Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes. Results Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker. Conclusions We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

scholarly journals Breast cancer data analysis for survivability studies and prediction

2018 ◽  
Vol 155 ◽  
pp. 199-208 ◽  
Author(s):  
Nagesh Shukla ◽  
Markus Hagenbuchner ◽  
Khin Than Win ◽  
Jack Yang
Keyword(s):  
Breast Cancer ◽  
Data Analysis ◽  
Breast Cancer Data ◽  
Cancer Data

A Unified Framework for Outlier Detection in Trace Data Analysis

2014 ◽  
Vol 27 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Zhiguo Li ◽  
Robert J. Baseman ◽  
Yada Zhu ◽  
Fateh A. Tipu ◽  
Noam Slonim ◽  
...  
Keyword(s):  
Data Analysis ◽  
Outlier Detection ◽  
Unified Framework ◽  
Trace Data

Network-Module-Based Approaches in Cancer Data Analysis

2013 ◽  
pp. 173-192
Author(s):  
Guanming Wu ◽  
Lincoln Stein
Keyword(s):  
Data Analysis ◽  
Network Module ◽  
Cancer Data

Early prevention program for colon and rectal cancer. Data analysis

2019 ◽  
Vol 493 ◽  
pp. S363
Author(s):  
R. Jáñez Carrera ◽  
N.L. Hernandez Martinez ◽  
V. O'valle Aisa ◽  
L. De La Hoz Gil
Keyword(s):  
Rectal Cancer ◽  
Data Analysis ◽  
Prevention Program ◽  
Early Prevention ◽  
Cancer Data ◽  
Colon And Rectal Cancer
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