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Semisupervised Profiling of Gene Expressions and Clinical Data [chapter]

Silvano Paoli, Giuseppe Jurman, Davide Albanese, Stefano Merler, Cesare Furlanello
2006 Lecture Notes in Computer Science  
We present an application of BioDCV, a computational environment for semisupervised profiling with Support Vector Machines, aimed at detecting outliers and deriving informative subtypes of patients with  ...  The procedure is demonstrated through the analysis of a liver cancer dataset of 213 samples described by 1993 genes and by pathological features.  ...  Dzerowsky for the helpful indication of this application. We particularly thank the EGrid Project at ITCP Trieste for guidance in developing the Grid implementation of BioDCV.  ... 
doi:10.1007/11676935_35 fatcat:4repyl3dynhobliftjok3utqyu

Inferring progression models for CGH data

Jun Liu, Nirmalya Bandyopadhyay, Sanjay Ranka, M. Baudis, Tamer Kahveci
2009 Computer applications in the biosciences : CABIOS  
We have developed an automatic method to infer a graph model for the markers of multiple cancers from a large population of CGH data.  ...  This method employs the graph model we developed for the individual markers to measure the distance between pairs of cancers. We used this measure to create an evolutionary tree for multiple cancers.  ...  ACKNOWLEDGMENTS This work was supported partially by NSF under grants CCF-0829867, DBI-0606607 and IIS-0845439, and UF Research Initiatives grant (00072365).  ... 
doi:10.1093/bioinformatics/btp365 pmid:19528087 fatcat:vul5wcrq2rabreba7j7awpvu2e

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

2019 Bioinformatics  
Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.  ...  Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management.  ...  Acknowledgements We thank the editor and the three reviewers for their valuable comments that have helped us significantly improve the quality of the article.  ... 
doi:10.1093/bioinformatics/btz769 pmid:31603461 pmcid:PMC8215925 fatcat:gkxuubniy5fslai2t2s5zasdpe

Cancer progression modeling using static sample data

Yijun Sun, Jin Yao, Norma J Nowak, Steve Goodison
2014 Genome Biology  
We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression.  ...  Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples.  ...  Acknowledgements Thanks to the collective efforts of the TCGA initiative of the National Institutes of Health (NIH) and equivalent European consortia, multi-format data from large numbers of tumor tissue  ... 
doi:10.1186/preaccept-5146409691283741 pmid:25155694 pmcid:PMC4196119 fatcat:2pexynwm6jgp5fqdg5s6kae4dy

Cancer progression modeling using static sample data

Yijun Sun, Jin Yao, Norma J Nowak, Steve Goodison
2014 Genome Biology  
We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression.  ...  Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples.  ...  Acknowledgements Thanks to the collective efforts of the TCGA initiative of the National Institutes of Health (NIH) and equivalent European consortia, multi-format data from large numbers of tumor tissue  ... 
doi:10.1186/s13059-014-0440-0 pmid:25155694 pmcid:PMC4196119 fatcat:o56keqrbwncbhdgznj4ycbswfm

St. Jude Cloud-a Pediatric Cancer Genomic Data Sharing Ecosystem

Clay McLeod, Alexander M Gout, Xin Zhou, Andrew Thrasher, Delaram Rahbarinia, Samuel Warren Brady, Michael Macias, Kirby Birch, David Finkelstein, Jobin Sunny, Rahul Mudunuri, Brent A. Orr (+61 others)
2021 Cancer Discovery  
Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients and long-term survivors, and >800  ...  We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes.  ...  Jude patients and their families for making this endeavor possible by contributing their data towards the advancement of cures for pediatric catastrophic disease.  ... 
doi:10.1158/2159-8290.cd-20-1230 pmid:33408242 pmcid:PMC8102307 fatcat:6lgopcgqavhzbhwvhzic6hfjda

PRADA: pipeline for RNA sequencing data analysis

Wandaliz Torres-García, Siyuan Zheng, Andrey Sivachenko, Rahulsimham Vegesna, Qianghu Wang, Rong Yao, Michael F. Berger, John N. Weinstein, Gad Getz, Roel G.W. Verhaak
2014 Computer applications in the biosciences : CABIOS  
by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion  ...  For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available  ...  For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis).  ... 
doi:10.1093/bioinformatics/btu169 pmid:24695405 pmcid:PMC4103589 fatcat:b5ew4ulldrfolnjnfeeybgmsja

Computational approach for deriving cancer progression roadmaps from static sample data

Yijun Sun, Jin Yao, Le Yang, Runpu Chen, Norma J. Nowak, Steve Goodison
2017 Nucleic Acids Research  
Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression.  ...  The developed approach overcame many technical limitations of existing methods.  ...  data from large numbers of tumor tissue samples are publicly available.  ... 
doi:10.1093/nar/gkx003 pmid:28108658 pmcid:PMC5436003 fatcat:wg5tjjhgkfb7lpdmqtas4z7l4y

Integrating gene expression profiling and clinical data

Silvano Paoli, Giuseppe Jurman, Davide Albanese, Stefano Merler, Cesare Furlanello
2008 International Journal of Approximate Reasoning  
Starting from BioDCV, a complete software setup for predictive classification and feature ranking without selection bias, we apply semisupervised profiling for detecting outliers and deriving informative  ...  We propose a combination of machine learning techniques to integrate predictive profiling from gene expression with clinical and epidemiological data.  ...  Dzerosky for helpful indication of this application. We particularly thank the Egrid Project at ICTP Trieste and R. Flor at FBK-irst for guidance in developing the Grid implementation of BioDCV.  ... 
doi:10.1016/j.ijar.2007.03.012 fatcat:56jmhyijsfad5naab26dyqthyu

A comparison of Methods for Data-Driven Cancer Outlier Discovery, and An Application Scheme to Semisupervised Predictive Biomarker Discovery

Seppo Karrila, Julian Hock Ean Lee, Greg Tucker-Kellogg
2011 Cancer Informatics  
Also our stability assessment is in favour of both MOST and COPA; the latter does not pair well with prefiltering for non-Gaussianity, but can handle data sets lacking non-cancer cases.  ...  Outlier detection is a key enabling technology of the workflow, and aids in identifying the focus genes.  ...  Acknowledgements The authors gratefully acknowledge the support of their employer, Lilly Singapore Centre for Drug Discovery, a wholly owned subsidiary of Eli Lilly and Company.  ... 
doi:10.4137/cin.s6868 pmid:21584264 pmcid:PMC3091411 fatcat:turx53fyf5g2jk66cv6mcebp2y

Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data [article]

Runpu Chen, Le Yang, Steve Goodison, Yijun Sun
2019 bioRxiv   pre-print
The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data.  ...  Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management.  ...  For clinical applications, the Conclusion In this paper, we developed a deep-learning based approach that addresses some technical limitations of existing methods for cancer subtype identification.  ... 
doi:10.1101/629865 fatcat:2fmb3vu7svfzrko4myz77srchi

Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications

D Amar, S Izraeli, R Shamir
2017 Oncogene  
Many methods analyze these data jointly with auxiliary information with the aim of identifying subtype-specific results.  ...  Here, we show that somatic gene mutations alone can reliably and specifically predict cancer subtypes. Interpretation of the classifiers provides useful insights for several biomedical applications.  ...  Safra Center for Bioinformatics at Tel Aviv University. Part of the work was done while DA and RS were visiting the Simons Institute for the Theory of Computing.  ... 
doi:10.1038/onc.2016.489 pmid:28092680 pmcid:PMC5485176 fatcat:oqkvurbnsfantirsdrrr66jziu

Artificial intelligence (AI) and big data in cancer and precision oncology

Zodwa Dlamini, Flavia Zita Francies, Rodney Hull, Rahaba Marima
2020 Computational and Structural Biotechnology Journal  
NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and  ...  Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images.  ...  Acknowledgments: We would like to thank the South African Medical Research Council (SAMRC) for funding this research.  ... 
doi:10.1016/j.csbj.2020.08.019 pmid:32994889 pmcid:PMC7490765 fatcat:ocli2vys3zfw7mbqeuio3foipq

Applications of Topological Data Analysis in Oncology

Anuraag Bukkuri, Noemi Andor, Isabel K. Darcy
2021 Frontiers in Artificial Intelligence  
But with great power comes great responsibility: there is now a pressing need for new data analysis algorithms to be developed to make sense of the data and transform this information into knowledge which  ...  We also provide suggestions on avenues for future research including utilizing TDA to analyze cancer time-series data such as gene expression changes during pathogenesis, investigation of the relation  ...  ACKNOWLEDGMENTS The authors would like to thank Ethan Rooke and Hind Benmerabet for their insightful comments on a draft of this manuscript.  ... 
doi:10.3389/frai.2021.659037 pmid:33928240 pmcid:PMC8076640 fatcat:dgpwp4w7qvb57iysz6u2efya3i

Editorial: Ultrasound in Oncology: Application of Big Data and Artificial Intelligence

Yu-Ting Shen, Wen-Wen Yue, Hui-Xiong Xu
2021 Frontiers in Oncology  
For example, with a prospective and multicenter study design, we (6) developed an assembled convolutional neural network model for identifying molecular subtypes of breast cancer that could contribute  ...  With the rapid development of science and technology, big data and artificial intelligence (AI) have ushered in a new era for medicine, especially the medical imaging.  ...  We hope that this Frontiers Research Topic will be an enrichment for US medicine, we give our acknowledgement to all authors for their efforts and commitments, as well as the reviewers who have corrected  ... 
doi:10.3389/fonc.2021.819487 pmid:35004335 pmcid:PMC8730332 fatcat:pkvkcsylz5fffefmhbjdabivae
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