Search

Your search keyword '"Visintainer, Roberto"' showing total 43 results
Start Over Author "Visintainer, Roberto"
43 results on '"Visintainer, Roberto"'

1. Differential network analysis and graph classification: a glocal approach

Author

Jurman, Giuseppe, Filosi, Michele, Riccadonna, Samantha, Visintainer, Roberto, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Quantitative Methods
Abstract

Based on the glocal HIM metric and its induced graph kernel, we propose a novel solution in differential network analysis that integrates network comparison and classification tasks. The HIM distance is defined as the one-parameter family of product metrics linearly combining the normalised Hamming distance H and the normalised Ipsen-Mikhailov spectral distance IM. The combination of the two components within a single metric allows overcoming their drawbacks and obtaining a measure that is simultaneously global and local. Furthermore, plugging the HIM kernel into a Support Vector Machine gives us a classification algorithm based on the HIM distance. First, we outline the theory underlying the metric construction. We introduce two diverse applications of the HIM distance and the HIM kernel to biological datasets. This versatility supports the adoption of the HIM family as a general tool for information extraction, quantifying difference among diverse in- stances of a complex system. An Open Source implementation of the HIM metrics is provided by the R package nettols and in its web interface ReNette., Comment: Submitted for BMTL 2015 Proceedings

Published
2016

3. DTW-MIC Coexpression Networks from Time-Course Data

Author

Riccadonna, Samantha, Jurman, Giuseppe, Visintainer, Roberto, Filosi, Michele, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying horizontal displacements (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on both synthetic and transcriptomic datasets.

Published
2012

4. Stability Indicators in Network Reconstruction

Author

Jurman, Giuseppe, Filosi, Michele, Visintainer, Roberto, Riccadonna, Samantha, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

The number of algorithms available to reconstruct a biological network from a dataset of high-throughput measurements is nowadays overwhelming, but evaluating their performance when the gold standard is unknown is a difficult task. Here we propose to use a few reconstruction stability tools as a quantitative solution to this problem. We introduce four indicators to quantitatively assess the stability of a reconstructed network in terms of variability with respect to data subsampling. In particular, we give a measure of the mutual distances among the set of networks generated by a collection of data subsets (and from the network generated on the whole dataset) and we rank nodes and edges according to their decreasing variability within the same set of networks. As a key ingredient, we employ a global/local network distance combined with a bootstrap procedure. We demonstrate the use of the indicators in a controlled situation on a toy dataset, and we show their application on a miRNA microarray dataset with paired tumoral and non-tumoral tissues extracted from a cohort of 241 hepatocellular carcinoma patients.

Published
2012

5. Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

Author

Albanese, Davide, Filosi, Michele, Visintainer, Roberto, Riccadonna, Samantha, Jurman, Giuseppe, and Furlanello, Cesare

Subjects
Statistics - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties, and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large (n=1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Availability and Implementation: Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX)., Comment: Bioinformatics 2012, in press

Published
2012
Full Text
View/download PDF

6. mlpy: Machine Learning Python

Author

Albanese, Davide, Visintainer, Roberto, Merler, Stefano, Riccadonna, Samantha, Jurman, Giuseppe, and Furlanello, Cesare

Subjects
Computer Science - Mathematical Software, Computer Science - Learning, Statistics - Machine Learning
Abstract

mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu., Comment: Corrected a few typos; rephrased two sentences in the Overview section

Published
2012

7. The HIM glocal metric and kernel for network comparison and classification

Author

Jurman, Giuseppe, Visintainer, Roberto, Filosi, Michele, Riccadonna, Samantha, and Furlanello, Cesare

Subjects
Mathematics - Combinatorics, Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Due to the ever rising importance of the network paradigm across several areas of science, comparing and classifying graphs represent essential steps in the networks analysis of complex systems. Both tasks have been recently tackled via quite different strategies, even tailored ad-hoc for the investigated problem. Here we deal with both operations by introducing the Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively measure the difference between two graphs sharing the same vertices. The new measure combines the local Hamming distance and the global spectral Ipsen-Mikhailov distance so to overcome the drawbacks affecting the two components separately. Building then the HIM kernel function derived from the HIM distance it is possible to move from network comparison to network classification via the Support Vector Machine (SVM) algorithm. Applications of HIM distance and HIM kernel in computational biology and social networks science demonstrate the effectiveness of the proposed functions as a general purpose solution., Comment: Frontiers of Network Analysis: Methods, Models, and Applications - NIPS 2013 Workshop

Published
2012

8. Biological network comparison via Ipsen-Mikhailov distance

Author

Jurman, Giuseppe, Riccadonna, Samantha, Visintainer, Roberto, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

Highlighting similarities and differences between networks is an informative task in investigating many biological processes. Typical examples are detecting differences between an inferred network and the corresponding gold standard, or evaluating changes in a dynamic network along time. Although fruitful insights can be drawn by qualitative or feature-based methods, a distance must be used whenever a quantitative assessment is required. Here we introduce the Ipsen-Mikhailov metric for biological network comparison, based on the difference of the distributions of the Laplacian eigenvalues of the compared graphs. Being a spectral measure, its focus is on the general structure of the net so it can overcome the issues affecting local metrics such as the edit distances. Relation with the classical Matthews Correlation Coefficient (MCC) is discussed, showing the finer discriminant resolution achieved by the Ipsen-Mikhailov metric. We conclude with three examples of application in functional genomic tasks, including stability of network reconstruction as robustness to data subsampling, variability in dynamical networks and differences in networks associated to a classification task.

Published
2011

9. A machine learning pipeline for discriminant pathways identification

Author

Barla, Annalisa, Jurman, Giuseppe, Visintainer, Roberto, Squillario, Margherita, Filosi, Michele, Riccadonna, Samantha, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks
Abstract

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix.

Published
2011

10. Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

Author

Grimaldi, Marco, Jurman, Giuseppe, and Visintainer, Roberto

Subjects
Quantitative Biology - Molecular Networks
Abstract

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat)

Published
2010
Full Text
View/download PDF

11. An introduction to spectral distances in networks (extended version)

Author

Jurman, Giuseppe, Visintainer, Roberto, and Furlanello, Cesare

Subjects
Quantitative Biology - Molecular Networks, Mathematics - Combinatorics
Abstract

Many functions have been recently defined to assess the similarity among networks as tools for quantitative comparison. They stem from very different frameworks - and they are tuned for dealing with different situations. Here we show an overview of the spectral distances, highlighting their behavior in some basic cases of static and dynamic synthetic and real networks.

Published
2010

12. Algebraic Comparison of Partial Lists in Bioinformatics

Author

Jurman, Giuseppe, Riccadonna, Samantha, Visintainer, Roberto, and Furlanello, Cesare

Subjects
Statistics - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset.

Published
2010
Full Text
View/download PDF

13. Stability in GRN Inference

Author

Jurman, Giuseppe, primary, Filosi, Michele, additional, Visintainer, Roberto, additional, Riccadonna, Samantha, additional, and Furlanello, Cesare, additional

Published
2018
Full Text
View/download PDF

15. A Machine Learning Pipeline for Discriminant Pathways Identification

Author

Barla, Annalisa, Jurman, Giuseppe, Visintainer, Roberto, Squillario, Margherita, Filosi, Michele, Riccadonna, Samantha, Furlanello, Cesare, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Biganzoli, Elia, editor, Vellido, Alfredo, editor, Ambrogi, Federico, editor, and Tagliaferri, Roberto, editor

Published
2012
Full Text
View/download PDF

16. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Author

Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, Mangravite, Lara M., Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, and Mangravite, Lara M.

Abstract

© 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

Published
2022

21. Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS

Author

Dobbyn, Amanda, primary, Huckins, Laura M., additional, Boocock, James, additional, Sloofman, Laura G., additional, Glicksberg, Benjamin S., additional, Giambartolomei, Claudia, additional, Hoffman, Gabriel E., additional, Perumal, Thanneer M., additional, Girdhar, Kiran, additional, Jiang, Yan, additional, Raj, Towfique, additional, Ruderfer, Douglas M., additional, Kramer, Robin S., additional, Pinto, Dalila, additional, Akbarian, Schahram, additional, Roussos, Panos, additional, Domenici, Enrico, additional, Devlin, Bernie, additional, Sklar, Pamela, additional, Stahl, Eli A., additional, Sieberts, Solveig K., additional, Buxbaum, Joseph, additional, Lewis, David, additional, Gur, Raquel, additional, Hahn, Chang-Gyu, additional, Hirai, Keisuke, additional, Toyoshiba, Hiroyoshi, additional, Essioux, Laurent, additional, Mangravite, Lara, additional, Peters, Mette, additional, Lehner, Thomas, additional, Lipska, Barbara, additional, Cicek, A. Ercument, additional, Lu, Cong, additional, Roeder, Kathryn, additional, Xie, Lu, additional, Talbot, Konrad, additional, Hemby, Scott E., additional, Browne, Andrew, additional, Chess, Andrew, additional, Topol, Aaron, additional, Charney, Alexander, additional, Dobbyn, Amanda, additional, Readhead, Ben, additional, Zhang, Bin, additional, Bennett, David A., additional, Kavanagh, David H., additional, Schadt, Eric E., additional, Shah, Hardik R., additional, Zhu, Jun, additional, Johnson, Jessica S., additional, Fullard, John F., additional, Dudley, Joel T., additional, Brennand, Kristen J., additional, Fromer, Menachem, additional, Mahajan, Milind C., additional, Purcell, Shaun M., additional, Hamamsy, Tymor, additional, Haroutunian, Vahram, additional, Wang, Ying-Chih, additional, Gümüş, Zeynep H., additional, Senthil, Geetha, additional, Kramer, Robin, additional, Logsdon, Benjamin A., additional, Derry, Jonathan M.J., additional, Dang, Kristen K., additional, Visintainer, Roberto, additional, Shinobu, Leslie A., additional, Sullivan, Patrick F., additional, and Klei, Lambertus L., additional

Published
2018
Full Text
View/download PDF

22. Gene Net Weaver Dataset

Author

Furlanello, Cesare, Filosi, Michele, Visintainer, Roberto, Jurman, Giuseppe, and Riccadonna, Samantha

Subjects
bepress|Life Sciences|Biology
Abstract

The zip archive collects the data used in the "GeneNetWeaver Yeast & E. coli data" Section of the manuscript "DTW-MIC Coexpression Networks from Time-Course Data".The zip archive include a text file (GeneNetWeaver_settings.txt) with the Gene Net Weaver parameters used to generate both the networks and the time-course data, and three folders, one for each of the dataset Ecoli20, Ecoli50, Yeast20.Each folder include a text file (gene_names.txt) with the gene names, a tab-separated text file with the true networks as edgelist (true_network.tsv) and a tab-separated text file with the 10 instances of the time-course perturbation data (perturbation_time_series.tsv). The Yeast20 folder includes and additional tab-separated text file with the 4 instances of the time-course knock-out data (dualknockout_time_series.tsv).

Published
2016

30. Distances and Stability in Biological Network Theory

Author

Visintainer, Roberto and Jurman, Giuseppe

Subjects
INF/01 INFORMATICA, BIO/11 BIOLOGIA MOLECOLARE
Abstract

In this thesis we introduce, define and quantitatively assess the stability of the algorithms for the econstruction of networks. We will focus on theory, development and implementation of operative procedures and algorithms for the assessment of stability in complex networks for biological systems, with gene regulatory networks as the key example. A major issue affecting network inference is indeed the high variability of network reconstruction and network topology inferred after data perturbation, different parameter choices and alternative methods. Network stability will thus be used to measure reliability of inferred topology, also obtaining confidence intervals for the outcomes. The methods will be employed to introduce a new approach to reproducibility in the study of complex networks. It will also be coupled with statistical machine learning models, in order to integrate feature selection and network inference within a pathway profiling approach. The evaluation of similarity between networks will be the first and central operative procedure of the developed pipelines, the key point being the identification of distances that can compare network structures improving over classical measures based on the confusion matrix, too coarse for this task. A combination of spectral and edit distances especially tailored for biological networks will be investigated and applied to several high-throughput biological datasets of different nature and with different tasks in oncogenomics, neurogenomics and exposomics.

Published
2013

34. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Author

Wang, Charles, primary, Gong, Binsheng, additional, Bushel, Pierre R, additional, Thierry-Mieg, Jean, additional, Thierry-Mieg, Danielle, additional, Xu, Joshua, additional, Fang, Hong, additional, Hong, Huixiao, additional, Shen, Jie, additional, Su, Zhenqiang, additional, Meehan, Joe, additional, Li, Xiaojin, additional, Yang, Lu, additional, Li, Haiqing, additional, Łabaj, Paweł P, additional, Kreil, David P, additional, Megherbi, Dalila, additional, Gaj, Stan, additional, Caiment, Florian, additional, van Delft, Joost, additional, Kleinjans, Jos, additional, Scherer, Andreas, additional, Devanarayan, Viswanath, additional, Wang, Jian, additional, Yang, Yong, additional, Qian, Hui-Rong, additional, Lancashire, Lee J, additional, Bessarabova, Marina, additional, Nikolsky, Yuri, additional, Furlanello, Cesare, additional, Chierici, Marco, additional, Albanese, Davide, additional, Jurman, Giuseppe, additional, Riccadonna, Samantha, additional, Filosi, Michele, additional, Visintainer, Roberto, additional, Zhang, Ke K, additional, Li, Jianying, additional, Hsieh, Jui-Hua, additional, Svoboda, Daniel L, additional, Fuscoe, James C, additional, Deng, Youping, additional, Shi, Leming, additional, Paules, Richard S, additional, Auerbach, Scott S, additional, and Tong, Weida, additional

Published
2014
Full Text
View/download PDF

36. Distances and Stability in Biological Network Theory

Author

Jurman, Giuseppe, Visintainer, Roberto, Jurman, Giuseppe, and Visintainer, Roberto

Abstract

In this thesis we introduce, define and quantitatively assess the stability of the algorithms for the econstruction of networks. We will focus on theory, development and implementation of operative procedures and algorithms for the assessment of stability in complex networks for biological systems, with gene regulatory networks as the key example. A major issue affecting network inference is indeed the high variability of network reconstruction and network topology inferred after data perturbation, different parameter choices and alternative methods. Network stability will thus be used to measure reliability of inferred topology, also obtaining confidence intervals for the outcomes. The methods will be employed to introduce a new approach to reproducibility in the study of complex networks. It will also be coupled with statistical machine learning models, in order to integrate feature selection and network inference within a pathway profiling approach. The evaluation of similarity between networks will be the first and central operative procedure of the developed pipelines, the key point being the identification of distances that can compare network structures improving over classical measures based on the confusion matrix, too coarse for this task. A combination of spectral and edit distances especially tailored for biological networks will be investigated and applied to several high-throughput biological datasets of different nature and with different tasks in oncogenomics, neurogenomics and exposomics.

Published
2013

37. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Author

Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjö, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjö, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, and Wolfinger, Russell D

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010
Full Text
View/download PDF

38. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Author

DNAVision SA, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjo, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, MAQC Consortium, DNAVision SA, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjo, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, and MAQC Consortium

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010

42. Stability in GRN Inference.

Author

Jurman G, Filosi M, Visintainer R, Riccadonna S, and Furlanello C

Subjects
Computational Biology instrumentation, Datasets as Topic, Gene Expression Profiling instrumentation, Gene Expression Profiling methods, Genome, Human genetics, Genomics instrumentation, Genomics methods, Humans, Proteomics instrumentation, Proteomics methods, Algorithms, Computational Biology methods, Gene Regulatory Networks, Models, Genetic
Abstract

Reconstructing a gene regulatory network from one or more sets of omics measurements has been a major task of computational biology in the last 20 years. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in an ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We introduce here empirical stability, which is induced by variability of reconstruction as a function of data subsampling. By evaluating differences between networks that are inferred using different subsets of the same data we obtain quantitative indicators of the robustness of the algorithm, of the noise level affecting the data, and, overall, of the reliability of the reconstructed graph. We show that empirical stability can be used whenever no ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subsets, where the chosen metric is the Hamming-Ipsen-Mikhailov (HIM) distance evaluating dissimilarity of graph topologies with shared nodes. Operatively, the NetSI family is demonstrated here on synthetic and high-throughput datasets, inferring graphs at different resolution levels (topology, direction, weight), showing how the stability indicators can be effectively used for the quantitative comparison of the stability of different reconstruction algorithms.

Published
2019
Full Text
View/download PDF

43. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Author

Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas BD, Ge X, Megherbi DB, Symmans WF, Wang MD, Zhang J, Bitter H, Brors B, Bushel PR, Bylesjo M, Chen M, Cheng J, Cheng J, Chou J, Davison TS, Delorenzi M, Deng Y, Devanarayan V, Dix DJ, Dopazo J, Dorff KC, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess KR, Hong H, Huan J, Irizarry RA, Judson R, Juraeva D, Lababidi S, Lambert CG, Li L, Li Y, Li Z, Lin SM, Liu G, Lobenhofer EK, Luo J, Luo W, McCall MN, Nikolsky Y, Pennello GA, Perkins RG, Philip R, Popovici V, Price ND, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang SJ, Wu J, Wu Y, Xie Q, Yousef WA, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S, Arasappan D, Bao W, Lucas AB, Berthold F, Brennan RJ, Buness A, Catalano JG, Chang C, Chen R, Cheng Y, Cui J, Czika W, Demichelis F, Deng X, Dosymbekov D, Eils R, Feng Y, Fostel J, Fulmer-Smentek S, Fuscoe JC, Gatto L, Ge W, Goldstein DR, Guo L, Halbert DN, Han J, Harris SC, Hatzis C, Herman D, Huang J, Jensen RV, Jiang R, Johnson CD, Jurman G, Kahlert Y, Khuder SA, Kohl M, Li J, Li L, Li M, Li QZ, Li S, Li Z, Liu J, Liu Y, Liu Z, Meng L, Madera M, Martinez-Murillo F, Medina I, Meehan J, Miclaus K, Moffitt RA, Montaner D, Mukherjee P, Mulligan GJ, Neville P, Nikolskaya T, Ning B, Page GP, Parker J, Parry RM, Peng X, Peterson RL, Phan JH, Quanz B, Ren Y, Riccadonna S, Roter AH, Samuelson FW, Schumacher MM, Shambaugh JD, Shi Q, Shippy R, Si S, Smalter A, Sotiriou C, Soukup M, Staedtler F, Steiner G, Stokes TH, Sun Q, Tan PY, Tang R, Tezak Z, Thorn B, Tsyganova M, Turpaz Y, Vega SC, Visintainer R, von Frese J, Wang C, Wang E, Wang J, Wang W, Westermann F, Willey JC, Woods M, Wu S, Xiao N, Xu J, Xu L, Yang L, Zeng X, Zhang J, Zhang L, Zhang M, Zhao C, Puri RK, Scherf U, Tong W, and Wolfinger RD

Subjects
Animals, Breast Neoplasms diagnosis, Breast Neoplasms genetics, Disease Models, Animal, Female, Gene Expression Profiling methods, Gene Expression Profiling standards, Guidelines as Topic, Humans, Liver Diseases etiology, Liver Diseases pathology, Lung Diseases etiology, Lung Diseases pathology, Multiple Myeloma diagnosis, Multiple Myeloma genetics, Neoplasms diagnosis, Neuroblastoma diagnosis, Neuroblastoma genetics, Predictive Value of Tests, Quality Control, Rats, Survival Analysis, Liver Diseases genetics, Lung Diseases genetics, Neoplasms genetics, Neoplasms mortality, Oligonucleotide Array Sequence Analysis methods, Oligonucleotide Array Sequence Analysis standards
Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results