The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Casey S. Greene - One of the best experts on this subject based on the ideXlab platform.
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ADAGE Signature Analysis: differential expression Analysis with data-defined gene sets.
BMC bioinformatics, 2017Co-Authors: Jie Tan, Matthew Huyck, Rene A. Zelaya, Deborah A. Hogan, Casey S. GreeneAbstract:Gene set enrichment Analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses Signatures extracted from public expression data. We first extract expression Signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify Signatures that are differentially active under a given treatment. Our results demonstrate that these Signatures represent biological processes that are perturbed by the experiment. Because these Signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE Signature Analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http:// ) to run these analyses. Both are open-source to allow easy expansion to other organisms or Signature generation methods. We applied ADAGE Signature Analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active Signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE Signature Analysis also identified a Signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed ADAGE Signature Analysis to perform gene set Analysis using data-defined functional gene Signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE Signature Analysis to the community.
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ADAGE Signature Analysis: differential expression Analysis with data-defined gene sets
2017Co-Authors: Jie Tan, Matthew Huyck, Rene A. Zelaya, Deborah A. Hogan, Casey S. GreeneAbstract:Background: Gene set enrichment Analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses Signatures extracted from public expression data. We first extract expression Signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify Signatures that are differentially active under a given treatment. Our results demonstrate that these Signatures represent biological processes that are perturbed by the experiment. Because these Signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE Signature Analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or Signature generation methods. We applied ADAGE Signature Analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active Signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE Signature Analysis also identified a Signature that revealed the molecularly supported link between the MexT regulon and Anr. Conclusions: We designed ADAGE Signature Analysis to perform gene set Analysis using data-defined functional gene Signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE Signature Analysis to the community.
M. El Hachemi Benbouzid - One of the best experts on this subject based on the ideXlab platform.
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A review of induction motors Signature Analysis as a medium for faults detection
IEEE Transactions on Industrial Electronics, 2000Co-Authors: M. El Hachemi BenbouzidAbstract:This paper is intended as a tutorial overview of induction motors Signature Analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor Signature Analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current Signature Analysis which utilizes the results of spectral Analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.
Jie Tan - One of the best experts on this subject based on the ideXlab platform.
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ADAGE Signature Analysis: differential expression Analysis with data-defined gene sets.
BMC bioinformatics, 2017Co-Authors: Jie Tan, Matthew Huyck, Rene A. Zelaya, Deborah A. Hogan, Casey S. GreeneAbstract:Gene set enrichment Analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses Signatures extracted from public expression data. We first extract expression Signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify Signatures that are differentially active under a given treatment. Our results demonstrate that these Signatures represent biological processes that are perturbed by the experiment. Because these Signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE Signature Analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http:// ) to run these analyses. Both are open-source to allow easy expansion to other organisms or Signature generation methods. We applied ADAGE Signature Analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active Signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE Signature Analysis also identified a Signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed ADAGE Signature Analysis to perform gene set Analysis using data-defined functional gene Signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE Signature Analysis to the community.
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ADAGE Signature Analysis: differential expression Analysis with data-defined gene sets
2017Co-Authors: Jie Tan, Matthew Huyck, Rene A. Zelaya, Deborah A. Hogan, Casey S. GreeneAbstract:Background: Gene set enrichment Analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses Signatures extracted from public expression data. We first extract expression Signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify Signatures that are differentially active under a given treatment. Our results demonstrate that these Signatures represent biological processes that are perturbed by the experiment. Because these Signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE Signature Analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or Signature generation methods. We applied ADAGE Signature Analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active Signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE Signature Analysis also identified a Signature that revealed the molecularly supported link between the MexT regulon and Anr. Conclusions: We designed ADAGE Signature Analysis to perform gene set Analysis using data-defined functional gene Signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE Signature Analysis to the community.
Edward J. Mccluskey - One of the best experts on this subject based on the ideXlab platform.
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ITC - Failing Frequency Signature Analysis
2008 IEEE International Test Conference, 2008Co-Authors: Jaekwang Lee, Edward J. MccluskeyAbstract:A failing frequency Signature is a collection of the maximum operating frequencies of each pattern in a pattern set. Analyzing the failing frequency Signature can successfully detect small-delay defects, because even a small-delay defect can cause an outlier to appear in a failing frequency Signature. Moreover, the failing frequency Signature will be consistent even in the presence of process variations. Failing frequency Signature Analysis can effectively detect very hard-to-find defects that occur in No-Trouble-Found (NTF) devices, without the necessity of thorough test patterns. The Analysis can also be used for characterization tests. In this paper, experimental results using the failing frequency Signature to detect small-delay defects are presented using test chips fabricated in a 0.18 mum technology.
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Parallel Signature Analysis design with bounds on aliasing
IEEE Transactions on Computers, 1997Co-Authors: N.r. Saxena, Edward J. MccluskeyAbstract:This paper presents parallel Signature design techniques that guarantee the aliasing probability to be less than 2/L, where L is the test length. Using y Signature samples, a parallel Signature Analysis design is proposed that guarantees the aliasing probability to be less than (y/L)/sup y/2/. Inaccuracies and incompleteness in previously published bounds on the aliasing probability are discussed. Simple bounds on the aliasing probability are derived for parallel Signature designs using primitive polynomials.
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Simple bounds on serial Signature Analysis aliasing for random testing
IEEE Transactions on Computers, 1992Co-Authors: N.r. Saxena, P. Franco, Edward J. MccluskeyAbstract:It is shown that the aliasing probability is bounded above by (1+ epsilon )/L approximately=1/L ( epsilon small for large L) for test lengths L less than the period, L/sub c/, of the Signature polynomial; for test lengths L that are multiples of L/sub c/, the aliasing probability is bounded above by 1; for test lengths L greater than L/sub c/ and not a multiple of L/sub c/, the aliasing probability is bounded above by 2/(L/sub c/+1). These simple bounds avoid any exponential complexity associated with the exact computation of the aliasing probability. Simple bounds also apply to Signature Analysis based on any linear finite state machine (including linear cellular automaton). From these simple bounds it follows that the aliasing probability in a Signature Analysis design using beta intermediate Signatures is bounded by ((1+ epsilon )/sup beta / beta /sup beta /)/L/sup beta /, for beta >
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FTCS - Bounds on Signature Analysis aliasing for random testing
[1991] Digest of Papers. Fault-Tolerant Computing: The Twenty-First International Symposium, 1Co-Authors: N.r. Saxena, P. Franco, Edward J. MccluskeyAbstract:Simple bounds on the aliasing probability for serial Signature Analysis are presented. To motivate the study, it is shown that calculation of exact aliasing is NP-hard and that coding theory does not necessarily help. It is shown that the aliasing probability is bounded above by 2/(L+2) for test lengths L less than the period, L/sub c/, of the Signature polynomials; for test lengths L that are multiples of L/sub c/, the aliasing probability is bounded above by 1; and, for test lengths L greater than L/sub c/ and not a multiple of L/sub c/, the aliasing probability is bounded above by 2/(L/sub c/+1). These simple bounds avoid any exponential complexity associated with the exact computation of the aliasing probability. Simple bounds also apply to Signature Analysis based on any linear finite state machine (including linear cellular automata). >
Matthew Huyck - One of the best experts on this subject based on the ideXlab platform.
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ADAGE Signature Analysis: differential expression Analysis with data-defined gene sets.
BMC bioinformatics, 2017Co-Authors: Jie Tan, Matthew Huyck, Rene A. Zelaya, Deborah A. Hogan, Casey S. GreeneAbstract:Gene set enrichment Analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses Signatures extracted from public expression data. We first extract expression Signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify Signatures that are differentially active under a given treatment. Our results demonstrate that these Signatures represent biological processes that are perturbed by the experiment. Because these Signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE Signature Analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http:// ) to run these analyses. Both are open-source to allow easy expansion to other organisms or Signature generation methods. We applied ADAGE Signature Analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active Signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE Signature Analysis also identified a Signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed ADAGE Signature Analysis to perform gene set Analysis using data-defined functional gene Signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE Signature Analysis to the community.
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ADAGE Signature Analysis: differential expression Analysis with data-defined gene sets
2017Co-Authors: Jie Tan, Matthew Huyck, Rene A. Zelaya, Deborah A. Hogan, Casey S. GreeneAbstract:Background: Gene set enrichment Analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses Signatures extracted from public expression data. We first extract expression Signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify Signatures that are differentially active under a given treatment. Our results demonstrate that these Signatures represent biological processes that are perturbed by the experiment. Because these Signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE Signature Analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or Signature generation methods. We applied ADAGE Signature Analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active Signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE Signature Analysis also identified a Signature that revealed the molecularly supported link between the MexT regulon and Anr. Conclusions: We designed ADAGE Signature Analysis to perform gene set Analysis using data-defined functional gene Signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE Signature Analysis to the community.
