The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jesper Tegner - One of the best experts on this subject based on the ideXlab platform.
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building gene regulatory networks from scatac seq and scrna seq using linked Self Organizing Maps
PLOS Computational Biology, 2019Co-Authors: Camden Jansen, Ricardo N Ramirez, Nicole Elali, David Gomezcabrero, Jesper Tegner, Matthias Merkenschlager, Ana ConesaAbstract:Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using Self-Organizing Maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.
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building gene regulatory networks from scatac seq and scrna seq using linked Self Organizing Maps
bioRxiv, 2018Co-Authors: Camden Jansen, Ricardo N Ramirez, Nicole Elali, David Gomezcabrero, Jesper Tegner, Matthias Merkenschlager, Ana ConesaAbstract:Abstract Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using Self-Organizing Maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.
Ana Conesa - One of the best experts on this subject based on the ideXlab platform.
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building gene regulatory networks from scatac seq and scrna seq using linked Self Organizing Maps
PLOS Computational Biology, 2019Co-Authors: Camden Jansen, Ricardo N Ramirez, Nicole Elali, David Gomezcabrero, Jesper Tegner, Matthias Merkenschlager, Ana ConesaAbstract:Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using Self-Organizing Maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.
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building gene regulatory networks from scatac seq and scrna seq using linked Self Organizing Maps
bioRxiv, 2018Co-Authors: Camden Jansen, Ricardo N Ramirez, Nicole Elali, David Gomezcabrero, Jesper Tegner, Matthias Merkenschlager, Ana ConesaAbstract:Abstract Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using Self-Organizing Maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.
David Gomezcabrero - One of the best experts on this subject based on the ideXlab platform.
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building gene regulatory networks from scatac seq and scrna seq using linked Self Organizing Maps
PLOS Computational Biology, 2019Co-Authors: Camden Jansen, Ricardo N Ramirez, Nicole Elali, David Gomezcabrero, Jesper Tegner, Matthias Merkenschlager, Ana ConesaAbstract:Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using Self-Organizing Maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.
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building gene regulatory networks from scatac seq and scrna seq using linked Self Organizing Maps
bioRxiv, 2018Co-Authors: Camden Jansen, Ricardo N Ramirez, Nicole Elali, David Gomezcabrero, Jesper Tegner, Matthias Merkenschlager, Ana ConesaAbstract:Abstract Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using Self-Organizing Maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.
Javier Blesa - One of the best experts on this subject based on the ideXlab platform.
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improving security in wmns with reputation systems and Self Organizing Maps
Journal of Network and Computer Applications, 2011Co-Authors: Zorana Bankovic, Jose M Moya, Alvaro Araujo, Juanmariano De Goyeneche, Juan Carlos Vallejo, Pedro Malagon, David Fraga, Elena Romero, Javier Blesa, Daniel VillanuevaAbstract:One of the most important problems of WMNs, that is even preventing them from being used in many sensitive applications, is the lack of security. To ensure security of WMNs, two strategies need to be adopted: embedding security mechanisms into the network protocols, and developing efficient intrusion detection and reaction systems. To date, many secure protocols have been proposed, but their role of defending attacks is very limited. We present a framework for intrusion detection in WMNs that is orthogonal to the network protocols. It is based on a reputation system, that allows to isolate ill-behaved nodes by rating their reputation as low, and distributed agents based on unsupervised learning algorithms (Self-Organizing Maps), that are able to detect deviations from the normal behavior. An additional advantage of this approach is that it is quite independent of the attacks, and therefore it can detect and confine new, previously unknown, attacks. Unlike previous approaches, and due to the inherent insecurity of WMN nodes, we assume that confidentiality and integrity cannot be preserved for any single node.
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improving security for scada sensor networks with reputation systems and Self Organizing Maps
Sensors, 2009Co-Authors: Jose M Moya, Alvaro Araujo, Zorana Bankovic, Juanmariano De Goyeneche, Juan Carlos Vallejo, Pedro Malagon, Daniel Villanueva, David Fraga, Elena Romero, Javier BlesaAbstract:The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (Self-Organizing Maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.
Jung Liu - One of the best experts on this subject based on the ideXlab platform.
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shape based image retrieval using support vector machines fourier descriptors and Self Organizing Maps
Information Sciences, 2007Co-Authors: Waitak Wong, Frank Y Shih, Jung LiuAbstract:Image retrieval based on image content has become an important topic in the fields of image processing and computer vision. In this paper, we present a new method of shape-based image retrieval using support vector machines (SVM), Fourier descriptors and Self-Organizing Maps. A list of predicted classes for an input shape is obtained using the SVM, ranked according to their estimated likelihood. The best match of the image to the top-ranked class is then chosen by the minimum mean square error. The nearest neighbors can be retrieved from the Self-Organizing map of the class. We employ three databases of 99, 216, and 1045 shapes for our experiment, and obtain prediction accuracy of 90%, 96.7%, and 84.2%, respectively. Our method outperforms some existing shape-based methods in terms of speed and accuracy.
