The Experts below are selected from a list of 2844 Experts worldwide ranked by ideXlab platform
Deena M.a. Gendoo - One of the best experts on this subject based on the ideXlab platform.
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MM2S: personalized diagnosis of medulloblastoma patients and model systems
Source Code for Biology and Medicine, 2016Co-Authors: Deena M.a. Gendoo, Benjamin Haibe-kainsAbstract:Background Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. Results The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. Conclusions Our MM2S package can be used to generate predictions without having to rely on an External Web Server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/Web/packages/MM2S/ , as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.
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mm2s personalized diagnosis of medulloblastoma patients and model systems
Source Code for Biology and Medicine, 2016Co-Authors: Deena M.a. Gendoo, Benjamin HaibekainsAbstract:Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. Our MM2S package can be used to generate predictions without having to rely on an External Web Server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/Web/packages/MM2S/ , as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.
Benjamin Haibekains - One of the best experts on this subject based on the ideXlab platform.
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mm2s personalized diagnosis of medulloblastoma patients and model systems
Source Code for Biology and Medicine, 2016Co-Authors: Deena M.a. Gendoo, Benjamin HaibekainsAbstract:Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. Our MM2S package can be used to generate predictions without having to rely on an External Web Server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/Web/packages/MM2S/ , as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.
Benjamin Haibe-kains - One of the best experts on this subject based on the ideXlab platform.
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MM2S: personalized diagnosis of medulloblastoma patients and model systems
Source Code for Biology and Medicine, 2016Co-Authors: Deena M.a. Gendoo, Benjamin Haibe-kainsAbstract:Background Medulloblastoma (MB) is a highly malignant and heterogeneous brain tumour that is the most common cause of cancer-related deaths in children. Increasing availability of genomic data over the last decade had resulted in improvement of human subtype classification methods, and the parallel development of MB mouse models towards identification of subtype-specific disease origins and signaling pathways. Despite these advances, MB classification schemes remained inadequate for personalized prediction of MB subtypes for individual patient samples and across model systems. To address this issue, we developed the Medullo-Model to Subtypes ( MM2S ) classifier, a new method enabling classification of individual gene expression profiles from MB samples (patient samples, mouse models, and cell lines) against well-established molecular subtypes [Genomics 106:96-106, 2015]. We demonstrated the accuracy and flexibility of MM2S in the largest meta-analysis of human patients and mouse models to date. Here, we present a new functional package that provides an easy-to-use and fully documented implementation of the MM2S method, with additional functionalities that allow users to obtain graphical and tabular summaries of MB subtype predictions for single samples and across sample replicates. The flexibility of the MM2S package promotes incorporation of MB predictions into large Medulloblastoma-driven analysis pipelines, making this tool suitable for use by researchers. Results The MM2S package is applied in two case studies involving human primary patient samples, as well as sample replicates of the GTML mouse model. We highlight functions that are of use for species-specific MB classification, across individual samples and sample replicates. We emphasize on the range of functions that can be used to derive both singular and meta-centric views of MB predictions, across samples and across MB subtypes. Conclusions Our MM2S package can be used to generate predictions without having to rely on an External Web Server or additional sources. Our open-source package facilitates and extends the MM2S algorithm in diverse computational and bioinformatics contexts. The package is available on CRAN, at the following URL: https://cran.r-project.org/Web/packages/MM2S/ , as well as on Github at the following URLs: https://github.com/DGendoo and https://github.com/bhklab.
Tuchyňa Juraj - One of the best experts on this subject based on the ideXlab platform.
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Wireless Data Transfer In Modern Vehicles
Vysoké učení technické v Brně Fakulta elektrotechniky a komunikačních technologií, 2019Co-Authors: Tuchyňa JurajAbstract:The main purpose of this project is to analyse the most common protocols used in the modern vehicles compatible with OBD II specifications and focus on processing acquired diagnostic data. The result of this project is prototype device for vehicle data acquisition. This device is able to send processed data on the External Web Server via the Internet network. Obtained data will be saved on data Server
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Wireless transmission of diagnostic data from the vehicle
Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019Co-Authors: Tuchyňa JurajAbstract:Cieľom tejto práce je teoretická analýza používaných protokolov v súčasnom štandarde motorových vozidiel OBD II. Taktiež spracovanie diagnostických dát a ich následná interpretácia. V rámci práce navrhneme zariadenie pre bezdrôtový prenos diagnostických dát z automobilu prostredníctvom siete Internet a analyzujeme možnosti prenosu do tejto siete. Spracované dáta budeme následne ukladať na vzdialenom externom Serveri. Toto zariadenie následne prakticky skonštruujeme a programovo oživíme.The main purpose of this project is to analyse used protocols in vehicles available under the OBD II specifications. In the project we will also focus on processing diagnostic data. We will design device for vehicle diagnostic. This device will be able to send processed data on External Web Server via Internet network. Obtained data will be saved on data Server. Building of functional receiver will be the major part of master´s thesis.
Filipe Perdigoto - One of the best experts on this subject based on the ideXlab platform.
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Mobile Web Server for elderly people monitoring
2008 IEEE International Symposium on Consumer Electronics, 2008Co-Authors: Sergio M. M. De Faria, Telmo R. Fernandes, Filipe PerdigotoAbstract:In this paper we propose a mobile system for monitoring of moving objects, namely tracking elderly people. This system aims to provide help for people with some kind of memory loss disease, namely Alzheimerpsilas. It is capable of providing the geographic position of the person carrying the mobile equipment, both by a patient request (pressing a key to send a message) or anytime trough a Webpage consult by an External authorized person. The portable device consists of a GSM/GPRS module, a GPS module, a I/O and MCU module and a Web Server. It also incorporates hands free voice communication capability, which allows communication with a distant person, in the event of an unexpected incident. The problem of External Web Server dependency is tackled by implementing an embedded Web Server in the mobile device. This significantly reduces the user expenses that are limited to the data communication between any authorized person and the mobile system. Additionally, we have included a tri-axial accelerometer sensor to constrain the current consumption while the device is not moving, which can also detect the falling of the patient. However, the main achievement of this work, regardless the hardware integration, is the development of a Web Server supported on the relatively limited computational resources available in the module. To this extent, it is not required any dedicated software to access this equipment, but a Web browser.
