The Experts below are selected from a list of 5727 Experts worldwide ranked by ideXlab platform
Scott Boyer - One of the best experts on this subject based on the ideXlab platform.
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AZOrange - High performance open source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
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AZOrange - High performance Open Source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:BACKGROUND: Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. RESULTS: This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. CONCLUSIONS: AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
Jonna C. Stålring - One of the best experts on this subject based on the ideXlab platform.
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AZOrange - High performance open source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
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AZOrange - High performance Open Source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:BACKGROUND: Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. RESULTS: This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. CONCLUSIONS: AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
Sairam Geethanath - One of the best experts on this subject based on the ideXlab platform.
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rapid prototyping of two dimensional non cartesian k space trajectories rocket using pulseq and Graphical Programming interface
Critical Reviews in Biomedical Engineering, 2019Co-Authors: Pavan Poojar, Sairam Geethanath, Ashok Kumar Reddy, Ramesh VenkatesanAbstract:: Magnetic resonance imaging is a well-established method for diagnostics and/or prognostics of various pathological conditions. Cartesian k-space trajectory-based acquisition is the popular choice in clinical magnetic resonance imaging, owing to its simple acquisition, reconstruction schemes, and well-understood artifacts. However, non-Cartesian trajectories are relatively more time efficient, with involved methods for image reconstruction. In this review, we survey non-Cartesian trajectories from the standpoint of rapid prototyping and/or implementation. We provide examples of two-dimensional (2D) and 3D non-Cartesian k-space trajectories with analytical equations, merits, limitations, and applications. We also demonstrate implementation of three variants of the 2D radial and spiral trajectories (standard, golden angle, and tiny golden angle), using open-source software. For rapid prototyping, pulse sequences were designed with the help of Pulseq. In-vitro phantom and in-vivo brain data were acquired with three variants of radial and spiral trajectories. The obtained raw data were reconstructed using a Graphical Programming interface. The signal-to-noise ratios of each of these reconstructions were quantified and assessed.
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pulseq Graphical Programming interface open source visual environment for prototyping pulse sequences and integrated magnetic resonance imaging algorithm development
Magnetic Resonance Imaging, 2018Co-Authors: Keerthi Sravan Ravi, Pavan Poojar, Sairam Geethanath, Ashok Kumar Reddy, Ramesh Venkatesan, Sneha Potdar, Stefan Kroboth, Jonfredrik Nielsen, Maxim ZaitsevAbstract:Abstract Purpose To provide a single open-source platform for comprehensive MR algorithm development inclusive of simulations, pulse sequence design and deployment, reconstruction, and image analysis. Methods We integrated the “Pulseq” platform for vendor-independent pulse Programming with Graphical Programming Interface (GPI), a scientific development environment based on Python. Our integrated platform, Pulseq-GPI, permits sequences to be defined visually and exported to the Pulseq file format for execution on an MR scanner. For comparison, Pulseq files using either MATLAB only (“MATLAB-Pulseq”) or Python only (“Python-Pulseq”) were generated. We demonstrated three fundamental sequences on a 1.5 T scanner. Execution times of the three variants of implementation were compared on two operating systems. Results In vitro phantom images indicate equivalence with the vendor supplied implementations and MATLAB-Pulseq. The examples demonstrated in this work illustrate the unifying capability of Pulseq-GPI. The execution times of all the three implementations were fast (a few seconds). The software is capable of user-interface based development and/or command line Programming. Conclusion The tool demonstrated here, Pulseq-GPI, integrates the open-source simulation, reconstruction and analysis capabilities of GPI Lab with the pulse sequence design and deployment features of Pulseq. Current and future work includes providing an ISMRMRD interface and incorporating Specific Absorption Ratio and Peripheral Nerve Stimulation computations.
Lars A. Carlsson - One of the best experts on this subject based on the ideXlab platform.
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AZOrange - High performance open source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
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AZOrange - High performance Open Source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:BACKGROUND: Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. RESULTS: This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. CONCLUSIONS: AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
Pedro Almeida - One of the best experts on this subject based on the ideXlab platform.
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AZOrange - High performance open source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
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AZOrange - High performance Open Source machine learning for QSAR modeling in a Graphical Programming environment
Journal of Cheminformatics, 2011Co-Authors: Jonna C. Stålring, Lars A. Carlsson, Pedro Almeida, Scott BoyerAbstract:BACKGROUND: Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both Graphical Programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. RESULTS: This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require Programming knowledge as flexible applications can be created, not only at a scripting level, but also in a Graphical Programming environment. CONCLUSIONS: AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.
