The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

Mikhail V Gorshkov - One of the best experts on this subject based on the ideXlab platform.

  • Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics
    Journal of The American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics , the Source Code Repository at http://hg.theorchromo.ru/pyteomics , documentation at http://packages.python.org/pyteomics . Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/ . Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

  • pyteomics a python framework for exploratory data analysis and rapid software prototyping in proteomics
    Journal of the American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the Source Code Repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Anton Goloborodko - One of the best experts on this subject based on the ideXlab platform.

  • Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics
    Journal of The American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics , the Source Code Repository at http://hg.theorchromo.ru/pyteomics , documentation at http://packages.python.org/pyteomics . Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/ . Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

  • pyteomics a python framework for exploratory data analysis and rapid software prototyping in proteomics
    Journal of the American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the Source Code Repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Lev I Levitsky - One of the best experts on this subject based on the ideXlab platform.

  • Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics
    Journal of The American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics , the Source Code Repository at http://hg.theorchromo.ru/pyteomics , documentation at http://packages.python.org/pyteomics . Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/ . Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

  • pyteomics a python framework for exploratory data analysis and rapid software prototyping in proteomics
    Journal of the American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the Source Code Repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Mark V Ivanov - One of the best experts on this subject based on the ideXlab platform.

  • Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics
    Journal of The American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics , the Source Code Repository at http://hg.theorchromo.ru/pyteomics , documentation at http://packages.python.org/pyteomics . Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/ . Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

  • pyteomics a python framework for exploratory data analysis and rapid software prototyping in proteomics
    Journal of the American Society for Mass Spectrometry, 2013
    Co-Authors: Anton Goloborodko, Lev I Levitsky, Mark V Ivanov, Mikhail V Gorshkov
    Abstract:

    Pyteomics is a cross-platform, open-Source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the Source Code Repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Yan Liu - One of the best experts on this subject based on the ideXlab platform.

  • CD-MAKE - Detecting and Ranking API Usage Pattern in Large Source Code Repository: A LFM Based Approach
    Lecture Notes in Computer Science, 2017
    Co-Authors: Jitong Zhao, Yan Liu
    Abstract:

    Code examples are key reSources for helping programmers to learn correct Application Programming Interface (API) usages efficiently. However, most framework and library APIs fail in providing sufficient and adequate Code examples in corresponding official documentations. Thus, it takes great programmers’ efforts to browse and extract API usage examples from websites. To reduce such effort, this paper proposes a graph-based pattern-oriented mining approach, LFM-OUPD (Local fitness measure for detecting overlapping usage patterns) for API usage facility, that recommends proper API Code examples from data analytics. API method queries are accepted from programmers and corresponding Code files are collected from related API dataset. The detailed structural links among API method elements in conceptual Source Codes are captured and generate a Code graph structure. Lancichinetti et al. proposed an overlapping community detecting algorithm (Local fitness measure, LFM), based on the local optimization of a fitness function. In LFM-OUPD, a mining algorithm based on LFM is presented to explore the division of method sequences in the directed Source Code element graph and detect candidates of different API usage patterns. Then a ranking approach is applied to obtain appropriate API usage pattern and Code example candidates. A case study on Google Guava is conducted to evaluate the effectiveness of this approach.