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  "Title": "Peak Detection for Mass Spectrometry data using wavelet-based\nalgorithms",
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  "Date": "2025-05-03",
  "Authors@R": "c(\nperson(given = \"Pan\",\nfamily = \"Du\",\nrole = c(\"aut\")),\nperson(given = \"Warren\",\nfamily = \"Kibbe\",\nrole = c(\"aut\")),\nperson(given = \"Simon\",\nfamily = \"Lin\",\nrole = c(\"aut\")),\nperson(given = \"Sergio\",\nfamily = \"Oller Moreno\",\nemail = \"sergioller@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8994-1549\"))\n)",
  "Description": "Peak Detection in Mass Spectrometry data is one of the\nimportant preprocessing steps. The performance of peak\ndetection affects subsequent processes, including protein\nidentification, profile alignment and biomarker identification.\nUsing Continuous Wavelet Transform (CWT), this package provides\na reliable algorithm for peak detection that does not require\nany type of smoothing or previous baseline correction method,\nproviding more consistent results for different spectra. See\n<doi:10.1093/bioinformatics/btl355} for further details.",
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      "page": "MassSpecWavelet-package",
      "title": "Peak detection of mass spectrum by Wavelet transform based methods",
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        "MassSpecWavelet-package",
        "MassSpecWavelet"
      ]
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      "title": "Continuous Wavelet Transform (CWT)",
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      ]
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      "topics": [
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      ]
    },
    {
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      "title": "Find local maxima and return the size of the window where they are maximum.",
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        "findLocalMaxWinSize"
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    },
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    },
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      "topics": [
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    },
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      "topics": [
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    },
    {
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      "title": "Identify peaks based on the ridges in 2-D CWT coefficient matrix",
      "topics": [
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