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Contenido proporcionado por Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk). Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk) o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
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Adventures in the Biology trade : Bioinformatics in the Petabyte era (60 mins, ~42 MB)

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Manage episode 205984210 series 2307601
Contenido proporcionado por Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk). Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk) o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
Bioinformatics and more widely Computational Biology is a largely data-driven Science. The array of high-throughput technology platforms in the last 10 years mean that the amount of data being generated in this field is likely to enter into Exabytes by 2020. The challenges associated with this are quite different from the data sets generated by High Energy Physics or Astrophysics in that they tend to gathered from a wide variety of different providers. Meta-analyses of these data sets can give startling new insights but come with many caveats - in particular that the quality of the data from each provider can be highly variable. I will spend some time talking about one set of experiences I have dealing with one specific technology platform and in particular how it is clear that the detection of bias in data sets is a key element of any high-throughput analysis. This talk was given as part of our MSc in HPC's 'HPC Ecosystem' course.
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19 episodios

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Manage episode 205984210 series 2307601
Contenido proporcionado por Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk). Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Iain Bethune and Iain Bethune (ibethune@exseed.ed.ac.uk) o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
Bioinformatics and more widely Computational Biology is a largely data-driven Science. The array of high-throughput technology platforms in the last 10 years mean that the amount of data being generated in this field is likely to enter into Exabytes by 2020. The challenges associated with this are quite different from the data sets generated by High Energy Physics or Astrophysics in that they tend to gathered from a wide variety of different providers. Meta-analyses of these data sets can give startling new insights but come with many caveats - in particular that the quality of the data from each provider can be highly variable. I will spend some time talking about one set of experiences I have dealing with one specific technology platform and in particular how it is clear that the detection of bias in data sets is a key element of any high-throughput analysis. This talk was given as part of our MSc in HPC's 'HPC Ecosystem' course.
  continue reading

19 episodios

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