Browsing Department of Biomedicine by Subject "Bioinformatics"

Now showing items 1-7 of 7

    • Colorectal cancer derived organotypic spheroids maintain essential tissue characteristics but adapt their metabolism in culture 

      Rajcevic, Uros; Knol, Jaco C; Piersma, Sander; Bougnaud, Sébastien; Fack, Fred; Sundlisæter, Eirik; Søndenaa, Karl; Myklebust, Reidar; Pham, Thang V.; Niclou, Simone P.; Jiménez, Connie R. (Peer reviewed; Journal article, 2014-07-11)
      Background: Organotypic tumor spheroids, a 3D in vitro model derived from patient tumor material, preserve tissue heterogeneity and retain structural tissue elements, thus replicating the in vivo tumor more closely than ...

    • Crowdsourcing in proteomics: public resources lead to better experiments 

      Barsnes, Harald; Martens, Lennart (Peer reviewed; Journal article, 2013-04)
      With the growing interest in the field of proteomics, the amount of publicly available proteome resources has also increased dramatically. This means that there are many useful resources available for almost all aspects ...

    • Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory 

      Verheggen, Kenneth; Barsnes, Harald; Martens, Lennart (Peer reviewed; Journal article, 2014-03)
      Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, ...

    • Exploring the potential of public proteomics data 

      Vaudel, Marc; Verheggen, Kenneth; Csordas, Attila; Ræder, Helge; Berven, Frode; Martens, Lennart; Vizcaíno, Juan Antonio; Barsnes, Harald (Peer reviewed; Journal article, 2016)
      In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has ...

    • Machine learning applications in proteomics research: How the past can boost the future 

      Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, S; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart (Peer reviewed; Journal article, 2014)
      Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution ...

    • Shedding light on black boxes in protein identification 

      Vaudel, Marc; Venne, A. Saskia; Berven, Frode; Zahedi, René P.; Martens, Lennart; Barsnes, Harald (Peer reviewed; Journal article, 2014-05)
      Performing a well thought-out proteomics data analysis can be a daunting task, especially for newcomers to the field. Even researchers experienced in the proteomics field can find it challenging to follow existing publication ...

    • Viewing the proteome: How to visualize proteomics data? 

      Oveland, Eystein; Muth, Thilo; Rapp, Erdmann; Martens, Lennart; Berven, Frode; Barsnes, Harald (Peer reviewed; Journal article, 2015-04)
      Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily ...