Predicting cancer prognosis using functional genomics data sets.
Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA. ; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
Jishnu
J
Das
Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA.
Kaitlyn M
KM
Gayvert
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA. ; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
Haiyuan
H
Yu
cancer prognosis prediction; cellular networks; functional genomics; gene expression; somatic mutations