TY - JOUR AU - Wu, Dingming AU - Wang, Dongfang AU - Zhang, Michael Q. AU - Gu, Jin PY - 2015 DA - 2015/12/01 TI - Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification JO - BMC Genomics SP - 1022 VL - 16 IS - 1 AB - One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. SN - 1471-2164 UR - https://doi.org/10.1186/s12864-015-2223-8 DO - 10.1186/s12864-015-2223-8 ID - Wu2015 ER -