Ying-Ying Xu1, Fan Yang, Yang Zhang, Hong-Bin Shen. 1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
Abstract
MOTIVATION: Human cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer. RESULTS: To reveal the cancer-related protein mislocalizations, we developed an image-based multi-label subcellular location predictor, iLocator, which covers seven cellular localizations. The iLocator incorporates both global and local image descriptors and generates predictions by using an ensemble multi-label classifier. The algorithm has the ability to treat both single- and multiple-location proteins. We first trained and tested iLocator on 3240 normal human tissue images that have known subcellular location information from the human protein atlas. The iLocator was then used to generate protein localization predictions for 3696 protein images from seven cancer tissues that have no location annotations in the human protein atlas. By comparing the output data from normal and cancer tissues, we detected eight potential cancer biomarker proteins that have significant localization differences with P-value < 0.01. AVAILABILITY: http://www.csbio.sjtu.edu.cn/bioinf/iLocator/
MOTIVATION:Human cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer. RESULTS: To reveal the cancer-related protein mislocalizations, we developed an image-based multi-label subcellular location predictor, iLocator, which covers seven cellular localizations. The iLocator incorporates both global and local image descriptors and generates predictions by using an ensemble multi-label classifier. The algorithm has the ability to treat both single- and multiple-location proteins. We first trained and tested iLocator on 3240 normal human tissue images that have known subcellular location information from the human protein atlas. The iLocator was then used to generate protein localization predictions for 3696 protein images from seven cancer tissues that have no location annotations in the human protein atlas. By comparing the output data from normal and cancer tissues, we detected eight potential cancer biomarker proteins that have significant localization differences with P-value < 0.01. AVAILABILITY: http://www.csbio.sjtu.edu.cn/bioinf/iLocator/
Authors: Mathias Uhlen; Per Oksvold; Linn Fagerberg; Emma Lundberg; Kalle Jonasson; Mattias Forsberg; Martin Zwahlen; Caroline Kampf; Kenneth Wester; Sophia Hober; Henrik Wernerus; Lisa Björling; Fredrik Ponten Journal: Nat Biotechnol Date: 2010-12 Impact factor: 54.908
Authors: Tao Peng; Ghislain M C Bonamy; Estelle Glory-Afshar; Daniel R Rines; Sumit K Chanda; Robert F Murphy Journal: Proc Natl Acad Sci U S A Date: 2010-02-01 Impact factor: 11.205