PMID- 27071189
OWN - NLM
STAT- MEDLINE
DCOM- 20171002
LR  - 20220408
IS  - 1557-9964 (Electronic)
IS  - 1545-5963 (Linking)
VI  - 13
IP  - 5
DP  - 2016 Sep-Oct
TI  - Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection 
      and Multiple Kernel Learning.
PG  - 825-835
AB  - Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with 
      very low median survival. In order to predict the patient's prognosis, 
      researchers have proposed rules to classify different glioma cancer cell 
      subtypes. However, survival time of different subtypes of GBM is often various 
      due to different individual basis. Recent development in gene testing has evolved 
      classic subtype rules to more specific classification rules based on single 
      biomolecular features. These classification methods are proven to perform better 
      than traditional simple rules in GBM prognosis prediction. However, the real 
      power behind the massive data is still under covered. We believe a combined 
      prediction model based on more than one data type could perform better, which 
      will contribute further to clinical treatment of GBM. The Cancer Genome Atlas 
      (TCGA) database provides huge dataset with various data types of many cancers 
      that enables us to inspect this aggressive cancer in a new way. In this research, 
      we have improved GBM prognosis prediction accuracy further by taking advantage of 
      the minimum redundancy feature selection method (mRMR) and Multiple Kernel 
      Machine (MKL) learning method. Our goal is to establish an integrated model which 
      could predict GBM prognosis with high accuracy.
FAU - Zhang, Ya
AU  - Zhang Y
FAU - Li, Ao
AU  - Li A
FAU - Peng, Chen
AU  - Peng C
FAU - Wang, Minghui
AU  - Wang M
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
DEP - 20160407
PL  - United States
TA  - IEEE/ACM Trans Comput Biol Bioinform
JT  - IEEE/ACM transactions on computational biology and bioinformatics
JID - 101196755
SB  - IM
MH  - Algorithms
MH  - Brain Neoplasms/classification/*diagnosis/*mortality
MH  - Decision Support Systems, Clinical
MH  - Diagnosis, Computer-Assisted/methods
MH  - Female
MH  - Glioblastoma/classification/*diagnosis/*mortality
MH  - Humans
MH  - *Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Pattern Recognition, Automated/*methods
MH  - Prevalence
MH  - Prognosis
MH  - Reproducibility of Results
MH  - Risk Assessment/methods
MH  - Sensitivity and Specificity
MH  - Survival Analysis
EDAT- 2016/04/14 06:00
MHDA- 2017/10/03 06:00
CRDT- 2016/04/13 06:00
PHST- 2016/04/14 06:00 [pubmed]
PHST- 2017/10/03 06:00 [medline]
PHST- 2016/04/13 06:00 [entrez]
AID - 10.1109/TCBB.2016.2551745 [doi]
PST - ppublish
SO  - IEEE/ACM Trans Comput Biol Bioinform. 2016 Sep-Oct;13(5):825-835. doi: 
      10.1109/TCBB.2016.2551745. Epub 2016 Apr 7.