Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
-
Making Trillion Correlations Feasible in Feature Grouping and Selection
Y Zhai, YS Ong, IWH Tsang. Cited by 13
-
Can Cluster-Boosted Regression Improve Prediction of Death and Length of Stay in the ICU?
M Rouzbahman, A Jovičić, M Chignell. Cited by 39
-
Data mining approach for automatic discovering success factors relationship statements in full text articles
W Krathu, P Padungweang, C Nukoolkit. Cited by 3
-
Multiplex methods provide effective integration of multi-omic data in genome-scale models
C Angione, M Conway, P Lió. Cited by 58
-
Predictive analytics of environmental adaptability in multi-omic network models
C Angione, P Lió. Cited by 57
-
BESC industry forum [6 abstracts]
Akpher, X Zhang, L Zhang, Z Zhu, G Xu, I King
-
NSPRING: the SPRING extension for subsequence matching of time series supporting normalization
X Gong, SJ Fong, JH Chan, S Mohammed. Cited by 9
-
Machine Learning Approaches for Cognitive State Classification and Brain Activity Prediction: A Survey
S Parida, S Dehuri, SB Cho. Cited by 3
-
Budget constrained non-monotonic feature selection
H Yang, Z Xu, MRT Lyu, I King. Cited by 10
-
A data-driven approach for online adaptation of game difficulty
H Yin, L Luo, W Cai, YS Ong, J Zhong. Cited by 13
-
Efficient Variation-Based Feature Selection for Medical Data Classification
SJ Fong, J Liang, SWI Siu, JH Chan. Cited by 1
-
Clustering-based gene-subnetwork biomarker identification using gene expression data
N Doungpan, W Engchuan, A Meechai, JH Chan