Massive parallelism for non-linear and non-stationary data analysis with GPGPU

2016 ◽  
Author(s):  
Chun-Chieh Chen ◽  
Chih-Ya Shen ◽  
Ming-Syan Chen
Keyword(s):  
Data Analysis ◽  
Massive Parallelism ◽  
Non Linear

Ridgetail White Prawn (Exopalaemon carinicauda) K Value Predicting Method by Using Electronic Nose Combined with Non-linear Data Analysis Model

2018 ◽  
Vol 11 (11) ◽  
pp. 3121-3129 ◽  
Author(s):  
Chenning Shao ◽  
Haonan Zheng ◽  
Zhixin Zhou ◽  
Jian Li ◽  
Xiongwei Lou ◽  
...  
Keyword(s):  
Data Analysis ◽  
Electronic Nose ◽  
Exopalaemon Carinicauda ◽  
Analysis Model ◽  
K Value ◽  
Non Linear

Metabolomics signature in serum months to years before thoracic cancer: The HUNT study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23059-e23059
Author(s):  
Oluf D. Røe ◽  
Vincenzo Lagani ◽  
Hans Fredrik Kvitvang ◽  
Maria Markaki ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Analysis ◽  
Cross Validation ◽  
Performance Estimation ◽  
Serum Samples ◽  
Analysis Pipeline ◽  
Early Cancer Diagnosis ◽  
Non Linear ◽  
Hunt Study ◽  
Data Analysis Pipeline

e23059 Background: The Cancer-Biomarkers in HUNTinitiative seeks to identify novel biomarkers for the early cancer diagnosis. For lung cancers and mesothelioma clinically useful early markers are not available. In the prospective HUNT study in Norway, pre-diagnostic samples ranging 0-20 years before diagnosis are available for research purposes. Here we present our first results on high-throughput metabolomics analysis in serum two months to 16 years before diagnosis. Methods: LC-MS untargeted (Amide-) metabolites (n = 1042) were profiled in serum samples from 48 future patients (12 each of adeno-, squamous cell carcinoma, small-cell lung cancer and mesothelioma) and from 48 controls that were cancer-free 5 years after blood sampling. All were active smokers. Metabolic features for (a) each cancer and (b) all cancers pooled together were analyzed with moderated t-test (R limma package). Multivariate analyses included (a) OPLS-DA and (b) signature identification through a data-analysis pipeline that includes feature selection (such as the algorithm in [1]), non-linear modelers (e.g., Random Forests) and Cross-Validation with bootstrapping [2] for optimizing algorithms and providing unbiased performance estimation. The pipeline is implemented in the Just Add Data software (Gnosis Data Analysis). Results: Univariate and OPLS-DA analyses did not identify any association between metabolites and cancer. The non-linear data analysis pipeline identified a signature containing five metabolites able to discriminate between cancer and non-cancer patients, statistically significantly better than random (AUC = 0.667, CI = [0.536, 0.784]). Conclusions: Our results indicate that metabolic profiling in serum may help in identifying subjects who are likely to be diagnosed with lung cancer/mesothelioma in a time period of several years before diagnosis. More data will be presented at the annual meeting. Further validation studies are planned for confirming the replicability of these findings. 1) Lagani V et al., 2016. arXiv:1611.03227 2) Greasidou L, 2017. Bias Correction of the Cross-Validation Performance Estimate and Speed Up of its Execution Time, MSc Thesis, University of Crete

Non-linear kinetics of ethanol elimination in man: Medico-legal applications of the terminal concentration-time data analysis

1983 ◽  
Vol 21 (3) ◽  
pp. 207-213 ◽  
Author(s):  
R. Bruno ◽  
A. Iliadis ◽  
M.J. Treffot ◽  
B. Mariotti ◽  
J.P. Cano ◽  
...  
Keyword(s):  
Data Analysis ◽  
Time Data ◽  
Concentration Time ◽  
Linear Kinetics ◽  
Non Linear ◽  
Ethanol Elimination ◽  
Kinetics Of
Sign in / Sign up

Export Citation Format

Share Document