scholarly journals Prediction of Lung Function in Adolescence Using Epigenetic Aging: A Machine Learning Approach

2020 ◽  
Vol 3 (4) ◽  
pp. 77
Author(s):  
Md Adnan Arefeen ◽  
Sumaiya Tabassum Nimi ◽  
M. Sohel Rahman ◽  
S. Hasan Arshad ◽  
John W. Holloway ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Function ◽  
Forced Expiratory Volume ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Time Points ◽  
Learning Techniques ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Lung Health

Epigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines whether lung function in adolescence can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over the life span can be beneficial to assess the lung health in adolescence.

scholarly journals Prediction of Lung Function in Adolescence Using Epigenetic Aging: A Machine Learning Approach

2020 ◽  
Author(s):  
Md. Adnan Arefeen ◽  
Sumaiya Tabassum Nimi ◽  
M. Sohel Rahman ◽  
S. Hasan Arshad ◽  
John W. Holloway ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Function ◽  
Forced Expiratory Volume ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Time Points ◽  
Learning Techniques ◽  
Epigenetic Aging ◽  
Epigenetic Age ◽  
Lung Health

Epigenetic aging has been found associated with a number of phenotypes and diseases. Few studies investigated its effect on lung function in relatively older people. However, this effect has not been explored in younger population. This study examines whether lung function at adolescent can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (Forced Expiratory Volume in one second) and FVC (Forced Vital Capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over life span can be beneficial to assess the lung health in adolescence.

scholarly journals Metabolomics of World Trade Center-Lung Injury: a machine learning approach

2018 ◽  
Vol 5 (1) ◽  
pp. e000274 ◽  
Author(s):  
George Crowley ◽  
Sophia Kwon ◽  
Syed Hissam Haider ◽  
Erin J Caraher ◽  
Rachel Lam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Function ◽  
Lung Injury ◽  
World Trade ◽  
World Trade Center ◽  
Forced Expiratory Volume ◽  
Machine Learning Techniques ◽  
Maximal Class ◽  
Class Separation ◽  
Trade Center

IntroductionBiomarkers of metabolic syndrome expressed soon after World Trade Center (WTC) exposure predict development of WTC Lung Injury (WTC-LI). The metabolome remains an untapped resource with potential to comprehensively characterise many aspects of WTC-LI. This case–control study identified a clinically relevant, robust subset of metabolic contributors of WTC-LI through comprehensive high-dimensional metabolic profiling and integration of machine learning techniques.MethodsNever-smoking, male, WTC-exposed firefighters with normal pre-9/11 lung function were segregated by post-9/11 lung function. Cases of WTC-LI (forced expiratory volume in 1s <lower limit of normal, n=15) and controls (n=15) were identified from previous cohorts. The metabolome of serum drawn within 6 months of 9/11 was quantified. Machine learning was used for dimension reduction to identify metabolites associated with WTC-LI.Results580 metabolites qualified for random forests (RF) analysis to identify a refined metabolite profile that yielded maximal class separation. RF of the refined profile correctly classified subjects with a 93.3% estimated success rate. 5 clusters of metabolites emerged within the refined profile. Prominent subpathways include known mediators of lung disease such as sphingolipids (elevated in cases of WTC-LI), and branched-chain amino acids (reduced in cases of WTC-LI). Principal component analysis of the refined profile explained 68.3% of variance in five components, demonstrating class separation.ConclusionAnalysis of the metabolome of WTC-exposed 9/11 rescue workers has identified biologically plausible pathways associated with loss of lung function. Since metabolites are proximal markers of disease processes, metabolites could capture the complexity of past exposures and better inform treatment. These pathways warrant further mechanistic research.

scholarly journals Predictive and interpretable models via the stacked elastic net

2020 ◽  
Author(s):  
Armin Rauschenberger ◽  
Enrico Glaab ◽  
Mark van de Wiel
Keyword(s):  
Machine Learning ◽  
R Package ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Biomedical Sciences ◽  
Molecular Features ◽  
Learning Techniques ◽  
Meta Learning ◽  
Interpretable Models

Abstract Motivation Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and Implementation The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet. Supplementary information Supplementary data are available at Bioinformatics online.

Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study

2020 ◽  
pp. 1-9
Author(s):  
Diego Librenza-Garcia ◽  
Ives Cavalcante Passos ◽  
Jacson Gabriel Feiten ◽  
Paulo A. Lotufo ◽  
Alessandra C. Goulart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Adult Health ◽  
Recurrent Depression ◽  
Elastic Net Regularization ◽  
Diagnosis And Prognosis ◽  
Learning Techniques ◽  
Occupational Cohort

Abstract Background Depression is highly prevalent and marked by a chronic and recurrent course. Despite being a major cause of disability worldwide, little is known regarding the determinants of its heterogeneous course. Machine learning techniques present an opportunity to develop tools to predict diagnosis and prognosis at an individual level. Methods We examined baseline (2008–2010) and follow-up (2012–2014) data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), a large occupational cohort study. We implemented an elastic net regularization analysis with a 10-fold cross-validation procedure using socioeconomic and clinical factors as predictors to distinguish at follow-up: (1) depressed from non-depressed participants, (2) participants with incident depression from those who did not develop depression, and (3) participants with chronic (persistent or recurrent) depression from those without depression. Results We assessed 15 105 and 13 922 participants at waves 1 and 2, respectively. The elastic net regularization model distinguished outcome levels in the test dataset with an area under the curve of 0.79 (95% CI 0.76–0.82), 0.71 (95% CI 0.66–0.77), 0.90 (95% CI 0.86–0.95) for analyses 1, 2, and 3, respectively. Conclusions Diagnosis and prognosis related to depression can be predicted at an individual subject level by integrating low-cost variables, such as demographic and clinical data. Future studies should assess longer follow-up periods and combine biological predictors, such as genetics and blood biomarkers, to build more accurate tools to predict depression course.

Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Önder Özgür ◽  
Uğur Akkoç
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Elastic Net ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Shrinkage Methods ◽  
Inflation Forecasting ◽  
Turkish Economy ◽  
Learning Techniques

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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