scholarly journals Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaorui Chen ◽  
Xiaowen Huang ◽  
Diao Jie ◽  
Caifang Zheng ◽  
Xiliang Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Folic Acid ◽  
Environmental Factors ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Ann Model ◽  
Acid Therapy ◽  
Youden’S Index

AbstractArtificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954–5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092–7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden’s index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden’s index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.

Prediction model for the efficacy of folic acid therapy on hyperhomocysteinaemia based on genetic risk score methods

2019 ◽  
Vol 122 (1) ◽  
pp. 39-46
Author(s):  
Binghui Du ◽  
Chengda Zhang ◽  
Limin Yue ◽  
Bingnan Ren ◽  
Qinglin Zhao ◽  
...  
Keyword(s):  
Folic Acid ◽  
Prediction Model ◽  
Risk Score ◽  
Genetic Risk ◽  
Acid Treatment ◽  
Genetic Risk Score ◽  
Assessment Tools ◽  
Acid Therapy ◽  
Gene Environment ◽  
History Of

AbstractNo risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene–environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene–environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy.

scholarly journals Effects of Environmental Factors on Ozone Flux over a Wheat Field Modeled with an Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhilin Zhu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Environmental Factors ◽  
Crop Yields ◽  
Global Radiation ◽  
Training Data ◽  
Response Analysis ◽  
Ann Model ◽  
Wheat Field ◽  
Artificial Neural

Ozone (O3) flux-based indices are considered better than O3 concentration-based indices in assessing the effects of ground O3 on ecosystem and crop yields. However, O3 flux (Fo) measurements are often lacking due to technical reasons and environmental conditions. This hampers the calculation of flux-based indices. In this paper, an artificial neural network (ANN) method was attempted to simulate the relationships between Fo and environmental factors measured over a wheat field in Yucheng, China. The results show that the ANN-modeled Fo values were in good agreement with the measured Fo values. The R2 of an ANN model with 6 routine independent environmental variables exceeded 0.8 for training datasets, and the RMSE and MAE were 3.074 nmol·m−2·s and 2.276 nmol·m−2·s for test dataset, respectively. CO2 flux and water vapor flux have strong correlations with Fo and could improve the fitness of ANN models. Besides the combinations of included variables and selection of training data, the number of neurons is also a source of uncertainties in an ANN model. The fitness of the modeled Fo was sensitive to the neuron number when it ranged from 1 to 10. The ANN model consists of complex arithmetic expressions between Fo and independent variables, and the response analysis shows that the model can reflect their basic physical relationships and importance. O3 concentration, global radiation, and wind speed are the important factors affecting O3 deposition. ANN methods exhibit significant value for filling the gaps of Fo measured with micrometeorological methods.

scholarly journals Application of Machine Learning To Predict The Occurrence of Arrhythmia After Acute Myocardial Infarction

2021 ◽  
Author(s):  
suhuai Wang ◽  
jingjie Li ◽  
Lin Sun ◽  
Jianing Cai ◽  
Shihui Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Risk Score ◽  
Ann Model ◽  
Training Set ◽  
Artificial Neural ◽  
Testing Set

Abstract Background: Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).Methods: A total of 2084 patients with acute myocardial infarction were enrolled in this study. The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into training set (80%) and internal testing set (20%). Three machine learning algorithms (including decision tree, random forest, and artificial neural network) learn from the training set to build a model, use the testing set to evaluate the prediction performance, and compare it with the model built by the variable set involved GRACE risk score.Results：Three ML models predict the occurrence of tachyarrhythmia after AMI. After variable selection, the artificial neural network (ANN) model achieves the highest accuracy of 0.654 (95% CI, 0.625--0.683). The area under the value of the curve (AUC) is 0.597 (95% CI, 0.568-0.626). The highest accuracy of the model built using the Grace variable set is 0.627 (95% CI, 0.598-0.656), and the AUC value is 0.574 (95% CI, 0.545-0.603).Conclusions：We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research.Trial registration：Clinical Trial Registry No.: ChiCTR2100041960.

scholarly journals An examination of clinical differences between carriers and non-carriers of chromosome 8q24 risk alleles in a New Zealand Caucasian population with prostate cancer

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1731 ◽  
Author(s):  
Karen S. Bishop ◽  
Dug Yeo Han ◽  
Nishi Karunasinghe ◽  
Megan Goudie ◽  
Jonathan G. Masters ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Environmental Factors ◽  
Cancer Risk ◽  
Gleason Score ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Prostate Cancer Risk ◽  
Link Type ◽  
Chromosome 8Q24

Background.Prostate cancer makes up approximately 15% of all cancers diagnosed in men in developed nations and approximately 4% of cases in developing nations. Although it is clear that prostate cancer has a genetic component and single nucleotide polymorphisms (SNPs) can contribute to prostate cancer risk, detecting associations is difficult in multi-factorial diseases, as environmental and lifestyle factors also play a role. In this study, specific clinical characteristics, environmental factors and genetic risk factors were assessed for interaction with prostate cancer.Methods.A total of 489 prostate cancer cases and 427 healthy controls were genotyped for SNPs found on chromosome 8q24 and a genetic risk score was calculated. In addition the SNPs were tested for an association with a number of clinical and environmental factors.Results.Age and tobacco use were positively associated, whilst alcohol consumption was negatively associated with prostate cancer risk. The following SNPs found on chromosome 8q24 were statistically significantly associated with prostate cancer:rs10086908,rs16901979;rs1447295andrs4242382. No association between Gleason score and smoking status, or between Gleason score and genotype were detected.Conclusion.A genetic risk score was calculated based on the 15 SNPs tested and found to be significantly associated with prostate cancer risk. Smoking significantly contributed to the risk of developing prostate cancer, and this risk was further increased by the presence of four SNPs in the 8q24 chromosomal region.

scholarly journals Artificial Neural Network (ANN) Modeling Analysis of Algal Blooms in an Estuary with Episodic and Anthropogenic Freshwater Inputs

2021 ◽  
Vol 11 (15) ◽  
pp. 6921
Author(s):  
Sangjun Park ◽  
Yongsik Sin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Environmental Factors ◽  
Chlorophyll A ◽  
Algal Blooms ◽  
River Estuary ◽  
Freshwater Discharge ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The Youngsan River estuary, located on the southwest coast of South Korea, has transitioned from a natural to an artificial estuary since dike construction in 1981 separated freshwater and seawater zones. This artificial transition has induced changes in the physical properties and circulation within the estuary, which has led to hypoxia and algal blooms. In this study, an artificial neural network (ANN) model was employed to simulate phytoplankton variations, including algal blooms and size fractions based on chlorophyll a, using data obtained by long-term monitoring (2008–2018) of the seawater zone of the Youngsan River estuary. The model was validated through statistical analyses, and the validated model was used to determine the contribution of the environmental factors on size-fractionated phytoplankton variations. The statistical validation of the model showed extremely low sum square error (SSE ≤ 0.0003) and root mean square error (RMSE ≤ 0.0173) values, with R2 ≥ 0.9952. The accuracy of the model predictions was high, despite the considerable irregularity and wide range of phytoplankton variations in the estuary. With respect to phytoplankton size structure, the contribution of seasonal environmental factors such as water temperature and solar radiation was high for net-sized chlorophyll a, whereas the contribution of factors such as freshwater discharge and salinity was high for nano-sized chlorophyll a, which includes typical harmful algae. Notably, because the Youngsan River estuary is influenced by a monsoon climate—characterized by high precipitation in summer—the contribution of freshwater discharge to harmful algal blooms is predicted to increase during this period. Our results suggest that the ANN model can be an important tool for understanding the influence of freshwater discharge, which is essential for managing algal blooms and maintaining the ecosystem health of altered estuaries.

1535-P: An Insulin Resistance Genetic Risk Score (IR_GRS) Is Associated with Mortality in Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1535-P
Author(s):  
RACHEL G. MILLER ◽  
TINA COSTACOU ◽  
SUNA ONENGUT-GUMUSCU ◽  
WEI-MIN CHEN ◽  
STEPHEN S. RICH ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

Sign in / Sign up

Export Citation Format

Share Document