scholarly journals Machine Learning-Based DNA Methylation Score for Fetal Exposure to Maternal Smoking: Development and Validation in Samples Collected from Adolescents and Adults

2020 ◽  
Vol 128 (9) ◽  
pp. 097003
Author(s):  
Sebastian Rauschert ◽  
Phillip E. Melton ◽  
Anni Heiskala ◽  
Ville Karhunen ◽  
Graham Burdge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Maternal Smoking ◽  
Fetal Exposure ◽  
Methylation Score ◽  
Adolescents And Adults ◽  
Development And Validation

scholarly journals DNA Methylation Score as a Biomarker in Newborns for Sustained Maternal Smoking during Pregnancy

2017 ◽  
Vol 125 (4) ◽  
pp. 760-766 ◽  
Author(s):  
Sarah E. Reese ◽  
Shanshan Zhao ◽  
Michael C. Wu ◽  
Bonnie R. Joubert ◽  
Christine L. Parr ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Maternal Smoking ◽  
Smoking During Pregnancy ◽  
Methylation Score ◽  
Maternal Smoking During Pregnancy

Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Genes

2019 ◽  
Author(s):  
Yi Bai ◽  
Chunlian Wei ◽  
Yuxin Zhong ◽  
Junyu Long ◽  
Shan Huang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Development And Validation

scholarly journals The development and validation of EpiComet-Chip, a modified high-throughput comet assay for the assessment of DNA methylation status

2017 ◽  
Vol 58 (7) ◽  
pp. 508-521 ◽  
Author(s):  
Todd A. Townsend ◽  
Marcus C. Parrish ◽  
Bevin P. Engelward ◽  
Mugimane G. Manjanatha
Keyword(s):  
Dna Methylation ◽  
Comet Assay ◽  
High Throughput ◽  
Methylation Status ◽  
Development And Validation

Development and validation of simplified machine learning algorithms to predict prognosis of hospitalized COVID-19 patients: a multi-center, retrospective study (Preprint)

2021 ◽  
Author(s):  
Fang He ◽  
John H Page ◽  
Kerry R Weinberg ◽  
Anirban Mishra
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
High Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
High Risk Patients ◽  
Risk Patients ◽  
Development And Validation

BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained setting, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients, however there are few risk scores derived from a substantially large EHR dataset, using simplified predictors as input. OBJECTIVE To develop and validate simplified machine learning algorithms which predicts COVID-19 adverse outcomes, to evaluate the AUC (area under the receiver operating characteristic curve), sensitivity, specificity and calibration of the algorithms, to derive clinically meaningful thresholds. METHODS We conducted machine learning model development and validation via cohort study using multi-center, patient-level, longitudinal electronic health records (EHR) from Optum® COVID-19 database which provides anonymized, longitudinal EHR from across US. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, ICU admission, respiratory failure, mechanical ventilator usages at inpatient setting. Data from patients who were admitted prior to Sep 7, 2020, is randomly sampled into development, test and validation datasets; data collected from Sep 7, 2020 through Nov 15, 2020 was reserved as prospective validation dataset. RESULTS Of 3.7M patients in the analysis, a total of 585,867 patients were diagnosed or tested positive for SARS-CoV-2; and 50,703 adult patients were hospitalized with COVID-19 between Feb 1 and Nov 15, 2020. Among the study cohort (N=50,703), there were 6,204 deaths, 9,564 ICU admissions, 6,478 mechanically ventilated or EMCO patients and 25,169 patients developed ARDS or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC = 0.89 (0.89 - 0.89) on validation dataset (N=10,752)), consistent prediction through the second wave of pandemic from September to November (AUC = 0.85 (0.85 - 0.86) on post-development validation (N= 14,863)), great clinical relevance and utility. Besides, a comprehensive 386 input covariates from baseline and at admission was included in the analysis; the end-to-end pipeline automates feature selection and model development process, producing 10 key predictors as input such as age, blood urea nitrogen, oxygen saturation, which are both commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validations demonstrate consistent model performance to predict even beyond the time period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated and reliable prediction model based on only ten clinical features as a prognostic tool to stratifying COVID-19 patients into intermediate, high and very high-risk groups. This simple predictive tool could be shared with a wider healthcare community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize healthcare resources. CLINICALTRIAL N/A

scholarly journals Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
José M. Lezcano-Valverde ◽  
Fernando Salazar ◽  
Leticia León ◽  
Esther Toledano ◽  
Juan A. Jover ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Predictive Model ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Development And Validation

scholarly journals Creating and validating a DNA methylation-based proxy for Interleukin-6

2020 ◽  
Author(s):  
Anna J Stevenson ◽  
Danni A Gadd ◽  
Robert Francis Hillary ◽  
Daniel L. McCartney ◽  
Archie Campbell ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cognitive Functioning ◽  
Cognitive Ability ◽  
Chronic Inflammation ◽  
Interleukin 6 ◽  
Epigenetic Mechanism ◽  
Inverse Association ◽  
Age Related ◽  
Independent Test ◽  
Methylation Score

Chronic inflammation is a pervasive feature of ageing and may be linked to age-related cognitive decline. However, population studies evaluating its relationship with cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals' persisting levels of inflammation. The epigenetic mechanism DNA methylation has shown utility in indexing environmental exposures and could potentially be leveraged to provide proxy signatures of chronic inflammation. We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 895 older adults (mean age: 69 years) to develop a DNA methylation-based predictor. The predictor was tested in an independent cohort (n=7,028 [417 with measured IL-6], mean age: 51 years).We examined the association between the DNA methylation IL-6 score and serum IL-6, its association with age and established correlates of circulating IL-6, and with cognitive ability. A weighted score from 12 DNA methylation sites optimally predicted IL-6 (independent test set R2=5.1%). In the independent test cohort, both measured IL-6, and the DNA methylation proxy, increased as a function of age (serum IL-6: n=417, β=0.02, SE=0.004 p=1.3x10-7; DNAm IL-6 score: n=7,028, β=0.02, SE=0.0009, p<2x10-16). Serum IL-6 was not found to associate with cognitive ability (n=417, β=-0.06, SE=0.05, p=0.19); however, an inverse association was identified between the DNA methylation score and cognitive functioning (n=7,028, β=-0.14, SE=0.02, pFDR=1.5x10-14). These results suggest DNA methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for reliable insights into the relationship between chronic inflammation and pertinent health outcomes.

Sign in / Sign up

Export Citation Format

Share Document