S88 Pancreatic Hormones Response-Generated Machine Learning Model Help Distinguish Sporadic Pancreatic Cancer From New-Onset Diabetes Cohort

Background Maturity-onset diabetes of the young (MODY) is a group of dominantly inherited monogenic diabetes, with HNF4A-MODY, GCK-MODY, and HNF1A-MODY as the three most common forms based on the causal genes. Molecular diagnosis of MODY is important for precise treatment. Although a DNA variant causing MODY can be assessed based on the criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines, gene-specific assessment of disease-causing mutations is important to differentiate among MODY subtypes. As the ACMG criteria were not originally designed for machine-learning algorithms, they are not true independent variables. Objective The aim of this study was to develop machine-learning models for interpretation of DNA variants and MODY diagnosis using the ACMG criteria. Methods We applied machine-learning models for interpretation of DNA variants in MODY genes defined by the ACMG criteria based on the Human Gene Mutation Database (HGMD) and ClinVar database. Results With a machine-learning procedure, we found that the weight matrix of the ACMG criteria was significantly different between the three MODY genes HNF1A, HNF4A, and GCK. The models showed high predictive abilities with accuracy over 95%. Conclusions Our results highlight the need for applying different weights of the ACMG criteria in relation to different MODY genes for accurate functional classification. As proof of principle, we applied the ACMG criteria as feature vectors in a machine-learning model and obtained a precision-based result.

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Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development (Preprint)

10.2196/preprints.20506 ◽

2020 ◽

Author(s):

Yichuan Liu ◽

Hui-Qi Qu ◽

Adam S Wenocur ◽

Jingchun Qu ◽

Xiao Chang ◽

...

Keyword(s):

Machine Learning ◽

Learning Model ◽

Medical Genetics ◽

Maturity Onset Diabetes ◽

Learning Models ◽

Onset Diabetes ◽

Machine Learning Model ◽

Dna Variants ◽

Genetics And Genomics ◽

Machine Learning Models

BACKGROUND Maturity-onset diabetes of the young (MODY) is a group of dominantly inherited monogenic diabetes, with HNF4A-MODY, GCK-MODY, and HNF1A-MODY as the three most common forms based on the causal genes. Molecular diagnosis of MODY is important for precise treatment. Although a DNA variant causing MODY can be assessed based on the criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines, gene-specific assessment of disease-causing mutations is important to differentiate among MODY subtypes. As the ACMG criteria were not originally designed for machine-learning algorithms, they are not true independent variables. OBJECTIVE The aim of this study was to develop machine-learning models for interpretation of DNA variants and MODY diagnosis using the ACMG criteria. METHODS We applied machine-learning models for interpretation of DNA variants in MODY genes defined by the ACMG criteria based on the Human Gene Mutation Database (HGMD) and ClinVar database. RESULTS With a machine-learning procedure, we found that the weight matrix of the ACMG criteria was significantly different between the three MODY genes HNF1A, HNF4A, and GCK. The models showed high predictive abilities with accuracy over 95%. CONCLUSIONS Our results highlight the need for applying different weights of the ACMG criteria in relation to different MODY genes for accurate functional classification. As proof of principle, we applied the ACMG criteria as feature vectors in a machine-learning model and obtained a precision-based result.

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Pancreatic hormones response-generated machine learning model to help distinguish sporadic pancreatic cancer from new-onset diabetes cohort.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16235 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16235-e16235

Author(s):

Jiantong Bao ◽

Chenyu Sun ◽

Yidi Zhang ◽

Ling Li ◽

Stephen Jacob Pandol ◽

...

Keyword(s):

Machine Learning ◽

Pancreatic Cancer ◽

Insulin Secretion ◽

Insulin Sensitivity ◽

Random Forest ◽

Machine Learning Algorithms ◽

Pancreatic Hormones ◽

C Peptide ◽

Discriminative Model ◽

New Onset

e16235 Background: Diabetes mellitus (DM), a paraneoplastic phenomenon, can develop earlier than other symptoms in pancreatic cancer (PaC) patients. Enhanced surveillance is encouraged on all elderly patients with new-onset DM. However, it is a challenge to differentiate newly developed PaCDM from type 2 DM (T2DM). Thus, we investigated the differences of pancreatic hormones responses and functions between PaCDM and T2DM patients, and developed discriminative model by machine learning algorithms. Methods: PaC patients with normal blood glucose (PaCNG) or with new-onset DM (PaCDM) were recruited. For each case, age and gender matched newly developed T2DM patients and healthy volunteers were selected as controls. After ≥10 hours fasting, all participants underwent a mixed meal stimulation test (MMTT). Blood samples were collected at 0, 15, 30, 60 and 120 min to measure insulin, C-peptide, glucagon, and pancreatic polypeptide (PP). Indices of insulin sensitivity (HOMA-IS, HOMA-IR) and insulin secretion (HOMA-β, insulinogenic index 30’ and 120’) were calculated. Increases in hormone levels were compared among groups with repeated measure analysis. Four machine learning algorithms (Random Forest, Logistic Regression, Support Vector Machines, Naïve bayes) were used to develop quadri-separated discriminative models of PaCDM based on baseline characteristics, pancreatic hormones and insulin indices listed above. Results: Insulin and C-peptide responses to MMTT were blunted in PaCDM patients compared to T2DM. The AUC of insulin were comparatively lower in PaCDM; between-group differences were observed at the fasting (197.15 ± 16.59 pg/mL to 537.96 ± 118.69 pg/mL; P = 0.040) and 15 min (523.94 ± 81.15 pg/mL to 1182.51 ± 219.35 pg/mL; P = 0.036) time-points. No statistical differences among groups were found for glucagon. The mean peak PP concentration after MMTT in PaCDM group (466.67 ± 79.05 pg/mL) was higher than control group (258.54 ± 31.36 pg/mL, P = 0.034), but not statistically different to T2DM patients (452.34 ± 62.96 pg/mL, P = 0.892). PaCDM patients had lower insulin secretion capacity but better insulin sensitivity compared to T2DM patients. Eight indices (age, HbA1c, CA19-9, peak concentration of glucose, area above basal of PP, HOMA-IR, HOMA-IS, HOMA-β) were recruited for model development. And the discriminative model generated by random forest algorithm obtained best performance (AUC = 1.000, CA = 0.963, F-1 = 0.941, Precision = 0.889, Recall = 1.000, Specificity = 0.947; model verified). Conclusions: PaCDM patients tend to present with lower β-cell function and better insulin resistance compared to T2DM patients. As our model based on machine learning algorithm generates a good result for discrimination, the above findings may help with early screening for sporadic PaC in new-onset DM. Clinical trial information: ChiCTR1800018247.

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Design of Machine Learning Model for Urban Planning and Management Improvement

International Journal of Performability Engineering ◽

10.23940/ijpe.20.06.p14.958967 ◽

2020 ◽

Vol 16 (6) ◽

pp. 958 ◽

Cited By ~ 1

Author(s):

Zhou Jiafeng ◽

Liu Tian ◽

Zou Lin

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Learning Model ◽

Planning And Management ◽

Machine Learning Model ◽

Urban Planning And Management ◽

Management Improvement

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A Novel Machine Learning Model for Early Operational Anomaly Detection Using LWD/MWD Data

10.2523/iptc-19230-ms ◽

2019 ◽

Author(s):

Mohammed Al-Ghazal ◽

Viranchi Vedpathak

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Model ◽

Machine Learning Model

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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10.26434/chemrxiv.7411172 ◽

2018 ◽

Author(s):

Steen Lysgaard ◽

Paul C. Jennings ◽

Jens Strabo Hummelshøj ◽

Thomas Bligaard ◽

Tejs Vegge

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Au Nanoparticles ◽

Learning Model ◽

Energy Calculations ◽

Atomic Distribution ◽

Machine Learning Model ◽

Fold Reduction ◽

Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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