scholarly journals A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier

2021 ◽  
Vol 9 ◽  
Author(s):  
Juan I. Di Filippo ◽  
Mariela Bollini ◽  
Claudio N. Cavasotto
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Placental Transfer ◽  
Learning Model ◽  
Structural Features ◽  
Placental Barrier ◽  
Analysis Model ◽  
Drug Transfer ◽  
Linear Discriminant ◽  
Machine Learning Model

The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns.

scholarly journals Single Cell Raman Spectroscopy to Identify Different Stages of Proliferating Human Hepatocytes for Cell Therapy

2021 ◽  
Author(s):  
Chen Ma ◽  
Ludi Zhang ◽  
Ting He ◽  
Huiying Cao ◽  
Chenhui Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Cell Therapy ◽  
Linear Discriminant Analysis ◽  
Learning Model ◽  
Human Hepatocytes ◽  
Linear Discriminant ◽  
Analytical Technique ◽  
Machine Learning Model

Abstract Background: Cell therapy provides hope for treatment of advanced liver failure. Proliferating human hepatocytes (ProliHHs) were derived from primary human hepatocytes (PHH) and as potential alternative for cell therapy in liver diseases. Due to the continuous decline of mature hepatic genes and increase of progenitor like genes during ProliHHs expanding, it is challenge to monitor the critical changes of the whole process. Raman microspectroscopy is a noninvasive, label free analytical technique with high sensitivity capacity. In this study, we evaluated the potential and feasibility to identify ProliHHs from PHH with Raman spectroscopy.Methods: Raman spectra were collected at least 600 single spectrum for PHH and ProliHHs at different stages (Passage 1 to Passage 4). Linear discriminant analysis and a two-layer machine learning model were used to analyze the Raman spectroscopy data. Significant differences in Raman bands were validated by the associated conventional kits.Results: Linear discriminant analysis successfully classified ProliHHs at different stages and PHH. A two-layer machine learning model was established and the overall accuracy was at 84.6%. Significant differences in Raman bands have been found within different ProliHHs cell groups, especially changes at 1003 cm-1, 1206 cm-1 and 1300 cm-1. These changes were linked with reactive oxygen species, hydroxyproline and triglyceride levels in ProliHHs, and the hypothesis were consistent with the corresponding assay results. Conclusions: In brief, Raman spectroscopy was successfully employed to identify different stages of ProliHHs during dedifferentiation process. The approach can simultaneously trace multiple changes of cellular components from somatic cells to progenitor cells.

scholarly journals Single cell Raman spectroscopy to identify different stages of proliferating human hepatocytes for cell therapy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chen Ma ◽  
Ludi Zhang ◽  
Ting He ◽  
Huiying Cao ◽  
Xiongzhao Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Cell Therapy ◽  
Linear Discriminant Analysis ◽  
Learning Model ◽  
Human Hepatocytes ◽  
Linear Discriminant ◽  
Analytical Technique ◽  
Machine Learning Model

Abstract Background Cell therapy provides hope for treatment of advanced liver failure. Proliferating human hepatocytes (ProliHHs) were derived from primary human hepatocytes (PHH) and as potential alternative for cell therapy in liver diseases. Due to the continuous decline of mature hepatic genes and increase of progenitor like genes during ProliHHs expanding, it is challenge to monitor the critical changes of the whole process. Raman microspectroscopy is a noninvasive, label free analytical technique with high sensitivity capacity. In this study, we evaluated the potential and feasibility to identify ProliHHs from PHH with Raman spectroscopy. Methods Raman spectra were collected at least 600 single spectrum for PHH and ProliHHs at different stages (Passage 1 to Passage 4). Linear discriminant analysis and a two-layer machine learning model were used to analyze the Raman spectroscopy data. Significant differences in Raman bands were validated by the associated conventional kits. Results Linear discriminant analysis successfully classified ProliHHs at different stages and PHH. A two-layer machine learning model was established and the overall accuracy was at 84.6%. Significant differences in Raman bands have been found within different ProliHHs cell groups, especially changes at 1003 cm−1, 1206 cm−1 and 1440 cm−1. These changes were linked with reactive oxygen species, hydroxyproline and triglyceride levels in ProliHHs, and the hypothesis were consistent with the corresponding assay results. Conclusions In brief, Raman spectroscopy was successfully employed to identify different stages of ProliHHs during dedifferentiation process. The approach can simultaneously trace multiple changes of cellular components from somatic cells to progenitor cells.

scholarly journals Artificial intelligence in critical care: prediction of sepsis in patients in intensive care from first initial laboratory parameters

2020 ◽  
Vol 8 (4) ◽  
pp. 1509
Author(s):  
Samrat Mitra ◽  
H. Sangwan
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
False Negative ◽  
Predictive Marker ◽  
Learning Model ◽  
Initial Assessment ◽  
Statistical Parameters ◽  
Predictive Tool ◽  
Linear Discriminant ◽  
Machine Learning Model

Background: Sepsis is a leading cause of morbidity and mortality in the critical care setting. The analysis of hemostatic parameters at admission have been proven to be a predictive marker for development of sepsis in the ICU. The present study aims to develop a machine learning model which can predict the development of sepsis after 72 hours of ICU admission, from initial assessment of hemostatic parameters.Methods: A total of 170 ICU admissions over six months (May 2018 - Dec 2018) period were included in the study. Hemostatic parameters including platelet counts, prothrombin time and Sonoclot assay were assayed at time of admission. The patients were followed up for development of sepsis. The data was split in two sets: training (100) and test (70). A machine learning model was developed using the linear discriminant analysis (LDA) model, in the R programming environment. The statistical parameters employed were sensitivity, specificity, positive and negative predictive value.Results: A comparison of incidence of development of clinical sepsis and predicted sepsis by the model showed 74.19% sensitivity and 84.61% specificity over the testing set. 06 false positives and 08 false negative predictions were encountered.Conclusions: The model shows potential to be used as a predictive tool for development of sepsis in the critical care ward. Moderate sensitivity and good specificity were achieved by the model, highlighting the role of hematologic assessment at admission in prediction of development of sepsis. However, further studies with larger datasets are required before implementation in clinical practice.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

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.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

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.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model
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