scholarly journals AI Informed Toxicity Screening of Amine Chemistries used in the Synthesis of Hybrid Organic-Inorganic Perovskites

2021 ◽  
Author(s):  
An Su ◽  
Haotian Xue ◽  
Yuanbin She ◽  
Krishna Rajan
Keyword(s):  
Machine Learning ◽  
Structure Function ◽  
Molecular Similarity ◽  
Toxicity Screening ◽  
Relative Toxicity ◽  
Potential Toxicity ◽  
Molecular Fingerprint ◽  
Data Dimensionality Reduction ◽  
Reduction Methods ◽  
2D And 3D

This paper describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic-inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non-linear data dimensionality reduction methods (UMAP), we develop an “Amine Atlas’. We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure-function relationships for safer materials chemistries that also incorporate sustainability/ toxicity concerns.

scholarly journals An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

2021 ◽  
Vol 23 (1) ◽  
pp. 69-85
Author(s):  
Hemank Lamba ◽  
Kit T. Rodolfa ◽  
Rayid Ghani
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Empirical Work ◽  
Bias Reduction ◽  
Research Community ◽  
High Stakes ◽  
Social Good ◽  
Social Service Delivery ◽  
Reduction Methods ◽  
Applications Of Machine Learning

Applications of machine learning (ML) to high-stakes policy settings - such as education, criminal justice, healthcare, and social service delivery - have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but postprocessing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.

scholarly journals Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chunlei Xia ◽  
Longwen Fu ◽  
Zuoyi Liu ◽  
Hui Liu ◽  
Lingxin Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Aquatic Toxicity ◽  
Warning System ◽  
Video Tracking ◽  
Behavioral Data ◽  
3D Space ◽  
Biological Early Warning System ◽  
Abnormal Pattern ◽  
2D And 3D

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

2021 ◽  
Author(s):  
Yuxiang Chen ◽  
Chuanlei Liu ◽  
Yang An ◽  
Yue Lou ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Similarity Search ◽  
Data Science ◽  
Molecular Similarity ◽  
Molecular Design ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Methyl Mercaptan ◽  
Model Framework ◽  
Modeling Framework

Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry's law constant 89.4% lower than that of methyl diethanolamine (MDEA).

Machine Learning Dimensionality Reduction Showed Marginal Performance Benefit Over Stepwise Regression for Risk Stratification of Chest Pain Patients in the Emergency Department

2020 ◽  
Author(s):  
Nan Liu ◽  
Marcel Lucas Chee ◽  
Zhi Xiong Koh ◽  
Su Li Leow ◽  
Andrew Fu Wah Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Chest Pain ◽  
Risk Stratification ◽  
Dimensionality Reduction ◽  
Prediction Models ◽  
Stepwise Logistic Regression ◽  
Stepwise Variable Selection ◽  
Reduction Methods ◽  
Pain Patients

Abstract Background: Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can achieve superior performance than the stepwise approach in deriving risk stratification models. Methods: A retrospective analysis was conducted on the data of patients >20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and HRnV parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization. Candidate variables identified using univariable analysis were then used to generate the stepwise logistic regression model and eight machine learning dimensionality reduction prediction models. A separate set of models was derived by excluding troponin. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.Results: 795 patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods marginally but non-significantly outperformed stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve (AUC) of 0.901. All HRnV-based models generated in this study outperformed several existing clinical scores in ROC analysis.Conclusions: HRnV-based models using stepwise logistic regression performed better than existing chest pain scores for predicting MACE, with only marginal improvements using machine learning dimensionality reduction. Moreover, traditional stepwise approach benefits from model transparency and interpretability; in comparison, machine learning dimensionality reduction models are black boxes, making them difficult to explain in clinical practice.

scholarly journals Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Wan ◽  
Jiaxuan Zhou ◽  
Xiaoying Xia ◽  
Jianfeng Hu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diagnostic Performance ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Selection Methods ◽  
Linear Discriminant ◽  
2D And 3D

ObjectiveTo evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).Material and MethodsA total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.ResultsThe 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.ConclusionsAfter algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

scholarly journals Feature Snatching and Performance Assessment for Connoting the Admittance Likelihood of student using Principal Component Reduction

2019 ◽  
Vol 8 (2) ◽  
pp. 4800-4807
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Dimensionality Reduction ◽  
Admission Rate ◽  
Principal Component ◽  
Feature Reduction ◽  
Experimental Result ◽  
Sparse Pca ◽  
Reduction Methods ◽  
Incremental Pca

Recently, engineers are concentrating on designing an effective prediction model for finding the rate of student admission in order to raise the educational growth of the nation. The method to predict the student admission towards the higher education is a challenging task for any educational organization. There is a high visibility of crisis towards admission in the higher education. The admission rate of the student is the major risk to the educational society in the world. The student admission greatly affects the economic, social, academic, profit and cultural growth of the nation. The student admission rate also depends on the admission procedures and policies of the educational institutions. The chance of student admission also depends on the feedback given by all the stake holders of the educational sectors. The forecasting of the student admission is a major task for any educational institution to protect the profit and wealth of the organization. This paper attempts to analyze the performance of the student admission prediction by using machine learning dimensionality reduction algorithms. The Admission Predict dataset from Kaggle machine learning Repository is used for prediction analysis and the features are reduced by feature reduction methods. The prediction of the chance of Admit is achieved in four ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly contributing to the prediction of chance of admit. Thirdly, the Admission Predict dataset is subjected to dimensionality reduction methods like principal component analysis (PCA), Sparse PCA, Incremental PCA , Kernel PCA and Mini Batch Sparse PCA. Fourth, the optimized dimensionality reduced dataset is then executed to analyze and compare the mean squared error, Mean Absolute Error and R2 Score of each method. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the CGPA, GRE Score and TOEFL Score are highly correlated features in predicting the chance of admit. The execution of performance analysis shows that Incremental PCA have achieved the effective prediction of chance of admit with minimum MSE of 0.09, MAE of 0.24 and reasonable R2 Score of 0.26.

scholarly journals FP-ADMET: a compendium of fingerprint-based ADMET prediction models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Vishwesh Venkatraman
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Shortest Paths ◽  
Predictive Ability ◽  
Molecular Fingerprint ◽  
Atom Pairs ◽  
Forest Models ◽  
Random Forest Models ◽  
Local Path ◽  
Binary Fingerprints

Abstract Motivation The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. Summary In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. Availability The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet.

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