A High-Speed Screening Method by Combining a High-Throughput Method and a Machine-Learning Algorithm for Developing Novel Organic Electrolytes in Rechargeable Batteries

This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques.

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A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea

Chemosphere ◽

10.1016/j.chemosphere.2019.05.113 ◽

2019 ◽

Vol 234 ◽

pp. 242-251 ◽

Cited By ~ 14

Author(s):

Mikaël Kedzierski ◽

Mathilde Falcou-Préfol ◽

Marie Emmanuelle Kerros ◽

Maryvonne Henry ◽

Maria Luiza Pedrotti ◽

...

Keyword(s):

Machine Learning ◽

Mediterranean Sea ◽

High Throughput ◽

Learning Algorithm ◽

Ftir Spectra ◽

Machine Learning Algorithm ◽

The Mediterranean

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Machine learned calibrations to high-throughput molecular excited state calculations

10.26434/chemrxiv-2022-08jm9 ◽

2022 ◽

Author(s):

Shomik Verma ◽

Miguel Rivera ◽

David O. Scanlon ◽

Aron Walsh

Keyword(s):

Machine Learning ◽

Excited State ◽

High Throughput ◽

High Speed ◽

Large Scale ◽

Chemical Space ◽

False Negative ◽

Screening Method ◽

Calibration Model ◽

Linear Calibration

Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~5-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.

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P.2.36 Assessment of serum and urinary biomarkers for pneumoconiosis in a cohort of stone workers exposed to asbestos-contaminated minerals

Occupational and Environmental Medicine ◽

10.1136/oem-2019-epi.263 ◽

2019 ◽

Vol 76 (Suppl 1) ◽

pp. A96.2-A96

Author(s):

Hsiao-Yu Yang ◽

Pau-Chung Chen

Keyword(s):

Machine Learning ◽

Operating Characteristic ◽

Prediction Models ◽

Learning Algorithm ◽

Characteristic Curve ◽

Screening Method ◽

Urinary Biomarkers ◽

Machine Learning Algorithm ◽

Dose Response Relationship

BackgroundPneumoconiosis is still a problem in workers process non-asbestiform asbestos minerals and serpentinite rocks, such as nephrite, antigorite or talc that may contaminate with paragenetic asbestos minerals. An effective screening method is still lacking. The objective of this study was to assess the diagnostic accuracy using the serum and urinary biomarkers for pneumoconiosis in workers exposed to asbestos-contaminated minerals.MethodsPrediction models of pneumoconiosis were constructed from 140 stone workers (48 cases of pneumoconiosis and 118 controls) exposed to asbestos-contaminated minerals. We measured serum soluble mesothelin-related peptide (SMRP), fibulin-3, carcinoembryonic antigen, and urinary 8-Oxo-2’-deoxyguanosine (8-OHdG)/creatinine levels. Using the ILO international classification of radiographs of pneumoconiosis profusion subcategory ≥1/0 as the reference standard, we established a prediction model by machine learning algorithm. We assessed the accuracy by the area under the receiver operating characteristic curve (AUROC).ResultsThe SMRP level increased in workers exposed to nephrite. A dose-response relationship was found between the SMRP level and the severity of pneumoconiosis in workers exposed to asbestos-contaminated minerals. Machine learning algorithm composed of sex, age, and 4 serum and urinary biomarkers is able to predict pneumoconiosis with high accuracy (AUROC ranged from 0.76 to 1.00).ConclusionOur finding highlight the use of serum and urinary biomarkers can be developed as a screening tool for pneumoconiosis in workers exposed to potential asbestos contaminated minerals.

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Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials

10.21203/rs.3.rs-902313/v1 ◽

2021 ◽

Author(s):

Chinedu Ekuma ◽

Z Liu ◽

Srihari Kastuar

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

High Throughput ◽

Learning Algorithm ◽

2D Materials ◽

Machine Learning Algorithm ◽

New Materials ◽

Temperature Dependent ◽

Efficient Prediction ◽

Properties Of Materials

Abstract An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures, including their temperature-dependent mechanical properties and developed a machine learning algorithm for exploring predicted properties.

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Epicardial fat Tissue Volumetry: Comparison of Semi-Automatic Measurement and the Machine Learning Algorithm

Kardiologiia ◽

10.18087/cardio.2020.9.n1111 ◽

2020 ◽

Vol 60 (9) ◽

pp. 46-54

Author(s):

V. Y. Chernina ◽

M. E. Pisov ◽

M. G. Belyaev ◽

I. V. Bekk ◽

K. A. Zamyatina ◽

...

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Correlation Coefficient ◽

Low Dose ◽

Learning Algorithm ◽

Screening Method ◽

Automatic Measurement ◽

Machine Learning Algorithm ◽

Epicardial Fat Tissue ◽

Clinical Conditions

Aim To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed tomography (CT) of chest organs.Material and methods This analytical, retrospective, transversal study randomly included 100 patients from a database of a united radiological informational service (URIS). The patients underwent LDCT as a part of the project “Low-dose chest computed tomography as a screening method for detection of lung cancer and other diseases of chest organs” (n=50) and chest CT according to a standard protocol (n=50) in outpatient clinics of Moscow. Each image was read by two radiologists on a Syngo. via VB20 workstation. In addition, each image was evaluated with a developed machine-learning algorithm, which provides a completely automatic measurement of EAT.Results Comparison of EAT volumes obtained with chest LDCT and CT showed highly consistent results both for the expert-performed semi-automatic analyses (correlation coefficient >98 %) and between the expert layout and the machine-learning algorithm (correlation coefficient >95 %). Time of performing segmentation and volumetry on one image with the machine-learning algorithm was not longer than 40 sec, which was 30 times faster than the quantitative analysis performed by an expert and potentially facilitated quantification of the EAT volume in the clinical conditions.Conclusion The proposed method of automatic volumetry will expedite the analysis of EAT for predicting the risk of ischemic heart disease.

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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