Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes?

Ksenofontov, Alexander A.; Lukanov, Michail M.; Bocharov, Pavel S.

doi:10.1016/j.saa.2022.121442

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…In comparison with traditional statistical methods, based on the previous studies, machine learning can generate models of higher predicted performance by handling more complex data, which may achieve higher accuracy and improved generalization[ 37 , 38 ]. With the rapid increase in artificial intelligence, machine learning methods are widely applied in the field of disease diagnosis and prediction[ 39 - 41 ]. Not only this, the method based on machine learning no longer requires strong assumptions about basic mechanisms such as image classification[ 40 ] and speech recognition[ 42 ], which have achieved cutting-edge predictive capabilities.…”

Section: Discussionmentioning

confidence: 99%

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Mao¹,

Chao²,

Zhong³

et al. 2023

World J Gastroenterol

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BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease (CD). However, thalidomide-induced peripheral neuropathy (TiPN), which has a large individual variation, is a major cause of treatment failure. TiPN is rarely predictable and recognized, especially in CD. It is necessary to develop a risk model to predict TiPN occurrence. AIM To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables. METHODS A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model. The National Cancer Institute Common Toxicity Criteria Sensory Scale (version 4.0) was used to assess TiPN. With 18 clinical features and 150 genetic variables, five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), specificity, sensitivity (recall rate), precision, accuracy, and F1 score. RESULTS The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248 [ P = 0.0004, odds ratio (OR): 8.983, 95% confidence interval (CI): 2.497-30.90], dose (mg/d, P = 0.002), brain-derived neurotrophic factor (BDNF) rs2030324 ( P = 0.001, OR: 3.164, 95%CI: 1.561-6.434), BDNF rs6265 ( P = 0.001, OR: 3.150, 95%CI: 1.546-6.073) and BDNF rs11030104 ( P = 0.001, OR: 3.091, 95%CI: 1.525-5.960). In the training set, gradient boosting decision tree (GBDT), extremely random trees (ET), random forest, logistic regression and extreme gradient boosting (XGBoost) obtained AUROC values > 0.90 and AUPRC > 0.87. Among these models, XGBoost and GBDT obtained the first two highest AUROC (0.90 and 1), AUPRC (0.98 and 1), accuracy (0.96 and 0.98), precision (0.90 and 0.95), F1 score (0.95 and 0.98), specificity (0.94 and 0.97), and sensitivity (1). In the validation set, XGBoost algorithm exhibited the best predictive performance with the highest specificity (0.857), accuracy (0.818), AUPRC (0.86) and AUROC (0.89). ET and GBDT obtained the highest sensitivity (1) and F1 score (0.8). Overall, compared with other state-of-the-art classifiers such as ET, GBDT and RF, XGBoost algorithm not only showed a more stable performance, but also yielded higher ROC-AUC and PRC-AUC scores, demonstrating its high accuracy in prediction of TiPN occurrence. CONCLUSION The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables. With the ability to identify high-risk patients using single nucleotide polymorphisms, it offers a feasible option for improving thalidomide efficacy in CD patients.

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Section: Discussionmentioning

confidence: 99%

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Mao¹,

Chao²,

Zhong³

et al. 2023

World J Gastroenterol

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“…39 At the first stage, the Deep Neural Network (DNN), 41 Random Forests Regression (RFR), 42 eXtreme Gradient Boosting (XGBoost), 43 Transformer Convolutional Neural Fingerprint (Transformer-CNF), 44 Transformer Convolutional Neural Networks (Transformer-CNN) 45 and SchNet 46 were chosen as ML algorithms. Previously, these methods showed a good quality of predicting the spectral properties (absorption maximum wavelengths and molar absorption coefficient) of dipyrromethene compounds including BODIPYs [47][48][49][50] and thermal properties of ionic liquids. 51,52 RFR is the most flexible and easy-to-use algorithm based on classification and regression trees (CART).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Accurate prediction of ¹¹B NMR chemical shift of BODIPYs via machine learning

Ksenofontov

Isaev

Lukanov

et al. 2023

Phys. Chem. Chem. Phys.

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“…To propel the fundamental studies and the discovery of new molecules, some open-source platforms have provided as web-based interfaces, such as Deep4Chem, [188] OCHEM, [211] ChemFluor, [206] ChemTS, [212] etc., facilitating the application and the fulfillment of the database at the same time. [213] Faced up to the fact that the field of AI-assisted molecular design is often questioned by the conventional wisdoms with a lot of virtually generated molecules inherently not synthesizable and/or functional, [214] Schneider et al provided a comprehensive review of the reflection on drug discovery to call for a designmake-test-analyze cycle for drug discovery.…”

Section: Variant Characteristicsmentioning

confidence: 99%

“…To propel the fundamental studies and the discovery of new molecules, some open‐source platforms have provided as web‐based interfaces, such as Deep4Chem, [ 188 ] OCHEM, [ 211 ] ChemFluor, [ 206 ] ChemTS, [ 212 ] etc., facilitating the application and the fulfillment of the database at the same time. [ 213 ]…”

Section: Ai For Selection and Design Of Sers Reportersmentioning

confidence: 99%

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

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Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes?

Cited by 11 publications

References 36 publications

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Accurate prediction of ¹¹B NMR chemical shift of BODIPYs via machine learning

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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Can machine learning methods accurately predict the molar absorption coefficient of different classes of dyes?

Cited by 11 publications

References 36 publications

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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Accurate prediction of ¹¹B NMR chemical shift of BODIPYs via machine learning