Pub Date : 2024-10-03DOI: 10.1016/j.chemolab.2024.105241
Yaoyang Liu , Morug Salih Mahdi , Usama Kadem Radi , Ali Jihad , Ali Hamid AbdulHussein , Irshad Ahmad , Nasrin Mansuri , Mostafa Adnan Abdalrahman , Ahmed Alkhayyat , Ahmed Faisal
Here, we employed machine learning models to predict how well Capecitabine drug would dissolve in supercritical carbon dioxide as the green solvent. The vision is to investigate the drug suitability for processing of nanodrugs with enhanced bioavailability in the body. In the employed data set, P (pressure) and T (temperature) serve as inputs, and Y, the solubility, is the only output for building the models. This study uses DT (Decision Tree) and MLP (Multilayer perceptron) as the core models. However, the raw and individual form of conventional algorithms may not provide accurate and general results. Ensemble methods like boosting improve the model performance. Also, single and ensemble models mounted on these models have hyper-parameters whose optimization affects the final models. Meta-heuristic algorithms are popular for tuning hyper-parameters. In this research, we used a new hybrid framework by coupling the basic models with the Adaboost algorithm (as an ensemble method) and PO and CS algorithms (as optimizers) to obtain four different models. The MLP model boosted with Adaboost and tuned with PO algorithm showed the best fitting accuracy among all models. This model reduces the RMSE error rate to 1.71, MSE to 2.92, and MAE to 1.42.
{"title":"Machine learning based modeling for estimation of drug solubility in supercritical fluid by adjusting important parameters","authors":"Yaoyang Liu , Morug Salih Mahdi , Usama Kadem Radi , Ali Jihad , Ali Hamid AbdulHussein , Irshad Ahmad , Nasrin Mansuri , Mostafa Adnan Abdalrahman , Ahmed Alkhayyat , Ahmed Faisal","doi":"10.1016/j.chemolab.2024.105241","DOIUrl":"10.1016/j.chemolab.2024.105241","url":null,"abstract":"<div><div>Here, we employed machine learning models to predict how well Capecitabine drug would dissolve in supercritical carbon dioxide as the green solvent. The vision is to investigate the drug suitability for processing of nanodrugs with enhanced bioavailability in the body. In the employed data set, P (pressure) and T (temperature) serve as inputs, and Y, the solubility, is the only output for building the models. This study uses DT (Decision Tree) and MLP (Multilayer perceptron) as the core models. However, the raw and individual form of conventional algorithms may not provide accurate and general results. Ensemble methods like boosting improve the model performance. Also, single and ensemble models mounted on these models have hyper-parameters whose optimization affects the final models. Meta-heuristic algorithms are popular for tuning hyper-parameters. In this research, we used a new hybrid framework by coupling the basic models with the Adaboost algorithm (as an ensemble method) and PO and CS algorithms (as optimizers) to obtain four different models. The MLP model boosted with Adaboost and tuned with PO algorithm showed the best fitting accuracy among all models. This model reduces the RMSE error rate to 1.71, MSE to 2.92, and MAE to 1.42.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105241"},"PeriodicalIF":3.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data measured on the same observations and organized in blocks of variables — from different measurement sources or deduced from topics specified by the user — are common in practice. Multiblock exploratory methods are useful tools to extract information from data in a reduced and interpretable common space. However, many methods have been proposed independently and the users are often lost in selecting the appropriate one, especially as they do not always lead to the same results or because outputs do not have the same form. For this purpose, the data decomposition by canonical factorization was introduced thus applied to some widely-used methods, CPCA, MCOA, MFA, STATIS and CCSWA. The methods were compared on simulated (resp. real) data whose structure is controlled (resp. known). Theoretical and practical results pinpoint that the block-structure must be carefully explored beforehand. The number of block-variables and the block-variance distribution along dimensions impacts the choice of the block-scaling. The observation-structure within and between blocks impacts the choice of the method. CPCA or MCOA mix common and specific information, STATIS highlights common structure only whereas CCSWA focuses on specific information. To enable these diagnoses, methods and proposed comparison tools are available on R, Matlab or Galaxy.
{"title":"Benchmarking multiblock methods with canonical factorization","authors":"Stéphanie Bougeard , Caroline Peltier , Benoit Jaillais , Jean-Claude Boulet , Mohamed Hanafi","doi":"10.1016/j.chemolab.2024.105240","DOIUrl":"10.1016/j.chemolab.2024.105240","url":null,"abstract":"<div><div>Data measured on the same observations and organized in blocks of variables — from different measurement sources or deduced from topics specified by the user — are common in practice. Multiblock exploratory methods are useful tools to extract information from data in a reduced and interpretable common space. However, many methods have been proposed independently and the users are often lost in selecting the appropriate one, especially as they do not always lead to the same results or because outputs do not have the same form. For this purpose, the data decomposition by canonical factorization was introduced thus applied to some widely-used methods, CPCA, MCOA, MFA, STATIS and CCSWA. The methods were compared on simulated (resp. real) data whose structure is controlled (resp. known). Theoretical and practical results pinpoint that the block-structure must be carefully explored beforehand. The number of block-variables and the block-variance distribution along dimensions impacts the choice of the block-scaling. The observation-structure within and between blocks impacts the choice of the method. CPCA or MCOA mix common and specific information, STATIS highlights common structure only whereas CCSWA focuses on specific information. To enable these diagnoses, methods and proposed comparison tools are available on <span>R</span>, <span>Matlab</span> or <span>Galaxy</span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105240"},"PeriodicalIF":3.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.chemolab.2024.105238
Zina-Sabrina Duma , Jouni Susiluoto , Otto Lamminpää , Tuomas Sihvonen , Satu-Pia Reinikainen , Heikki Haario
Partial Least-Squares (PLS) regression is a widely used tool in chemometrics for performing multivariate regression. As PLS has a limited capacity of modelling non-linear relations between the predictor variables and the response, Kernel PLS (K-PLS) has been introduced for modelling non-linear predictor-response relations. Most available studies use fixed kernel parameters, reducing the performance potential of the method. Only a few studies have been conducted on optimizing the kernel parameters for K-PLS. In this article, we propose a methodology for the kernel function optimization based on Kernel Flows (KF), a technique developed for Gaussian Process Regression (GPR). The results are illustrated with four case studies. The case studies represent both numerical examples and real data used in classification and regression tasks. K-PLS optimized with KF, called KF-PLS in this study, is shown to yield good results in all illustrated scenarios, outperforming literature results and other non-linear regression methodologies. In the present study, KF-PLS has been compared to convolutional neural networks (CNN), random trees, ensemble methods, support vector machines (SVM), and GPR, and it has proved to perform very well.
{"title":"KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows","authors":"Zina-Sabrina Duma , Jouni Susiluoto , Otto Lamminpää , Tuomas Sihvonen , Satu-Pia Reinikainen , Heikki Haario","doi":"10.1016/j.chemolab.2024.105238","DOIUrl":"10.1016/j.chemolab.2024.105238","url":null,"abstract":"<div><div>Partial Least-Squares (PLS) regression is a widely used tool in chemometrics for performing multivariate regression. As PLS has a limited capacity of modelling non-linear relations between the predictor variables and the response, Kernel PLS (K-PLS) has been introduced for modelling non-linear predictor-response relations. Most available studies use fixed kernel parameters, reducing the performance potential of the method. Only a few studies have been conducted on optimizing the kernel parameters for K-PLS. In this article, we propose a methodology for the kernel function optimization based on Kernel Flows (KF), a technique developed for Gaussian Process Regression (GPR). The results are illustrated with four case studies. The case studies represent both numerical examples and real data used in classification and regression tasks. K-PLS optimized with KF, called KF-PLS in this study, is shown to yield good results in all illustrated scenarios, outperforming literature results and other non-linear regression methodologies. In the present study, KF-PLS has been compared to convolutional neural networks (CNN), random trees, ensemble methods, support vector machines (SVM), and GPR, and it has proved to perform very well.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105238"},"PeriodicalIF":3.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-29DOI: 10.1016/j.chemolab.2024.105239
Ali Raza , Jamal Uddin , Quan Zou , Shahid Akbar , Wajdi Alghamdi , Ruijun Liu
Inflammation is a biological response to harmful stimuli including infections, damaged cells, tissue injuries, and toxic chemicals. It plays an essential role in facilitating tissue repair by eliminating pathogenic microorganisms. Currently, numerous therapies are applied to treat autoimmune and inflammatory diseases. However, these conventional anti-inflammatory medications are often labor-intensive, costly, and associated with adverse side effects. Recently, researchers have identified anti-inflammatory peptides (AIPs) as a cost-effective alternative for treating several inflammatory diseases, due to their high selectivity for target cells with minimal side effects. In this study, we introduce a novel computational predictor, AIPs-DeepEnC-GA, developed to accurately predict AIP samples. The training sequences are encoded using a novel n-spaced dipeptide-based position-specific scoring matrix (NsDP-PSSM) and Pseudo position-specific scoring matrix (PsePSSM)-based embedded evolutionary features. Additionally, the reduced-amino acid alphabet (RAAA-11), and composite Physiochemical properties (CPP) are employed to capture cluster-physiochemical properties based on structural information. A hybrid feature strategy is then applied, integrating embedded evolutionary features, CPP and RAAA-11 descriptors to overcome the limitations of individual encoding methods. Minimum redundancy and maximum relevance (mRMR) is utilized to select the optimal features. The selected features are trained using four different deep-learning models. The predictive labels generated by these models are provided to a genetic algorithm to form a deep-ensemble training model. The proposed AIPs-DeepEnC-GA model achieved a ∼15 % increase in predictive accuracy, reaching 94.39 %, and a 19 % improvement in the area under the curve (AUC), achieving a value of 0.98 using training sequences. For independent datasets, our method obtained improved accuracies of 91.87 %, and 89.21 %, with AUC values of 0.94 and 0.92 for Ind-I, and Ind-II, respectively. Our proposed AIPs-DeepEnC-GA model demonstrates an 11 % improvement in predictive accuracy over existing AIPs computational models using training samples. The efficacy and reliability of this model make it a promising tool for both in drug development and research academia.
{"title":"AIPs-DeepEnC-GA: Predicting anti-inflammatory peptides using embedded evolutionary and sequential feature integration with genetic algorithm based deep ensemble model","authors":"Ali Raza , Jamal Uddin , Quan Zou , Shahid Akbar , Wajdi Alghamdi , Ruijun Liu","doi":"10.1016/j.chemolab.2024.105239","DOIUrl":"10.1016/j.chemolab.2024.105239","url":null,"abstract":"<div><div>Inflammation is a biological response to harmful stimuli including infections, damaged cells, tissue injuries, and toxic chemicals. It plays an essential role in facilitating tissue repair by eliminating pathogenic microorganisms. Currently, numerous therapies are applied to treat autoimmune and inflammatory diseases. However, these conventional anti-inflammatory medications are often labor-intensive, costly, and associated with adverse side effects. Recently, researchers have identified anti-inflammatory peptides (AIPs) as a cost-effective alternative for treating several inflammatory diseases, due to their high selectivity for target cells with minimal side effects. In this study, we introduce a novel computational predictor, AIPs-DeepEnC-GA, developed to accurately predict AIP samples. The training sequences are encoded using a novel n-spaced dipeptide-based position-specific scoring matrix (NsDP-PSSM) and Pseudo position-specific scoring matrix (PsePSSM)-based embedded evolutionary features. Additionally, the reduced-amino acid alphabet (RAAA-11), and composite Physiochemical properties (CPP) are employed to capture cluster-physiochemical properties based on structural information. A hybrid feature strategy is then applied, integrating embedded evolutionary features, CPP and RAAA-11 descriptors to overcome the limitations of individual encoding methods. Minimum redundancy and maximum relevance (mRMR) is utilized to select the optimal features. The selected features are trained using four different deep-learning models. The predictive labels generated by these models are provided to a genetic algorithm to form a deep-ensemble training model. The proposed AIPs-DeepEnC-GA model achieved a ∼15 % increase in predictive accuracy, reaching 94.39 %, and a 19 % improvement in the area under the curve (AUC), achieving a value of 0.98 using training sequences. For independent datasets, our method obtained improved accuracies of 91.87 %, and 89.21 %, with AUC values of 0.94 and 0.92 for Ind-I, and Ind-II, respectively. Our proposed AIPs-DeepEnC-GA model demonstrates an 11 % improvement in predictive accuracy over existing AIPs computational models using training samples. The efficacy and reliability of this model make it a promising tool for both in drug development and research academia.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105239"},"PeriodicalIF":3.7,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1016/j.chemolab.2024.105234
Mathias Sawall , Christoph Kubis , Benedict N. Leidecker , Lukas Prestin , Tomass Andersons , Martina Beese , Jan Hellwig , Robert Franke , Armin Börner , Klaus Neymeyr
Peak Group Analysis (PGA) is a multivariate curve resolution technique that attempts to extract single pure component spectra from time series of spectral mixture data. It requires that the mixture spectra consist of relatively sharp peaks, as is typical in IR and Raman spectroscopy. PGA aims to construct from individual peaks the associated pure component spectra in the form of nonnegative linear combinations of the right singular vectors of the spectral data matrix.
This work presents an automated PGA (autoPGA) that starts with upstream peak detection applied to time series of spectra, combining different window-based peak detection techniques with balanced peak acceptance criteria and peak grouping to deal with repeated detections. The next step is a single-spectrum-oriented PGA analysis. This is followed by a downstream correlation analysis to identify pure component spectra that occur multiple times. AutoPGA provides a complete pure component factorization of the matrix of measured data. The algorithm is applied to FT-IR data sets on various rhodium carbonyl complexes and from an equilibrium of iridium complexes.
{"title":"An automated Peak Group Analysis for vibrational spectra analysis","authors":"Mathias Sawall , Christoph Kubis , Benedict N. Leidecker , Lukas Prestin , Tomass Andersons , Martina Beese , Jan Hellwig , Robert Franke , Armin Börner , Klaus Neymeyr","doi":"10.1016/j.chemolab.2024.105234","DOIUrl":"10.1016/j.chemolab.2024.105234","url":null,"abstract":"<div><div>Peak Group Analysis (PGA) is a multivariate curve resolution technique that attempts to extract single pure component spectra from time series of spectral mixture data. It requires that the mixture spectra consist of relatively sharp peaks, as is typical in IR and Raman spectroscopy. PGA aims to construct from individual peaks the associated pure component spectra in the form of nonnegative linear combinations of the right singular vectors of the spectral data matrix.</div><div>This work presents an automated PGA (autoPGA) that starts with upstream peak detection applied to time series of spectra, combining different window-based peak detection techniques with balanced peak acceptance criteria and peak grouping to deal with repeated detections. The next step is a single-spectrum-oriented PGA analysis. This is followed by a downstream correlation analysis to identify pure component spectra that occur multiple times. AutoPGA provides a complete pure component factorization of the matrix of measured data. The algorithm is applied to FT-IR data sets on various rhodium carbonyl complexes and from an equilibrium of iridium complexes.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105234"},"PeriodicalIF":3.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1016/j.chemolab.2024.105237
Bahram Hemmateenejad , Knut Baumann
The responses of the paper-based colorimetric sensor arrays are typically recorded by an imaging device. The color values of the images are subjected to chemometrics data analysis, with a view to extract the relevant information. As is the case with data extracted from other analytical instruments, these data must undergo pre-processing prior to undergoing further analysis. This study represents the first comprehensive and systematic investigation into the impact of data pre-processing techniques on the quality of subsequent data analysis methods applied to imaging data collected from paper-based colorimetric sensor arrays. The use of color difference data (calculated by subtracting the images of the sensors before exposure from those after exposure) revealed that pre-treatment of the data was not a critical factor, although it could reduce the complexity of the model. For example, the number of principal components in the principal component-linear discriminant analysis model was reduced from eight (for data that had not been pre-processed) to three (for pre-processed data) to achieve the same level of accuracy (92 %). Nevertheless, the pivotal role of data pre-processing was elucidated through the examination of data sets collected immediately following exposure to the samples’ vapor. It was demonstrated that the use of an appropriate pre-processing method allows for the elimination or significant reduction of between-sensor variations, obviating the necessity for the inclusion of data from images taken prior to exposure. With regard to the objective of classification, the object pre-processing methods that demonstrated particular promise were mean (or median) centering, Pareto scaling and standard normal variate. To illustrate, in the analysis of volatile organic compounds by an array of metallic nanoparticles, the cross-validation classification accuracy of the unprocessed data, which was 70 %, increased to 95 % when unit variance scaling and range scaling were applied to objects and variables, respectively. In the calibration phase, the majority of pre-processing methods enhanced the quality of the regression models. Using suitable pre-processing methods for both objects and variables, eliminated the need for using the before exposing image of the CSAs.
{"title":"Data pre-processing for paper-based colorimetric sensor arrays","authors":"Bahram Hemmateenejad , Knut Baumann","doi":"10.1016/j.chemolab.2024.105237","DOIUrl":"10.1016/j.chemolab.2024.105237","url":null,"abstract":"<div><div>The responses of the paper-based colorimetric sensor arrays are typically recorded by an imaging device. The color values of the images are subjected to chemometrics data analysis, with a view to extract the relevant information. As is the case with data extracted from other analytical instruments, these data must undergo pre-processing prior to undergoing further analysis. This study represents the first comprehensive and systematic investigation into the impact of data pre-processing techniques on the quality of subsequent data analysis methods applied to imaging data collected from paper-based colorimetric sensor arrays. The use of color difference data (calculated by subtracting the images of the sensors before exposure from those after exposure) revealed that pre-treatment of the data was not a critical factor, although it could reduce the complexity of the model. For example, the number of principal components in the principal component-linear discriminant analysis model was reduced from eight (for data that had not been pre-processed) to three (for pre-processed data) to achieve the same level of accuracy (92 %). Nevertheless, the pivotal role of data pre-processing was elucidated through the examination of data sets collected immediately following exposure to the samples’ vapor. It was demonstrated that the use of an appropriate pre-processing method allows for the elimination or significant reduction of between-sensor variations, obviating the necessity for the inclusion of data from images taken prior to exposure. With regard to the objective of classification, the object pre-processing methods that demonstrated particular promise were mean (or median) centering, Pareto scaling and standard normal variate. To illustrate, in the analysis of volatile organic compounds by an array of metallic nanoparticles, the cross-validation classification accuracy of the unprocessed data, which was 70 %, increased to 95 % when unit variance scaling and range scaling were applied to objects and variables, respectively. In the calibration phase, the majority of pre-processing methods enhanced the quality of the regression models. Using suitable pre-processing methods for both objects and variables, eliminated the need for using the before exposing image of the CSAs.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105237"},"PeriodicalIF":3.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1016/j.chemolab.2024.105236
Joan Borràs-Ferrís , Carl Duchesne , Alberto Ferrer
The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw material properties block followed by a process variables block that is manipulated according to raw material properties. The SMB-PLS uses orthogonalization to separate correlated information between blocks from orthogonal variations. This allows monitoring the system in different stages considering only the remaining orthogonal part in each block. Thus, the SMB-PLS increases the interpretability and process understanding in the model building (Phase I), since it provides a deep insight about the nature of the system variations. Besides, it prevents any special cause from propagating to subsequent blocks enabling their use in the model exploitation (Phase II). The methodology is applied to a real case study from a food manufacturing process.
{"title":"Multivariate SPC via sequential multiblock-PLS","authors":"Joan Borràs-Ferrís , Carl Duchesne , Alberto Ferrer","doi":"10.1016/j.chemolab.2024.105236","DOIUrl":"10.1016/j.chemolab.2024.105236","url":null,"abstract":"<div><div>The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw material properties block followed by a process variables block that is manipulated according to raw material properties. The SMB-PLS uses orthogonalization to separate correlated information between blocks from orthogonal variations. This allows monitoring the system in different stages considering only the remaining orthogonal part in each block. Thus, the SMB-PLS increases the interpretability and process understanding in the model building (Phase I), since it provides a deep insight about the nature of the system variations. Besides, it prevents any special cause from propagating to subsequent blocks enabling their use in the model exploitation (Phase II). The methodology is applied to a real case study from a food manufacturing process.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105236"},"PeriodicalIF":3.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell Painting is an established community-based microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest publicly available Cell Painting dataset with CellProfiler features, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 115k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this context, our paper introduces a scalable and computationally efficient data analytics workflow created to meet the needs of researchers. This data-driven workflow facilitates the comparison of drug treatment effects through significant and biologically relevant insights. The workflow consists of two parts: first, the Equivalence score (Eq. score), a straightforward yet sophisticated metric highlighting relevant deviations from negative controls based on cell image morphology; second, the scalability of the workflow, by utilizing the Eq. scores on a large scale to predict and classify the subtle morphological changes in cell image profiles. By doing so, we show classification improvements compared to using the raw CellProfiler features on the CPJUMP1-pilot dataset on three types of perturbations.
We hope that our workflow’s contributions will enhance drug screening efficiency and streamline the drug development process. As this process is resource-intensive, every incremental improvement is valuable. Through our collective efforts in advancing the understanding of high-throughput image-based data, we aim to reduce both the time and cost of developing new, life-saving treatments.
{"title":"A scalable, data analytics workflow for image-based morphological profiles","authors":"Edvin Forsgren , Olivier Cloarec , Pär Jonsson , Gillian Lovell , Johan Trygg","doi":"10.1016/j.chemolab.2024.105232","DOIUrl":"10.1016/j.chemolab.2024.105232","url":null,"abstract":"<div><p>Cell Painting is an established community-based microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest publicly available Cell Painting dataset with CellProfiler features, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 115k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this context, our paper introduces a scalable and computationally efficient data analytics workflow created to meet the needs of researchers. This data-driven workflow facilitates the comparison of drug treatment effects through significant and biologically relevant insights. The workflow consists of two parts: first, the Equivalence score (Eq. score), a straightforward yet sophisticated metric highlighting relevant deviations from negative controls based on cell image morphology; second, the scalability of the workflow, by utilizing the Eq. scores on a large scale to predict and classify the subtle morphological changes in cell image profiles. By doing so, we show classification improvements compared to using the raw CellProfiler features on the CPJUMP1-pilot dataset on three types of perturbations.</p><p>We hope that our workflow’s contributions will enhance drug screening efficiency and streamline the drug development process. As this process is resource-intensive, every incremental improvement is valuable. Through our collective efforts in advancing the understanding of high-throughput image-based data, we aim to reduce both the time and cost of developing new, life-saving treatments.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105232"},"PeriodicalIF":3.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001722/pdfft?md5=8447cc5a34c516ef2e46efef43419f28&pid=1-s2.0-S0169743924001722-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.chemolab.2024.105233
Tinghua Wang, Daili Zhang, Hanming Liu
Support vector machine (SVM) is known for its good generalization performance and wide application in various fields. Despite its success, the learning efficiency of SVM decreases significantly originating from the assumption that the number of training samples increases rapidly. Consequently, the traditional SVM with standard optimization methods faces challenges such as excessive memory requirements and slow training speed, especially for large-scale training sets. To address this issue, this paper draws inspiration from the fuzzy support vector machine (FSVM). Considering that each sample has varying contributions to the decision plane, we propose an effective SVM sample reduction method based on the fuzzy membership function (FMF). This method uses FMF to calculate the fuzzy membership of each training sample. Training samples with low fuzzy memberships are then deleted. Specifically, we propose SVM sample reduction algorithms based on class center distance, kernel target alignment, centered kernel alignment, slack factor, entropy, and bilateral weighted FMF, respectively. Comprehensive experiments on UCI and KEEL datasets demonstrate that our proposed algorithms outperform other comparative methods in terms of accuracy, F-measure, and hinge-loss measures.
{"title":"Toward effective SVM sample reduction based on fuzzy membership functions","authors":"Tinghua Wang, Daili Zhang, Hanming Liu","doi":"10.1016/j.chemolab.2024.105233","DOIUrl":"10.1016/j.chemolab.2024.105233","url":null,"abstract":"<div><p>Support vector machine (SVM) is known for its good generalization performance and wide application in various fields. Despite its success, the learning efficiency of SVM decreases significantly originating from the assumption that the number of training samples increases rapidly. Consequently, the traditional SVM with standard optimization methods faces challenges such as excessive memory requirements and slow training speed, especially for large-scale training sets. To address this issue, this paper draws inspiration from the fuzzy support vector machine (FSVM). Considering that each sample has varying contributions to the decision plane, we propose an effective SVM sample reduction method based on the fuzzy membership function (FMF). This method uses FMF to calculate the fuzzy membership of each training sample. Training samples with low fuzzy memberships are then deleted. Specifically, we propose SVM sample reduction algorithms based on class center distance, kernel target alignment, centered kernel alignment, slack factor, entropy, and bilateral weighted FMF, respectively. Comprehensive experiments on UCI and KEEL datasets demonstrate that our proposed algorithms outperform other comparative methods in terms of accuracy, F-measure, and hinge-loss measures.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"254 ","pages":"Article 105233"},"PeriodicalIF":3.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号