Support vector machine and PCA for the exploratory analysis of Salvia officinalis samples treated with growth regulators based in the agronomic parameters and multielement composition

Moreira, Gisele Chagas; Carneiro, Candice N.; Anjos, Gilvanda Leão dos; Silva, Franceli da; Santos, Jorge L.O.; Dias, Fábio de S.

doi:10.1016/j.foodchem.2021.131345

Cited by 9 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior to conducting further analysis, the recurrent feature elimination (RFE) method, widely recognized for feature selection, was applied. The final dataset was used to train a random forest (RF) model [ 23 ], a support vector machine (SVM) model [ 24 ], Gaussian naïve Bayes (GNB) [ 25 ], and a k-nearest neighbor (k-NN) model [ 26 ]. The overall workflow of the work is given in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

et al. 2023

View full text Add to dashboard Cite

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2’s active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.

show abstract

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Inductively coupled plasma optical emission spectrometry (ICP OES) and inductively coupled plasma mass spectrometry (ICP-MS), their emission techniques are high selectivity and sensitivity, however expensive cost [11]. An analytical technique that has been reported in the literature with a new proposal by multielement analyses is microwave-induced plasma optical emission spectrometry (MIP OES) [12][13][14][15]. This technique is inexpensive because use a nitrogen plasma and present good sensibility for multielement determination [16,17].…”

Section: Introductionmentioning

confidence: 99%

Exploratory analysis of South America wines using artificial intelligence

Carneiro

Gomez

Spisso

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The microwave-induced plasma optical emission spectrometer has been used for multielement determination in South American wine samples. The analytes were determined after acid digestion of 47 samples of Brazilian and Argentine wine and the application of logistic regression, support vector machine, and decision tree for exploratory analysis and the comparison of these algorithms in differentiating red wine samples by region of origin. The Brazilian wines samples were clustering in a group, while Argentines wines samples formed another cluster. The quantification limits (mg L-1) were P (0.06); B (0.08); K (0.17); Mn (0.002); Cr (0.002) and Al (0.02). The accuracy of the method was evaluated analyzing the wine samples by ICP OES for results comparison. The concentration in mg L-1 found for each element in wine samples, were Al (< 0.02 - 1.82), Cr (0.15 - 0.50), Mn (< 0.002 - 0.8), P (97 - 277), B (1.7 - 11.6), Pb (< 0.06 - 0.3), Na (8.84 - 41.57) and K (604 - 1701), in mg L-1.

show abstract

“…Sage (Salvia officinalis L.) belongs to the Lamiaceae family and is one of the most economically popular aromatic and medicinal herbs [2]. Sage is a perennial crop and it is found that the essential oil is higher in the first cutting (in the end of spring) while the leaf yield is lower when the cultivation takes place at the end of summer [3].…”

Section: Introductionmentioning

confidence: 99%

Effect of Bio-nitrogen Fertilization, Plant Density and Irrigation on Physiological and Agronomic Characteristics of Salvia officinalis Organic Cultivation

Skoufogianni¹,

Molla²,

Angelaki³

et al. 2022

ujar

View full text Add to dashboard Cite

Sage (Salvia officinalis L) is a widespread aromatic and medicinal plant with various positive affects in human diet and health. In order to investigate the interaction among bio-fertilization, plant density and irrigation regimes, in Salvia officinalis cultivation, an experimental field was established at the University Farm of Thessaly, in Velestino (Greece). The transplant was carried out in October 2016 and the experiment data concern the plant height, Leaf Area Index (LAI), Chlorophyll content and dry biomass of the 3 rd and 4 th growing years. A split-split-plot experimental design was used, including two irrigation regimes (IR0: no irrigation and IR1: irrigation), three bio -nitrogen fertilization systems (N0: 0 kg ha -1 , N1: 40 kg ha -1 , N2: 80 kg ha -1 ) and two different plant density (D1: 10.000 and D2: 20.000 plants ha -1 ). The use of the drip irrigation system ameliorated the plant height, the dry leaves yield and the LAI. The dry leaves yield in 3 rd year was ranged between 166.3 -522 kg ha -1 and 101.67 -359 kg ha -1 in May and August, respectively. As it concerns the Chlorophyll index (CCI), the bio -nitrogen fertilization and the irrigation had provoked an increase to its amount during the studied growing year. Therefore, Salvia officinalis can be a promising perennial crop, cultivated under the Greek climatic conditions.

show abstract

Support vector machine and PCA for the exploratory analysis of Salvia officinalis samples treated with growth regulators based in the agronomic parameters and multielement composition

Cited by 9 publications

References 35 publications

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Exploratory analysis of South America wines using artificial intelligence

Effect of Bio-nitrogen Fertilization, Plant Density and Irrigation on Physiological and Agronomic Characteristics of Salvia officinalis Organic Cultivation

Contact Info

Product

Resources

About