16 research outputs found

    Additional file 1 of Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

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    Supplementary materials. The supplementary PDF file contains relevant information omitted from the main manuscript such as: (1) the ranked list of the top 50 genes selected by the TMRMR-C approach for H3N2, HRV and RSV datasets, respectively and (2) error bars for the two groups, symptomatic and asymptomatic, for the top genes selected from the three datasets. (DOCX 240 kb

    The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties

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    Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak

    The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties

    No full text
    Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak

    Three dimensional mesh model of a right coronary artery.

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    <p>Panel (A) shows the inflow (arrow) at the ostium of a right coronary artery and outflows of two small side branches (chevrons). Panel (B) shows a magnification of the outflow shown in panel (A). Notice the three boundary layers with small cell size (arrow head), necessary to accurately calculate ESS. Towards the inner lumen of the vessel, cell size increases to reduce the total number of cells and thus computation time.</p

    Wall thickness.

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    <p>Mean wall thickness was highest in quartile 1 (low endothelial shear stress (ESS)) and lowest in quartiles 2 and 3 (intermediate ESS). Vessel wall thickness in quartile 4 (high ESS) was in between. Differences were not significant between quartile 2 and 3 (p = 0.15). All other differences were statistically significant (p < 0.001).</p><p>SD: standard deviation</p><p>Wall thickness.</p

    Distribution of plaque tissue composition in areas exposed to different levels of endothelial shear stress (ESS).

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    <p>Panel (A) shows an example of an early, panel (B) an example of a more advanced atherosclerotic lesion as assessed by intravascular ultrasound radiofrequency data analysis. Fibrous tissue is represented by dark green, fibrofatty tissue by light green, necrotic tissue by red and calcified tissue by white colour. We observed a significantly higher amount of fibrofatty (*) tissue in areas exposed to the lowest level of ESS (quartile 1) in comparison to low-intermediate ESS (quartile 2), intermediate-high ESS (quartile 3) or high ESS (quartile 4) (p≤0.023) (C). There was no difference in the amount of other tissue types depending on the level of ESS (p≥0.061).</p

    Plaque prevalence.

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    <p>There was a significantly higher prevalence of atherosclerotic plaques in areas of very low (quartile 1) and very high (quartile 2) endothelial shear stress (ESS) as compared to areas of intermediate ESS (quartile 2 and 3) (p < 0.001). Furthermore plaque prevalence was higher in quartile 1 compared to quartile 4 (p < 0.001). Differences between quartile 2 and 3 were not significant (p = 0.56).</p><p>Plaque prevalence.</p

    Color encoded illustration of endothelial shear stress (ESS) on a 3D model of a right coronary artery obtained by coronary computed tomography angiography.

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    <p>After segmentation side branches were cut 1–2 cm from the branching point. The volume mesh consisted of about 400,000 polyhedral cells. The Navier-Stokes equations were solved by the finite element method. The level of ESS increases from blue to red as shown in the color map on the left.</p

    Optical measurement of heart rates in response to isoprenaline, tricaine and sodium nitroprusside (SNP) <i>in vivo</i>.

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    <p>The heart rates of the same fish were followed over a period of 30 min at intervals of 2 min. Values obtained between t<sub>10</sub> and t<sub>30</sub> are expressed as percentage of the baseline (t<sub>0</sub>-t<sub>8</sub>). Drug solutions were administered at 9.5 min (indicated by the dashed line).</p
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