Dataset Information

Dataset on hydrophobicity indices and differential scanning calorimetry thermograms for poly(HEMA)-based hydrogels.

ABSTRACT: Hydrophobicity indices for poly(HEMA)-based hydrogels: HEMA, AEMA, and DMAEMA calculated from two different methods: 1) Partition coefficients, and 2) Kyte-Doolittle scale are depicted. Thermograms from differential scanning calorimetry of poly(HEMA)-based hydrogels containing AEMA, DMAEMA, and a mixture of AEMA and DMAEMA are included to represent the glass transition temperature (T_g) values of the hydrogels. More information on the methodology to calculate the hydrophobicity indices using the aforementioned methods and the procedure for using a differential scanning calorimeter and analysis of a thermogram is described. Details of how the changes in the feed composition of poly(HEMA)-based hydrogels was made is provided in the research article 'MOLECULAR ENGINEERING OF POLY(HEMA-co-PEGMA)-BASED HYDROGELS: ROLE OF MINOR AEMA AND DMAEMA INCLUSION' (Bhat et al., 2019).[1].

SUBMITTER: Bhat A

PROVIDER: S-EPMC6517576 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Dataset on hydrophobicity indices and differential scanning calorimetry thermograms for poly(HEMA)-based hydrogels.

Bhat Ankita A Smith Blake B Dinu Cerasela-Zoica CZ Guiseppi-Elie Anthony A

Data in brief 20190428

Hydrophobicity indices for poly(HEMA)-based hydrogels: HEMA, AEMA, and DMAEMA calculated from two different methods: 1) Partition coefficients, and 2) Kyte-Doolittle scale are depicted. Thermograms from differential scanning calorimetry of poly(HEMA)-based hydrogels containing AEMA, DMAEMA, and a mixture of AEMA and DMAEMA are included to represent the glass transition temperature (T<sub>g</sub>) values of the hydrogels. More information on the methodology to calculate the hydrophobicity indices ...[more]

PMID: 31193140

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Project description:The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Dataset Information

Dataset on hydrophobicity indices and differential scanning calorimetry thermograms for poly(HEMA)-based hydrogels.

Publications

Dataset on hydrophobicity indices and differential scanning calorimetry thermograms for poly(HEMA)-based hydrogels.

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