Analysis of dose distribution reproducibility based on a fluence map of in vivo transit dose using an electronic portal imaging device

Morphological changes can affect distribution of dose in patients. Determination of the dose distribution changes for each fraction radiotherapy can be done by relativein vivodosimetry (IVD). This study analysed the distribution of doses per fraction based on the fluence map recorded by the electronic portal imaging device (EPID) of the patient's transit dose. This research examined cases involving the cervix, breast, and nasopharynx. Transit dose analysis was performed by calculating the gamma index (GI) with composite and field-by-field methods. The gamma passing rate (GPR) value was assessed for its correlation with the subject's body weight. In the case of the nasopharynx, breast, and cervix, the GPR value decreased as the fraction increased. In the case of the nasopharynx, the correlation between the GPR and fraction radiotherapy showed no difference when using either composite or field-by-field methods. However, in cases involving the cervix and breast, there was a difference in the correlation values between the composite and field-by-field methods, where the subject had a significant correlation (p< 0.05) when it was done using a field-by-field method. In addition, the nasopharynx had the highest number of subjects with significant correlation (p< 0.05) between GPR and body weight, followed by the cervix and breast. In the nasopharynx, breast, and cervix, the reproducibility of the dose distribution decreased. This decreased reproducibility was associated with changes in body weight.

An Indonesian Adaptation of The Students’ Preparedness for University e-Learning Environment Questionnaire

Although most students are digital natives, online learning requires different skills as compared to conventional face-to-face learning. This paper aims to adapt and test the reliability and validity of the Students’ Preparedness for the University e-Learning Environment questionnaire developed by Parkes et al. into Bahasa Indonesia.  The original questionnaire covers a wide range of competencies relevant to e-learning preparedness for university e-learning environments in three dimensions. Prior to reliability and validity checking, pilot testing is conducted to test the unidimesionality of the instrument and the rating scale. An item-match analysis test is also carried out to observe the suitability of each item. Then, the final version of the questionnaire is administered to a large representative sample of respondents for whom the questionnaire is intended. The results show that, with a total of 1446 students from a public university in Indonesia as respondents, the adapted questionnaire is valid and reliable.

Correction: Triclustering method for finding biomarkers in human immunodeficiency virus-1 gene expression data

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Iterative bicluster-based Bayesian principal component analysis and least squares for missing-value imputation in microarray and RNA-sequencing data

Microarray and RNA-sequencing (RNA-seq) techniques each produce gene expression data that can be expressed as a matrix that often contains missing values. Thus, a process of missing-value imputation that uses coherence information of the dataset is necessary. Existing imputation methods, such as iterative bicluster-based least squares (bi-iLS), use biclustering to estimate the missing values because genes are only similar under correlative experimental conditions. Also, they use the row average to obtain a temporary complete matrix, but the use of the row average is considered to be a flaw. The row average cannot reflect the real structure of the dataset because the row average only uses the information of an individual row. Therefore, we propose the use of Bayesian principal component analysis (BPCA) to obtain the temporary complete matrix instead of using the row average in bi-iLS. This alteration produces new missing values imputation method called iterative bicluster-based Bayesian principal component analysis and least squares (bi-BPCA-iLS). Several experiments have been conducted on two-dimension independent gene expression datasets, which are microarray (e.g., cell-cycle expression dataset of yeast saccharomyces cerevisiae) and RNA-seq (gene expression data from schizosaccharomyces pombe) datasets. In the case of the microarray dataset, our proposed bi-BPCA-iLS method showed a significant overall improvement in the normalized root mean square error (NRMSE) values of 10.6% from the local least squares (LLS) and 0.6% from the bi-iLS. In the case of the RNA-seq dataset, our proposed bi-BPCA-iLS method showed an overall improvement in the NRMSE values of 8.2% from the LLS and 3.1% from the bi-iLS. The additional computational time of bi-BPCA-iLS is not significant compared to bi-iLS.

Triclustering method for finding biomarkers in human immunodeficiency virus-1 gene expression data

HIV-1 is a virus that destroys CD4 + cells in the body's immune system, causing a drastic decline in immune system performance. Analysis of HIV-1 gene expression data is urgently needed. Microarray technology is used to analyze gene expression data by measuring the expression of thousands of genes in various conditions. The gene expression series data, which are formed in three dimensions, are analyzed using triclustering. Triclustering is an analysis technique for 3D data that aims to group data simultaneously into rows and columns across different times/conditions. The result of this technique is called a tricluster. A tricluster is a subspace in the form of a subset of rows, columns, and time/conditions. In this study, we used the δ-Trimax, THD Tricluster, and MOEA methods by applying different measures, namely, transposed virtual error, the New Residue Score, and the Multi Slope Measure. The gene expression data consisted of 22,283 probe gene IDs, 40 observations, and four conditions: normal, acute, chronic, and non-progressor. Tricluster evaluation was carried out based on intertemporal homogeneity. An analysis of the probe ID gene that affects AIDS was carried out through this triclustering process. Based on this analysis, a gene symbol which is biomarkers associated with AIDS due to HIV-1, HLA-C, was found in every condition for normal, acute, chronic, and non-progressive HIV-1 patients.

Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Unlike other typical clustering analysis, which considers column only, biclustering analysis processes a matrix into sub-matrices based on rows and columns simultaneously. One method of bicluster analysis uses the probabilistic model, like the plaid model, that provides overlapping bicluster. The plaid model calculates the value of an element given from a particular sub-matrix for each cell; thus, the value can be seen as the number of contributions of a particular bicluster. The algorithm begins with preparing the input data as a matrix, then an initial model is assessed and makes a residual matrix from the model. After that, we determine bicluster candidates, which are evaluated for its effect parameters and bicluster membership parameters. Finally, the bicluster candidate is pruned to give the optimal bicluster. We implemented the algorithm on gene expression dataset of colon cancer, where the rows and columns contain observations and types of genes, respectively. We carried out in six distinct scenarios in which each scenario uses different model parameters and threshold values. We measured the results using Jaccard index and coherence variance. Our experiments show that biclustering analysis on a model with mean, row, and column effects of colon cancer data output low coherence variance.

Application of BiMax, POLS, and LCM-MBC to Find Bicluster on Interactions Protein between HIV-1 and Human

Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data. Further, biclustering results can be analyzed from a biological perspective. Biclustering can also be used for protein-protein interaction. In protein-protein interaction, biclustering can cluster interactions based on rows and columns. In this research, we applied three biclustering algorithms based on graph approach, Binary inclusion-Maximal (BiMax), local search framework based on pairs operation (POLS), and (LCM-MBC) to clustering data of protein-protein interaction between HIV-1 and human. We change the interaction protein-protein interaction data into binary then divided into two datasets called HV positive and HV negative. Then compare the biclustering results of each dataset using heatmap and analyze them with GO terms. From dataset HV positive, BiMax found 30 biclusters, LCM-MBC 31 biclusters, and POLS 13 biclusters. From dataset HV negative, BiMax found eight biclusters, LCM-MBC 14 bicluster, and POLS 10 biclusters. Based on the results of the heatmap, all bicluster entry from BiMax is a protein that interacts, whereas biclusters entry of LCM-MBC and POLS still have proteins that do not interact. It can be concluded that BiMax algorithm is good for clustering protein-protein interaction, especially for binary data.