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Python

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Python is a high-level programming language used for a variety of applications, including data analysis, web development, and automation. It is designed to be easy to read and write, with a focus on simplicity and readability. Python provides a wide range of built-in libraries and modules, making it a powerful tool for data manipulation, visualization, and machine learning.

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8 protocols using Python

Analyses were generated using Python (version 3.6.5; Anaconda, Inc., Austin, TX)/18.04.1-Ubuntu SMP System (8 cores 64GB RAM; Canonical Ltd, London, UK)/5.4.0-1043-Google Cloud Platform server (Google LLC, Mountain View, CA).
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We used confusion matrices to compare the prediction of DL models with the reference standard (ACD < 2.4 mm). The matrices included the area under the receiver operating characteristic curve (AUC) of the receiver operating characteristic (ROC) curves, accuracy, sensitivity, and specificity. The ROC curve was plotted by applying different thresholds to the output score maps from the DL model. The closer the AUC is to 1, the better the DL model. Accuracy, sensitivity, and specificity are expressed as follows:
Accuracy=TP+TNTP+TN+FN+FP, Sensitivity=TPTP+FN, Specificity=TNTN+FP,
TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively. Python (version 3.7) and Scikit_learn modules (Anaconda, version 1.9.12, Continuum Analytics) were used to perform the statistical analysis.
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All graphs and analyses were generated in Python (version 3.7.10, Anaconda Individual Edition) (Figures 1C; 2B,C,D,E; 3A; 4A; 5A), R (version 3.6.0, R Development Core Team) (Figure 1C, 2B), Orange (Version 3.0, Orange Developing Team) (Figures 1B, 4A, 5A) (Demsar et al. 2013 ) and Prism (version 5.0, GraphPad) (Figures 1D, 2B, 2C, 2D, 2E, 3A, 3B, 4B, 4C, 4D, 5B, 5C, 6B). ToxCast (version 3.1) data was downloaded from https://www.epa.gov/chemical-research/exploring-toxcast-data, and the reported logAC50 (Activity Concentration 50) was used for analysis based on the data from Judson et al. 2015 (link). Nonparametric analysis of variance (ANOVA) using vehicle as comparator was used when indicated in the figure legends, and groups were considered significant at p<0.05 .
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The evaluation matrices used to assess the performance of the semi-supervised GANs and the supervised DL models included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (95% CIs). The accuracy, sensitivity, and specificity of the DL algorithms for detecting closed angles were computed according to the reference standard. All statistical analyses were performed using the Python (version 3.7) and Scikit_learn modules (Anaconda, version 1.9.12, Continuum Analytics)
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Observed monthly Storage, inflow, evaporation rate, and release data were collected for the years 2001 to 2019 [Source: Lembaga Urus Air Selangor (LUAS) or Selangor Water Management Authority]. The inflow was classified into three categories, namely high, medium, and low flows, which can be found in Ref.15 . According to Ref.15 , Puncak Niaga (M) Sdn Bhd is the company that is involved with the running of the dam operations. The HHO and OBL-HHO were utilised to optimise the monthly reservoir release operation at the KGD. This was accomplished with reference to the monthly observed dataset of inflow, demand, storage, and losses (measured in a million cubic meters, MCM). The optimisation process with the datasets mentioned, was performed with the Python (Anaconda) software.
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PRGS was presented as mean ± SD for patients having four biopsies at least, and in addition, the highest and lowest grading was reported [4 (link)]. Continuous variables were presented as mean with standard deviation (SD) or median with range or interquartile range (IQR) for skewed data. Categorical variables were reported as frequencies (%) and compared with the chi-square test. Depending on the normality of distribution, Student’s t-test and Mann–Whitney U test or Wilcoxon signed ranked test were used for float comparisons. Statistical correlations were tested by use of Pearson’s rank correlation. A level of 0.05 was considered statistically significant. Statistical analyses were performed, and figures were produced with SPSS v20 software (Chicago, IL, USA), GraphPad Prism 7 (GraphPad Software, Inc., La Jolla, CA, USA), Python, NumPy, Pandas, and Seaborne (Anaconda, Berlin, Germany).
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The analysis was performed in an Anaconda Python environment with the following packages: Python (version 3.9), BioPython (version 1.79), Matplotlib (version 3.5.1), Plotly (version 4.14.3), Scikit-learn (version 1.0.2), Pandas (version 1.4.1), Pyteomics (version 4.5.3), Matplotlib-venn (version 0.11.6), Seaborn (version 0.11.2), UMAP-learn (version 0.5.2), HDBSCAN (version 0.8.28), and xlrd (version 2.0.1), Logomaker v0.8. An R environment within Anaconda was also used, consisting of R-essentials (version 4.1), R-base (version 4.1.2), and R-devtools (version 2.4.3). These tools and environments enabled data processing and creation of visualizations.
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Statistical analysis used in this study was completed through SPSS 23 and R 4.1.3. For continuous variables, normality was first tested through the Kolmogorov-Smirnov test, means and standard deviations were used if they followed a normal distribution (ANOVA was used to compare continuous variables between groups); if they did not follow a normal distribution, the median (quartiles) were used (non-parametric tests were used to compare continuous variables between groups). For categorical variables, counts and percentages were used (the chi-square test was used to compare categorical data), and all statistical tests were two-sided (P < 0.05 was considered statistically significant). Processes such as feature engineering, data balancing and prediction model construction were done in python (Anaconda, Version 3.8), while TensorFlow 2.11.0; scikit-learn 1.1.3; keras 2.11.0; pandas1.5.2; numpy, etc. Package installation is complete.
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