The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug–drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug–drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project.

Experience the creative power of conversations with this insightful dataset! With 7k conversations, this dataset has been designed to explore a vibrant range of dialogue modes, from exuding personality and demonstrating empathy to showing off your knowledge. Each record within this data is structured according to personas, additional context, previous utterance, free messages, guided messages and suggestions fields – giving you an array of options for stimulating topics that will inspire creative conversations. Unlock your conversational potential today and use this data-set to craft unique dialogues that get people talking!

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset This dataset can be used to train and validate conversational models and explore the potential of dynamic dialogues. To get started, use the following steps:

Load the datasets [validation, train, test] into your preferred Machine Learning framework or programming language (Python, R). Explore the data fields for each conversation: personas, additional context, previous utterance, free messages, suggested messages etc. Use this information to create a model that is capable of recognizing conversations by accentuating personality traits or knowledge topics based on past conversations within a specific context. Test and Evaluate your model with data points from validation/test dataset to measure its performance on training or unseen conversations as well as its ability to learn new concepts and respond appropriately in creative ways while maintaining its original character within different contexts! Research Ideas Generating creative responses using creative personas and additional context. Creating knowledge-based conversations based on the data provided in the suggestions field, which contains recommended messages for the conversation. Developing a chatbot that provides personalized responses and empathetic support to users by utilizing the personas and free message fields in this dataset

License

CC0

Original Data Source: Blended Skill Talk (1 On 1 Conversations)

Sustainable application of nuclear energy requires efficient sequestration of actinides, which relies on extensive understanding of actinide–ligand interactions to guide rational design of ligands. Currently, the design of novel ligands adopts mainly the time-consuming and labor-intensive trial-and-error strategy and is impeded by the heavy-metal toxicity and radioactivity of actinides. The advancement of machine learning techniques brings new opportunities given a sensible choice of appropriate descriptors. In this study, by using the binding equilibrium constant (log K1) to represent the binding affinity of ligand with metal ion, 14 typical algorithms were used to train machine learning models toward accurate predictions of log K1 between actinide ions and ligands, among which the Gradient Boosting model outperforms the others, and the most relevant 15 out of the 282 descriptors of ligands, metals, and solvents were identified, encompassing key physicochemical properties of ligands, solvents, and metals. The Gradient Boosting model achieved R2 values of 0.98 and 0.93 on the training and test sets, respectively, showing its ability to establish qualitative correlations between the features and log K1 for accurate prediction of log K1 values. The impact of these properties on log K1 values was discussed, and a quantitative correlation was derived using the SISSO model. The model was then applied to eight recently reported ligands for Am3+, Cm3+, and Th4+ outside of the training set, and the predicted values agreed with the experimental ones. This study enriches the understanding of the fundamental properties of actinide–ligand interactions and demonstrates the feasibility of machine-learning-assisted discovery and design of ligands for actinides.

Feature preparation Preprocessing was applied to the data, such as creating dummy variables and performing transformations (centering, scaling, YeoJohnson) using the preProcess() function from the “caret” package in R. The correlation among the variables was examined and no serious multicollinearity problems were found. A stepwise variable selection was performed using a logistic regression model. The final set of variables included: Demographic: age, body mass index, sex, ethnicity, smoking History of disease: heart disease, migraine, insomnia, gastrointestinal disease, COVID-19 history: covid vaccination, rashes, conjunctivitis, shortness of breath, chest pain, cough, runny nose, dysgeusia, muscle and joint pain, fatigue, fever ,COVID-19 reinfection, and ICU admission. These variables were used to train and test various machine-learning models Model selection and training The data was randomly split into 80% training and 20% testing subsets. The “h2o” package in R version 4.3.1 was employed to implement different algorithms. AutoML was first used, which automatically explored a range of models with different configurations. Gradient Boosting Machines (GBM), Random Forest (RF), and Regularized Generalized Linear Model (GLM) were identified as the best-performing models on our data and their parameters were fine-tuned. An ensemble method that stacked different models together was also used, as it could sometimes improve the accuracy. The models were evaluated using the area under the curve (AUC) and C-statistics as diagnostic measures. The model with the highest AUC was selected for further analysis using the confusion matrix, accuracy, sensitivity, specificity, and F1 and F2 scores. The optimal prediction threshold was determined by plotting the sensitivity, specificity, and accuracy and choosing the point of intersection as it balanced the trade-off between the three metrics. The model’s predictions were also plotted, and the quantile ranges were used to classify the model’s prediction as follows: > 1st quantile, > 2nd quantile, > 3rd quartile and < 3rd quartile (very low, low, moderate, high) respectively. Metric Formula C-statistics (TPR + TNR - 1) / 2 Sensitivity/Recall TP / (TP + FN) Specificity TN / (TN + FP) Accuracy (TP + TN) / (TP + TN + FP + FN) F1 score 2 * (precision * recall) / (precision + recall) Model interpretation We used the variable importance plot, which is a measure of how much each variable contributes to the prediction power of a machine learning model. In H2O package, variable importance for GBM and RF is calculated by measuring the decrease in the model's error when a variable is split on. The more a variable's split decreases the error, the more important that variable is considered to be. The error is calculated using the following formula: 𝑆𝐸=𝑀𝑆𝐸∗𝑁=𝑉𝐴𝑅∗𝑁 and then it is scaled between 0 and 1 and plotted. Also, we used The SHAP summary plot which is a graphical tool to visualize the impact of input features on the prediction of a machine learning model. SHAP stands for SHapley Additive exPlanations, a method to calculate the contribution of each feature to the prediction by averaging over all possible subsets of features [28]. SHAP summary plot shows the distribution of the SHAP values for each feature across the data instances. We use the h2o.shap_summary_plot() function in R to generate the SHAP summary plot for our GBM model. We pass the model object and the test data as arguments, and optionally specify the columns (features) we want to include in the plot. The plot shows the SHAP values for each feature on the x-axis, and the features on the y-axis. The color indicates whether the feature value is low (blue) or high (red). The plot also shows the distribution of the feature values as a density plot on the right.

Description:

👉 Download the dataset here

This dataset was created to serve as an easy-to-use image dataset, perfect for experimenting with object detection algorithms. The main goal was to provide a simplified dataset that allows for quick setup and minimal effort in exploratory data analysis (EDA). This dataset is ideal for users who want to test and compare object detection models without spending too much time navigating complex data structures. Unlike datasets like chest x-rays, which require expert interpretation to evaluate model performance, the simplicity of balloon detection enables users to visually verify predictions without domain expertise.

The original Balloon dataset was more complex, as it was split into separate training and testing sets, with annotations stored in two separate JSON files. To streamline the experience, this updated version of the dataset merges all images into a single folder and replaces the JSON annotations with a single, easy-to-use CSV file. This new format ensures that the dataset can be loaded seamlessly with tools like Pandas, simplifying the workflow for researchers and developers.

Download Dataset

The dataset contains a total of 74 high-quality JPG images. Each featuring one or more balloons in different scenes and contexts. Accompanying the images is a CSV file that provides annotation data. Such as bounding box coordinates and labels for each balloon within the images. This structure makes the dataset easily accessible for a range of machine learning and computer vision tasks. Including object detection and image classification. The dataset is versatile and can be used to test algorithms like YOLO, Faster R-CNN, SSD, or other popular object detection models.

Key Features:

Image Format: 74 JPG images, ensuring high compatibility with most machine learning frameworks.

Annotations: A single CSV file that contains structure data. Including bounding box coordinates, class labels, and image file names, which can be load with Python libraries like Pandas.

Simplicity: Design for users to quickly start training object detection models without needing to preprocess or deeply explore the dataset.

Variety: The images feature balloons in various sizes, colors, and scenes, making it suitable for testing the robustness of detection models.

This dataset is sourced from Kaggle.

Cross-validation is a common method to validate a QSAR model. In cross-validation, some compounds are held out as a test set, while the remaining compounds form a training set. A model is built from the training set, and the test set compounds are predicted on that model. The agreement of the predicted and observed activity values of the test set (measured by, say, R2) is an estimate of the self-consistency of the model and is sometimes taken as an indication of the predictivity of the model. This estimate of predictivity can be optimistic or pessimistic compared to true prospective prediction, depending how compounds in the test set are selected. Here, we show that time-split selection gives an R2 that is more like that of true prospective prediction than the R2 from random selection (too optimistic) or from our analog of leave-class-out selection (too pessimistic). Time-split selection should be used in addition to random selection as a standard for cross-validation in QSAR model building.

physioDL: A dataset for geomorphic deep learning representing a scene classification task (predict physiographic region in which a hilshade occurs)Purpose: Datasets for geomorphic deep learning. Predict the physiographic region of an area based on a hillshade image. Terrain data were derived from the 30 m (1 arc-second) 3DEP product across the entirety of CONUS. Each chip has a spatial resolution of 30 m and 256 rows and columns of pixels. As a result, each chip measures 7,680 meters-by-7,680 meters. Two datasets are provided. Chips in the hs folder represent a multidirectional hillshade while chips in the ths folder represent a tinted multidirectional hillshade. Data are represented in 8-bit (0 to 255 scale, integer values). Data are projected to the Web Mercator projection relative to the WGS84 datum. Data were split into training, test, and validation partitions using stratified random sampling by region. 70% of the samples per region were selected for training, 15% for testing, and 15% for validation. There are a total of 16,325 chips. The following 22 physiographic regions are represented: "ADIRONDACK" , "APPALACHIAN PLATEAUS", "BASIN AND RANGE", "BLUE RIDGE", "CASCADE-SIERRA MOUNTAINS", "CENTRAL LOWLAND", "COASTAL PLAIN", "COLORADO PLATEAUS", "COLUMBIA PLATEAU", "GREAT PLAINS", "INTERIOR LOW PLATEAUS", "MIDDLE ROCKY MOUNTAINS", "NEW ENGLAND", "NORTHERN ROCKY MOUNTAINS", "OUACHITA", "OZARK PLATEAUS", "PACIFIC BORDER", and "PIEDMONT", "SOUTHERN ROCKY MOUNTAINS", "SUPERIOR UPLAND", "VALLEY AND RIDGE", "WYOMING BASIN". Input digital terrain models and hillshades are not provided due to the large file size (> 100GB). FilesphysioDL.csv: Table listing all image chips and associated physiographic region (id = unique ID for each chip; region = physiographic region; fnameHS = file name of associated chip in hs folder; fnameTHS = file name of associated chip in ths folder; set = data split (train, test, or validation).chipCounts.csv: Number of chips in each data partition per physiographic province. map.png: Map of data.makeChips.R: R script used to process the data into image chips and create CSV files.inputVectorschipBounds.shp = square extent of each chipchipCenters.shp = center coordinate of each chipprovinces.shp = physiographic provincesprovinces10km.shp = physiographic provinces with a 10 km negative buffer