Biomedical and Health Sciences

During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.

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Blood pressure (BP) measurement is an indispensable parameter for diagnosing many diseases, e.g., heart attack, stroke, vascular disease, and kidney disease. All these disease sometimes lead to fatal injuries due to the failure of vital human organs. The measurement of BP using BP device has several inaccuracies due to the non-availability of SI traceable calibration systems, which can also meet the criteria of International Organization of Legal Metrology (OIML) particularly OIML R 148 and OIML R 149 guidelines.

The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.

This dataset is from our study that challenges the conventional interpretation of electrocardiogram (ECG) measurements, suggesting a paradigm shift in our understanding. Traditionally, ECGs are seen as reflections of the electric potential on the body's surface, but we propose an alternative hypothesis: ECGs may represent the gradient of the electric potential rather than the potential itself. To investigate this, we use computational methods based on the boundary element method (BEM) within the SCIRun numerical package.