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Bioinformatics Laboratory

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Bioinformatics Laboratory is a large research group at the Faculty of Computer and Information Science, which performs research in data science. We like to combine machine learning and data visualization, and craft techniques for exploratory data analysis and explainable artificial intelligence. Our aim is to design intuitive, useful, and visually appealing approaches for both

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iCLIPro: read overlap testing — iCLIPro 0.1 | Data Mining docs. | scOrange – Download Single Cell Analysis | scOrange – Workflows Single Cell Analysis | Nacionalna raziskava o razširjenosti COVID-19 | Predict New Cell Types Based on singlecell. | scOrange – Documentation Single Cell Analysis | download. |

Iz vsebine

If no random barcode information is available, then iCLIPro will most likely be able to work with the original read names. In such case, please check that the read names do not include any text that conforms to the rules for specifying random barcode as it may mislead iCLIPro. Functional Genomics Workshop October 15 -16, 2014 Welcome to the hands-on Data Mining workshop! This three-hour workshop is designed for students and researchers in molecular Download Orange add-on. Orange3-SingleCell can be installed directly from Orange add-on manager. You can find add-on manager in Options menu. Anaconda Orange Data Mining Toolbox. Data Visualization with t-SNE Loads the data from scOrange’s single-cell datasets server and feeds it into a spreadsheet viewer and a t-SNE visualization. Sledite poteku raziskave o razširjenosti bolezni COVID-19 v živo. Poročamo o metodologiji, izboru populacije, izboru diagnostičnih testov, in rezultatih raziskave. Preberi več Prva objava rezultatov Nacionalne raziskave o razširjenosti bolezni COVID-19 v Sloveniji v priznani mednarodni medicinski reviji Orange Data Mining Toolbox. Batch effect removal, machine learning, cell-embedding, and transfer learning – these are all techniques useful in the classification of new cells, and scOrange has them all. Widget Catalog Orange widgets are building blocks of data analysis workflows we assemble using visual programming. scOrange extends Orange’s set with widgets that are specific for single-cell expression data analysis. Automatic Embedding of New Cells Onto an Existing Landscape Embed the single cell datasets from an AML patient collected on different timepoints in therapy onto a t-SNE of a healthy individual’s bone marrow cells clustered according to cell type with the Annotator widget.

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