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README.md

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-license: mit
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+---
+license: mit
+pretty_name: >-
+  hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000
+  proteomic experiments. 
+repo: https://github.com/KDrewLab/huMAP3.0_analysis
+---
+# hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. 
+Proteins interact with each other and organize themselves into macromolecular machines (ie. complexes) 
+to carry out essential functions of the cell. We have a good understanding of a few complexes such as 
+the proteasome and the ribosome but currently we have an incomplete view of all protein complexes as 
+well as their functions. The hu.MAP attempts to address this lack of understanding by integrating several
+large scale protein interaction datasets to obtain the most comprehensive view of protein complexes. 
+In hu.MAP 3.0 we integrated large scale affinity purification mass spectrometry (AP/MS) datasets from Bioplex, 
+Bioplex2.0, Bioplex3.0, Boldt et al. and Hein et al., large scale biochemical fractionation data (Wan et al.), 
+proximity labeling data (Gupta et al., Youn et al.), and RNA hairpin pulldown data (Treiber et al.) to produce 
+a complex map with over 15k complexes. 
+
+
+## Funding
+
+  NIH R00, NSF/BBSRC
+  
+## Citation
+
+    Samantha N. Fischer, Erin R Claussen, Savvas Kourtis, Sara Sdelci, Sandra Orchard, Henning Hermjakob, Georg Kustatscher, Kevin Drew hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments BioRxiv https://doi.org/10.1101/2024.10.11.617930
+
+## References
+
+    Kevin Drew, John B. Wallingford, Edward M. Marcotte hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies Mol Syst Biol (2021)17:e10016https://doi.org/10.15252/msb.202010016
+    Kevin Drew, Chanjae Lee, Ryan L Huizar, Fan Tu, Blake Borgeson, Claire D McWhite, Yun Ma, John B Wallingford, Edward M Marcotte Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes. Molecular Systems Biology (2017) 13, 932. DOI 10.15252/msb.20167490
+    Huttlin et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome Cell. 2021 May 27;184(11):3022-3040.e28. doi: 10.1016/j.cell.2021.04.011.
+    Huttlin et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017 May 25;545(7655):505-509. DOI: 10.1038/nature22366.
+    Treiber et al. A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis.. Mol Cell. 2017 Apr 20;66(2):270-284.e13. doi: 10.1016/j.molcel.2017.03.014.
+    Boldt et al. An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 May 13;7:11491. doi: 10.1038/ncomms11491.
+    Youn et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol Cell. 2018 Feb 1;69(3):517-532.e11. doi: 10.1016/j.molcel.2017.12.020.
+    Gupta et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell. 2015 Dec 3;163(6):1484-99. doi: 10.1016/j.cell.2015.10.065.
+    Wan, Borgeson et al. Panorama of ancient metazoan macromolecular complexes. Nature. 2015 Sep 17;525(7569):339-44. doi: 10.1038/nature14877. Epub 2015 Sep 7.
+    Hein et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell. 2015 Oct 22;163(3):712-23. doi: 10.1016/j.cell.2015.09.053. Epub 2015 Oct 22.
+    Huttlin et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell. 2015 Jul 16;162(2):425-40. doi: 10.1016/j.cell.2015.06.043.
+    Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199.
+
+## Associated code
+
+  Code examples using the hu.MAP 3.0 model and downstream analysis can be found on our
+  [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis)
+  All feature matrices and associated files can be found in the [sfisch/hu.MAP3.0](https://huggingface.co/datasets/sfisch/hu.MAP3.0) datasets
+  repo
+
+# Usage
+
+## Accessing the model
+hu.MAP 3.0 was built using the auto-ML tool [AutoGluon](https://auto.gluon.ai/stable/index.html) and the [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html)
+module is used train, test, and make predictions with the model. 
+
+This can be downloaded using the following:
+
+      $ pip install autogluon==0.4.0
+
+Then it can be imported as:
+
+    >>> from autogluon.tabular import TabularPredictor
+Note that to perform operations with our model the **0.4.0 version** must be used
+
+Our trained model can be downloaded through Huggingface using [huggingface_hub](https://huggingface.co/docs/hub/index)
+
+    >>> from huggingface_hub import snapshot_download
+
+    >>> model_dir = snapshot_download(repo_id="sfisch/hu.MAP3.0_AutoGluon")
+
+    >>> predictor = TabularPredictor.load(f"{model_dir}/huMAP3_20230503_complexportal_subset10kNEG_notScaled_accuracy")
+
+To use the model and make predictions, we show two full code examples using the [full feature matrix](https://github.com/KDrewLab/huMAP3.0_analysis/blob/main/huMAP3.0_model_devel/generating_predictions_w_hu.MAP3.0.ipynb) 
+and the [test feature matrix](https://github.com/KDrewLab/huMAP3.0_analysis/blob/main/huMAP3.0_model_devel/humap3_test_20230503.pairsWprob) in jupyter notebooks.
+
+All feature matrices can be pulled using the 'datasets' module from HuggingFace and examples of that are seen on our [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis/tree/main/huMAP3.0_model_devel)
+and on our HuggingFace dataset repo [sfisch/hu.MAP3.0](https://huggingface.co/datasets/sfisch/hu.MAP3.0)