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- 01GK49T9CJSAQSXCJ1FE24Q8MW classification V.
- 01GK49T9CJSAQSXCJ1FE24Q8MW date "2023".
- 01GK49T9CJSAQSXCJ1FE24Q8MW language "eng".
- 01GK49T9CJSAQSXCJ1FE24Q8MW type misc.
- 01GK49T9CJSAQSXCJ1FE24Q8MW hasPart 01GK49W6QZQ11JG5QRZJM6PA48.pdf.
- 01GK49T9CJSAQSXCJ1FE24Q8MW subject "Biology and Life Sciences".
- 01GK49T9CJSAQSXCJ1FE24Q8MW subject "Technology and Engineering".
- 01GK49T9CJSAQSXCJ1FE24Q8MW doi "10.1101/2022.07.06.499022".
- 01GK49T9CJSAQSXCJ1FE24Q8MW abstract "Protein networks are commonly used for understanding the interplay between proteins in the cell as well as for visualizing omics data. Unfortunately, most existing high-quality networks are heavily biased by data availability, in the sense that well-studied proteins have many more interactions than understudied proteins. To create networks that can help elucidate functions for the latter, we must start from data that are not affected by this literature bias, in other words, from omics data such as single cell RNA-seq (scRNA-seq) and proteomics. While networks can be inferred from such data through simple co-expression analysis, this approach does not work well due to high sparseness (many transcripts/proteins are not consistently observed in each cell/sample) and redundancy (many similar cells/samples are analyzed) of such data. We have therefore developed FAVA, Functional Associations using Variational Autoencoders, which deals with both issues by compressing these high-dimensional data into a dense, low-dimensional latent space. We demonstrate that calculating correlations in this latent space results in much improved networks compared to the original representation for large-scale scRNA-seq and proteomics data from the Human Protein Atlas, and from PRIDE, respectively. We show that these networks, which given the nature of the input data should be free of literature bias, indeed have much better coverage of understudied proteins than existing networks.".
- 01GK49T9CJSAQSXCJ1FE24Q8MW author 21800212-F0EE-11E1-A9DE-61C894A0A6B4.
- 01GK49T9CJSAQSXCJ1FE24Q8MW author 2FFF4F10-F855-11E5-94C6-EB0DB5D1D7B1.
- 01GK49T9CJSAQSXCJ1FE24Q8MW author urn:uuid:75209098-006b-4c43-8a18-3f06349f6f5b.
- 01GK49T9CJSAQSXCJ1FE24Q8MW author urn:uuid:7b671aec-a120-4035-a7a3-f9cb211886ff.
- 01GK49T9CJSAQSXCJ1FE24Q8MW author urn:uuid:7f5878b9-17cc-4cba-a8f9-45829ee3b524.
- 01GK49T9CJSAQSXCJ1FE24Q8MW author urn:uuid:acb56590-582d-443f-86de-473d6be08388.
- 01GK49T9CJSAQSXCJ1FE24Q8MW author urn:uuid:af9b57ef-f811-495f-8fad-de65554b8f2f.
- 01GK49T9CJSAQSXCJ1FE24Q8MW dateCreated "2022-11-30T12:57:32Z".
- 01GK49T9CJSAQSXCJ1FE24Q8MW dateModified "2024-10-29T17:59:54Z".
- 01GK49T9CJSAQSXCJ1FE24Q8MW name "FAVA : high-quality functional association networks inferred from scRNA-seq and proteomics data".
- 01GK49T9CJSAQSXCJ1FE24Q8MW publisher urn:uuid:2f46349f-2106-4802-886a-847c9d5a31f1.
- 01GK49T9CJSAQSXCJ1FE24Q8MW sameAs LU-01GK49T9CJSAQSXCJ1FE24Q8MW.
- 01GK49T9CJSAQSXCJ1FE24Q8MW sourceOrganization urn:uuid:95063c2e-f3d1-49fa-bad1-6cc30511ec17.
- 01GK49T9CJSAQSXCJ1FE24Q8MW type V.