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Here is a list of interesting projects that would be nice to have identified because they have been previously used in deep-learning training algorithms:
DDA datasets:
PXD008034: 12
PXD000269: 79
PXD024364: Global detection of human variants and isoforms by deep proteome sequencing
PXD004977: 42
PXD001608: 60
PXD002549: 89
PXD002452: 179
PXD001636: 60
PXD004087: 72
PXD001695: 234
PXD002785: 22
PXD000955: 196
PXD003472: 36
PXD001865: 144
PXD002607: 36 (De novo PTM Peaks)
PXD002908: 40
MSV000085230
PXD031812: 32
PXD003002: 24
PXD004276: 136
PXD003189: 64
PXD003583: 30
PXD004321: 42
PXD005126: 45
PXD002912: 90
DeepMass Manuscript:
PXD002908: 40
PXD002912: 90
PXD003668: 101
PXD001608: 60
PXD004977: 42
PXD002549: 89
PXD002452: 179
PXD000955: 196
PXD001865: 144
PXD001695: 234
PXD026436: 3
PXD039809: 36
PXD019483: 128
Prosit:
PXD010871: 8 (This dataset do not contains spectra DDA)
PXD034056: 6 (This dataset do not contains spectra)
Here is a list of interesting projects that would be nice to have identified because they have been previously used in deep-learning training algorithms:
DDA datasets:
DeepMass Manuscript:
Prosit:
ProteomeTools:
Ion mobility:
PTMs - ubiquitinome:
### PTMs - phospho:
DIA- Pposphoproteomics:
DIA:
### Single cell dataset:
HLA Datasets:
Tumor, Cell line dataset:
### Big datasets, tissue, species proteome:
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