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مقایسه شاخبهشاخ مدلهای زیستی، موتورهای داکینگ و پایپلاینهای محاسباتی. فیلتر بر اساس مدل دسترسی، مصرف پردازشی و دستهبندی جهت اخذ تصمیم دقیق در کمتر از یک دقیقه.
| مشخصه | + افزودن ابزار | |||
|---|---|---|---|---|
| Category | Proteomics & Immunoinformatics | Proteomics & Immunoinformatics | Proteomics & Immunoinformatics | — |
| Subcategory | Research AI Methods | Research AI Methods | Research AI Methods | — |
| Company | Research / method authors | Research / method authors | Research / method authors | — |
| Access mode | Confirm distribution and exact release | Confirm distribution and exact release | Confirm distribution and exact release | — |
| Licence conditions | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Version and data dependent | Version and data dependent | Version and data dependent | — |
| Biological application | ثبتنشده | ثبتنشده | ثبتنشده | — |
| Research workflow | ثبتنشده | ثبتنشده | ثبتنشده | — |
| Evidence level | PEER_REVIEWED_METHOD_OR_CONFERENCE | PEER_REVIEWED_METHOD_OR_CONFERENCE | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Verify access and licence | Verify access and licence | Verify access and licence | — |
| Availability ⓘ | Method paper or official documentation | Method paper or official documentation | Method paper or official documentation | — |
| Features | Prosit scientific method, Peptide sequence, collision energy and mass spectrometer setting, Predicted MS/MS fragment ion intensities and retention time | AlphaPeptDeep scientific method, Peptide sequences, fragmentation conditions and mass spectrometry context, Predicted fragment intensities and retention times | Casanovo scientific method, DDA-like tandem mass spectra in compatible peak-list representation, Peptide sequence predictions from spectra | — |
| Entry type | Source-scoped research method | Source-scoped research method | Source-scoped research method | — |
| AI role | DEEP_LEARNING | DEEP_LEARNING | DEEP_LEARNING | — |
| Input data | Peptide sequence, collision energy and mass spectrometer setting | Peptide sequences, fragmentation conditions and mass spectrometry context | DDA-like tandem mass spectra in compatible peak-list representation | — |
| Output data | Predicted MS/MS fragment ion intensities and retention time | Predicted fragment intensities and retention times | Peptide sequence predictions from spectra | — |
| Validation | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | Published method scope; no independent BioAtlas execution or benchmark | — |