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Confronta modelli biologici, motori di docking e flussi di calcolo affiancati. Filtra per modello di accesso, impronta di calcolo e categoria per ottenere una tabella decisionale attuabile in meno di un minuto.
| Attributo | ＋ Aggiungi strumento | |||
|---|---|---|---|---|
| Category | Genomics & Sequence Modeling | Genomics & Bioinformatics | Genomics & Bioinformatics | — |
| Subcategory | Clinical Variant Effect Prediction | Regulatory Genomics | Conventional or Statistical Baselines | — |
| Company | Google DeepMind | Google DeepMind | Research / method authors | — |
| Access mode | Open Access (Research) | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | Code: Apache 2.0; Predictions/Data: CC BY-NC 4.0 | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | Pre-computed database lookup (trained weights for arbitrary novel sequences not published) | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | — |
| Biological application | Rare disease diagnosis, oncogenic driver mutation identification, and human genetics | Non registrato | Non registrato | — |
| Research workflow | Query missense variant (e.g. BRCA1 p.Cys61Gly) -> obtain pathogenicity score (0 to 1) | Non registrato | Non registrato | — |
| Evidence level | Peer-reviewed research (Science 2023) | Peer-reviewed primary method | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Research use only (non-commercial clinical evaluation) | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | Free Open Access | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | Available on GitHub and Ensembl / ClinVar cross-references | Public research method/code | Method paper or official documentation | — |
| Features | Comprehensive predictions for all 71 million possible human missense variants, Trained on population frequency data and structural context without clinical label leakage, Classified 89% of missense variants as either likely pathogenic (32%) or likely benign (57%), Pre-computed database and downloadable BigWig/VCF files for integration into clinical pipelines | Predicts genomic regulatory tracks and predicted functional effects of noncoding DNA variants., Genomic regulatory track, sequence-variant contrast | bcftools scientific method, Reference genome with aligned short-read input and correct ploidy assumptions, VCF/BCF variant calls and variants QC operations | — |
| Entry type | AI Model & Database | Research AI model / method | Classical/statistical scientific method | — |
| AI role | Clinical Pathogenicity Classification | Published biomedical AI method | NON_AI_STATISTICAL | — |
| Input data | Non registrato | DNA sequence interval; alleles and tissue/assay ontology | Reference genome with aligned short-read input and correct ploidy assumptions | — |
| Output data | Non registrato | Genomic regulatory track; sequence-variant contrast | VCF/BCF variant calls and variants QC operations | — |
| Validation | Non registrato | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | Structural variant effect prediction model & database | Non registrato | Non registrato | — |
| Intended use | Non registrato | Non registrato | Non registrato | — |
| Compatibility | Non registrato | Non registrato | Non registrato | — |
| Manufacturer | Google DeepMind | Non registrato | Non registrato | — |
| Integration evidence | https://github.com/google-deepmind/alphamissense | Non registrato | Non registrato | — |
| Laboratory handoff | Informs targeted functional genomics assays (Deep Mutational Scanning) | Non registrato | Non registrato | — |