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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |||
|---|---|---|---|---|
| Category | Genomics & Bioinformatics | Genomics & Bioinformatics | Genomics & Bioinformatics | — |
| Subcategory | Regulatory Genomics | Regulatory Genomics | Conventional or Statistical Baselines | — |
| Company | Kundaje laboratory | Google DeepMind | Research / method authors | — |
| Access mode | Public code / project terms | Research code or publication | Confirm distribution and exact release | — |
| Licence conditions | Review code, weights, data and service terms separately | Check code and model weights separately | NOT_INDEPENDENTLY_AUDITED | — |
| Compute requirements | See source requirements for selected configuration | Check selected checkpoint and runtime; no GPU catalog recommendation | Version and data dependent | — |
| Biological application | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Research workflow | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Evidence level | Project/provider documentation; no local benchmark | Peer-reviewed primary method | PEER_REVIEWED_METHOD_OR_CONFERENCE | — |
| Commercial eligibility | Not independently confirmed | Unknown, requires audit | NOT_INDEPENDENTLY_AUDITED | — |
| Price | See project terms; compute costs may apply | Check selected model licence | Verify access and licence | — |
| Availability ⓘ | See project release and maintenance status | Public research method/code | Method paper or official documentation | — |
| Features | Base-resolution neural modeling of functional genomics, Input: DNA sequence and functional genomics signal tracks, Output: Predicted signal profiles and model interpretations | 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 | Research software / framework | Research AI model / method | Classical/statistical scientific method | — |
| AI role | Scientific machine learning | Published biomedical AI method | NON_AI_STATISTICAL | — |
| Input data | DNA sequence and functional genomics signal tracks | DNA sequence interval; alleles and tissue/assay ontology | Reference genome with aligned short-read input and correct ploidy assumptions | — |
| Output data | Predicted signal profiles and model interpretations | Genomic regulatory track; sequence-variant contrast | VCF/BCF variant calls and variants QC operations | — |
| Validation | 未明确记录 | Requires independent dataset-specific evaluation | Published method scope; no independent BioAtlas execution or benchmark | — |
| Type | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Intended use | Biological and pharmaceutical research | 未明确记录 | 未明确记录 | — |
| Compatibility | 未明确记录 | 未明确记录 | 未明确记录 | — |
| Limitations | Training design and assay biases affect results; this is not a diagnostic variant assay. | 未明确记录 | 未明确记录 | — |