{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PNWE7DFFX5MRCJR4XEPWZFC2KY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4327b1f90b85e410c4a91373f30b38407da3774625f008cc847812e21bbe35f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-05-07T14:38:30Z","title_canon_sha256":"ddb2765f5c5655f3cd6b81dd1297e1d2628f2bf94946f5390efbae14d00811b8"},"schema_version":"1.0","source":{"id":"1805.02547","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.02547","created_at":"2026-05-18T00:16:39Z"},{"alias_kind":"arxiv_version","alias_value":"1805.02547v1","created_at":"2026-05-18T00:16:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.02547","created_at":"2026-05-18T00:16:39Z"},{"alias_kind":"pith_short_12","alias_value":"PNWE7DFFX5MR","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PNWE7DFFX5MRCJR4","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PNWE7DFF","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:9716254a65169436be9088aff57817c05a3d5a9962e34fb3f1431ef3b9c9aaab","target":"graph","created_at":"2026-05-18T00:16:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. Most existing methods for Gaussian graphical models assume that the data are homogeneous, i.e., all samples are drawn from a single Gaussian distribution. However, for many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This paper proposes to model the heterogeneous data using a mixture Gaussian graphical model, and apply the imputation-consistency algorithm, combining with the $\\psi$-learning algorit","authors_text":"Bochao Jia, Faming Liang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-05-07T14:38:30Z","title":"Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.02547","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:88c2321db761ada27e17da339464b495d8bb280f04405847074430de64cd6074","target":"record","created_at":"2026-05-18T00:16:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4327b1f90b85e410c4a91373f30b38407da3774625f008cc847812e21bbe35f2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-05-07T14:38:30Z","title_canon_sha256":"ddb2765f5c5655f3cd6b81dd1297e1d2628f2bf94946f5390efbae14d00811b8"},"schema_version":"1.0","source":{"id":"1805.02547","kind":"arxiv","version":1}},"canonical_sha256":"7b6c4f8ca5bf5911263cb91f6c945a562efaf71e650c8772c7c387587658c967","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7b6c4f8ca5bf5911263cb91f6c945a562efaf71e650c8772c7c387587658c967","first_computed_at":"2026-05-18T00:16:39.661443Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:39.661443Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jeK4KPi22WqNvrUPTdusMmnmPiNz/u/HoHCG5Ir3P0Cl6aDgxvvIvTF7lRNx0RB3cA5AJ+QD6QJe+M1h79CTAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:39.662036Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.02547","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88c2321db761ada27e17da339464b495d8bb280f04405847074430de64cd6074","sha256:9716254a65169436be9088aff57817c05a3d5a9962e34fb3f1431ef3b9c9aaab"],"state_sha256":"d08064b464210c426e73d1c8271f2d200321542714402d63f5c657977b70c4fb"}