{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6NJK57DOEW3IC4I3HDBJC3TFON","short_pith_number":"pith:6NJK57DO","schema_version":"1.0","canonical_sha256":"f352aefc6e25b681711b38c2916e65734f1d2204df2280e241b0d5c87e082b76","source":{"kind":"arxiv","id":"2501.14948","version":1},"attestation_state":"computed","paper":{"title":"HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.CE","authors_text":"Bo Li, Guangyu Wang, Jing Su, Qianqian Song, Qing Wang, Wen-Jie Chen","submitted_at":"2025-01-24T22:18:37Z","abstract_excerpt":"Histopathology, particularly hematoxylin and eosin (H\\&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H\\&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcriptomics to map gene expression with spatial resolution. To address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive Learning for Imputation of Profiles), an innovative deep learning framework that bridges the gap between histological imaging and molecular prof"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.14948","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CE","submitted_at":"2025-01-24T22:18:37Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"220081e17e18df83c15e7b3dce9fd8dd166bd332b81a2eebfddc8bbea01c5e69","abstract_canon_sha256":"436498c341ccdadb1b35b5f007678a910a09066fc8d91868bfa2d2fdc2c566f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:21.324143Z","signature_b64":"Olmvq4eYil2KAxcj4beBIVRX0m7VfyVKYJ/jqj1eyu923+2HV7aXvK55vuGyYSp0Zss5hLrZb/GROPsfGY+ADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f352aefc6e25b681711b38c2916e65734f1d2204df2280e241b0d5c87e082b76","last_reissued_at":"2026-07-05T10:05:21.323581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:21.323581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.CE","authors_text":"Bo Li, Guangyu Wang, Jing Su, Qianqian Song, Qing Wang, Wen-Jie Chen","submitted_at":"2025-01-24T22:18:37Z","abstract_excerpt":"Histopathology, particularly hematoxylin and eosin (H\\&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H\\&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcriptomics to map gene expression with spatial resolution. To address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive Learning for Imputation of Profiles), an innovative deep learning framework that bridges the gap between histological imaging and molecular prof"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14948","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.14948/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.14948","created_at":"2026-07-05T10:05:21.323643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14948v1","created_at":"2026-07-05T10:05:21.323643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14948","created_at":"2026-07-05T10:05:21.323643+00:00"},{"alias_kind":"pith_short_12","alias_value":"6NJK57DOEW3I","created_at":"2026-07-05T10:05:21.323643+00:00"},{"alias_kind":"pith_short_16","alias_value":"6NJK57DOEW3IC4I3","created_at":"2026-07-05T10:05:21.323643+00:00"},{"alias_kind":"pith_short_8","alias_value":"6NJK57DO","created_at":"2026-07-05T10:05:21.323643+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON","json":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON.json","graph_json":"https://pith.science/api/pith-number/6NJK57DOEW3IC4I3HDBJC3TFON/graph.json","events_json":"https://pith.science/api/pith-number/6NJK57DOEW3IC4I3HDBJC3TFON/events.json","paper":"https://pith.science/paper/6NJK57DO"},"agent_actions":{"view_html":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON","download_json":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON.json","view_paper":"https://pith.science/paper/6NJK57DO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14948&json=true","fetch_graph":"https://pith.science/api/pith-number/6NJK57DOEW3IC4I3HDBJC3TFON/graph.json","fetch_events":"https://pith.science/api/pith-number/6NJK57DOEW3IC4I3HDBJC3TFON/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON/action/storage_attestation","attest_author":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON/action/author_attestation","sign_citation":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON/action/citation_signature","submit_replication":"https://pith.science/pith/6NJK57DOEW3IC4I3HDBJC3TFON/action/replication_record"}},"created_at":"2026-07-05T10:05:21.323643+00:00","updated_at":"2026-07-05T10:05:21.323643+00:00"}