{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EHTASJRC25KUY455I3JAKHRTA5","short_pith_number":"pith:EHTASJRC","schema_version":"1.0","canonical_sha256":"21e6092622d7554c73bd46d2051e3307428e42e16fb0721ae22cc7e2c1bd1d91","source":{"kind":"arxiv","id":"2605.31014","version":1},"attestation_state":"computed","paper":{"title":"SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chen Zhao, Nan Mu, Xiaoyang Fan","submitted_at":"2026-05-29T08:47:51Z","abstract_excerpt":"Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine. However, acquiring complete multi-omics profiles is expensive and time-consuming, while most existing deep learning methods assume full modality availability during inference, resulting in substantial redundancy and limited practicality in clinical settings. To address this issue, we propose SDM-Q, a reinforcement learning framework for adaptive and cost-aware multi-omics classification. Specifically, multi-om"},"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":"2605.31014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T08:47:51Z","cross_cats_sorted":[],"title_canon_sha256":"c30202e80dc1af159c740d90cb29b0834ef73339a0c862549c3ee7a8d2cf33f2","abstract_canon_sha256":"e0fa5abfcfb69fec82058f22b1e42c6a61f5538ca59c0d71d9361839132140ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:30.778873Z","signature_b64":"+rntA9lpe5iD+A6BffXdtRjtFm1ycA1a+75wlxrMEla+y2FEDGvyARUnT2qQ9UA9WW3Lmga7QJ1zeLhxlHW4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21e6092622d7554c73bd46d2051e3307428e42e16fb0721ae22cc7e2c1bd1d91","last_reissued_at":"2026-06-01T01:03:30.777940Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:30.777940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chen Zhao, Nan Mu, Xiaoyang Fan","submitted_at":"2026-05-29T08:47:51Z","abstract_excerpt":"Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine. However, acquiring complete multi-omics profiles is expensive and time-consuming, while most existing deep learning methods assume full modality availability during inference, resulting in substantial redundancy and limited practicality in clinical settings. To address this issue, we propose SDM-Q, a reinforcement learning framework for adaptive and cost-aware multi-omics classification. Specifically, multi-om"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31014","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/2605.31014/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":"2605.31014","created_at":"2026-06-01T01:03:30.778110+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31014v1","created_at":"2026-06-01T01:03:30.778110+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31014","created_at":"2026-06-01T01:03:30.778110+00:00"},{"alias_kind":"pith_short_12","alias_value":"EHTASJRC25KU","created_at":"2026-06-01T01:03:30.778110+00:00"},{"alias_kind":"pith_short_16","alias_value":"EHTASJRC25KUY455","created_at":"2026-06-01T01:03:30.778110+00:00"},{"alias_kind":"pith_short_8","alias_value":"EHTASJRC","created_at":"2026-06-01T01:03:30.778110+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/EHTASJRC25KUY455I3JAKHRTA5","json":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5.json","graph_json":"https://pith.science/api/pith-number/EHTASJRC25KUY455I3JAKHRTA5/graph.json","events_json":"https://pith.science/api/pith-number/EHTASJRC25KUY455I3JAKHRTA5/events.json","paper":"https://pith.science/paper/EHTASJRC"},"agent_actions":{"view_html":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5","download_json":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5.json","view_paper":"https://pith.science/paper/EHTASJRC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31014&json=true","fetch_graph":"https://pith.science/api/pith-number/EHTASJRC25KUY455I3JAKHRTA5/graph.json","fetch_events":"https://pith.science/api/pith-number/EHTASJRC25KUY455I3JAKHRTA5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5/action/storage_attestation","attest_author":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5/action/author_attestation","sign_citation":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5/action/citation_signature","submit_replication":"https://pith.science/pith/EHTASJRC25KUY455I3JAKHRTA5/action/replication_record"}},"created_at":"2026-06-01T01:03:30.778110+00:00","updated_at":"2026-06-01T01:03:30.778110+00:00"}