{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:J7CW2XE5KNODWSAF2GYGTTLI5N","short_pith_number":"pith:J7CW2XE5","canonical_record":{"source":{"id":"2504.06306","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-04-07T20:48:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1d25876af6d89d37fad4c3530072667508b08a4d2f956095642e378f521b6444","abstract_canon_sha256":"6cf45a02d151eb930294b244387c15032594a081f975055d22415ef35e81238f"},"schema_version":"1.0"},"canonical_sha256":"4fc56d5c9d535c3b4805d1b069cd68eb7082e4986b94a7cb18242c1bf1090608","source":{"kind":"arxiv","id":"2504.06306","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.06306","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"arxiv_version","alias_value":"2504.06306v1","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06306","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"pith_short_12","alias_value":"J7CW2XE5KNOD","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"pith_short_16","alias_value":"J7CW2XE5KNODWSAF","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"pith_short_8","alias_value":"J7CW2XE5","created_at":"2026-07-05T10:46:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:J7CW2XE5KNODWSAF2GYGTTLI5N","target":"record","payload":{"canonical_record":{"source":{"id":"2504.06306","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-04-07T20:48:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1d25876af6d89d37fad4c3530072667508b08a4d2f956095642e378f521b6444","abstract_canon_sha256":"6cf45a02d151eb930294b244387c15032594a081f975055d22415ef35e81238f"},"schema_version":"1.0"},"canonical_sha256":"4fc56d5c9d535c3b4805d1b069cd68eb7082e4986b94a7cb18242c1bf1090608","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:24.913712Z","signature_b64":"Adb/1vBWSUuUsKCW7V3tDUoX1Lp0CFGjXHZE1ySg6+y6M3aJhiKX0j6rpc0r8wtMziwmPmqzniGf7d4lARBNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fc56d5c9d535c3b4805d1b069cd68eb7082e4986b94a7cb18242c1bf1090608","last_reissued_at":"2026-07-05T10:46:24.913223Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:24.913223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.06306","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:46:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tkJu0SR3UbMAT+yqvx3xWm02O0or6XSgnsQO8wn4JSsKgxDuH0fZe6i5hy3NrxgwT7T6MzmjG1n0ileRmFuaBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T00:11:34.899584Z"},"content_sha256":"fdb481baf8bcf346637fb847cbc908d7272f833341dcc9423a5f49accf8c87f7","schema_version":"1.0","event_id":"sha256:fdb481baf8bcf346637fb847cbc908d7272f833341dcc9423a5f49accf8c87f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:J7CW2XE5KNODWSAF2GYGTTLI5N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.QM","authors_text":"Deepthi Rao, Polycarp Nalela, Praveen Rao","submitted_at":"2025-04-07T20:48:15Z","abstract_excerpt":"Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivability of cancer patients with metastatic patterns using the comprehensive MSK-MET dataset, which includes genomic and clinical data from 25,775 patients across 27 cancer types. We evaluated five ML models-XGBoost, Na\\\"ive Bayes, Decision Tree, Logistic Regression, and Random Fores using hyperparameter tuning and grid search. XGBoost emerged as the best performer with an area under the curve (AUC) of 0.82. To enhance model interpretability, SHapley A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06306","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/2504.06306/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:46:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RSzKVRavtBkomrGRxuVaXYhv2T3n1+y66HLCl0zccyT/AHxEvMNKrb72zWHm7md3htPmXluEzIYZfLkyHoztAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T00:11:34.899956Z"},"content_sha256":"5c207a179766e1d1b509602bff197553a20a29c07a9b8b8af7e38bd3b7fa83d9","schema_version":"1.0","event_id":"sha256:5c207a179766e1d1b509602bff197553a20a29c07a9b8b8af7e38bd3b7fa83d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J7CW2XE5KNODWSAF2GYGTTLI5N/bundle.json","state_url":"https://pith.science/pith/J7CW2XE5KNODWSAF2GYGTTLI5N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J7CW2XE5KNODWSAF2GYGTTLI5N/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T00:11:34Z","links":{"resolver":"https://pith.science/pith/J7CW2XE5KNODWSAF2GYGTTLI5N","bundle":"https://pith.science/pith/J7CW2XE5KNODWSAF2GYGTTLI5N/bundle.json","state":"https://pith.science/pith/J7CW2XE5KNODWSAF2GYGTTLI5N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J7CW2XE5KNODWSAF2GYGTTLI5N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:J7CW2XE5KNODWSAF2GYGTTLI5N","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":"6cf45a02d151eb930294b244387c15032594a081f975055d22415ef35e81238f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-04-07T20:48:15Z","title_canon_sha256":"1d25876af6d89d37fad4c3530072667508b08a4d2f956095642e378f521b6444"},"schema_version":"1.0","source":{"id":"2504.06306","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.06306","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"arxiv_version","alias_value":"2504.06306v1","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06306","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"pith_short_12","alias_value":"J7CW2XE5KNOD","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"pith_short_16","alias_value":"J7CW2XE5KNODWSAF","created_at":"2026-07-05T10:46:24Z"},{"alias_kind":"pith_short_8","alias_value":"J7CW2XE5","created_at":"2026-07-05T10:46:24Z"}],"graph_snapshots":[{"event_id":"sha256:5c207a179766e1d1b509602bff197553a20a29c07a9b8b8af7e38bd3b7fa83d9","target":"graph","created_at":"2026-07-05T10:46:24Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.06306/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivability of cancer patients with metastatic patterns using the comprehensive MSK-MET dataset, which includes genomic and clinical data from 25,775 patients across 27 cancer types. We evaluated five ML models-XGBoost, Na\\\"ive Bayes, Decision Tree, Logistic Regression, and Random Fores using hyperparameter tuning and grid search. XGBoost emerged as the best performer with an area under the curve (AUC) of 0.82. To enhance model interpretability, SHapley A","authors_text":"Deepthi Rao, Polycarp Nalela, Praveen Rao","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-04-07T20:48:15Z","title":"Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06306","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:fdb481baf8bcf346637fb847cbc908d7272f833341dcc9423a5f49accf8c87f7","target":"record","created_at":"2026-07-05T10:46:24Z","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":"6cf45a02d151eb930294b244387c15032594a081f975055d22415ef35e81238f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2025-04-07T20:48:15Z","title_canon_sha256":"1d25876af6d89d37fad4c3530072667508b08a4d2f956095642e378f521b6444"},"schema_version":"1.0","source":{"id":"2504.06306","kind":"arxiv","version":1}},"canonical_sha256":"4fc56d5c9d535c3b4805d1b069cd68eb7082e4986b94a7cb18242c1bf1090608","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4fc56d5c9d535c3b4805d1b069cd68eb7082e4986b94a7cb18242c1bf1090608","first_computed_at":"2026-07-05T10:46:24.913223Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:46:24.913223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Adb/1vBWSUuUsKCW7V3tDUoX1Lp0CFGjXHZE1ySg6+y6M3aJhiKX0j6rpc0r8wtMziwmPmqzniGf7d4lARBNAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:46:24.913712Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.06306","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fdb481baf8bcf346637fb847cbc908d7272f833341dcc9423a5f49accf8c87f7","sha256:5c207a179766e1d1b509602bff197553a20a29c07a9b8b8af7e38bd3b7fa83d9"],"state_sha256":"209b4f532d5a1b92f1887a538f2e37185f41d28003e64defa0829ca789c604f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ufCuJQJtC7z4OFR0IgCSMKzox+sFqOZ+oZUwmFfipWnmh/CURVgb7WM+e5MthvwryNYcHqmpHyVznEhVm5dACQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T00:11:34.902008Z","bundle_sha256":"b934d7e69e8be66ae12a4600cf1d5c46ebc1dd390701d0c857a928e17f4326d4"}}