{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:XSHIFIGTQFSYEL3JKGKUSLPA3B","short_pith_number":"pith:XSHIFIGT","canonical_record":{"source":{"id":"1602.07614","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-15T16:33:39Z","cross_cats_sorted":[],"title_canon_sha256":"7195aba62fb79e9b3b78a89966afeac8c204c21b5a34e8bec4ea6aff4ffba7c2","abstract_canon_sha256":"d97f9ef6a36607192df443a595adc4d46eb44879c73fccbb42f0c36bc1c5f8e9"},"schema_version":"1.0"},"canonical_sha256":"bc8e82a0d38165822f695195492de0d876d72aa40ac88d96f8cebaa2dcb98055","source":{"kind":"arxiv","id":"1602.07614","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.07614","created_at":"2026-05-18T01:20:02Z"},{"alias_kind":"arxiv_version","alias_value":"1602.07614v1","created_at":"2026-05-18T01:20:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07614","created_at":"2026-05-18T01:20:02Z"},{"alias_kind":"pith_short_12","alias_value":"XSHIFIGTQFSY","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"XSHIFIGTQFSYEL3J","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"XSHIFIGT","created_at":"2026-05-18T12:30:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:XSHIFIGTQFSYEL3JKGKUSLPA3B","target":"record","payload":{"canonical_record":{"source":{"id":"1602.07614","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-15T16:33:39Z","cross_cats_sorted":[],"title_canon_sha256":"7195aba62fb79e9b3b78a89966afeac8c204c21b5a34e8bec4ea6aff4ffba7c2","abstract_canon_sha256":"d97f9ef6a36607192df443a595adc4d46eb44879c73fccbb42f0c36bc1c5f8e9"},"schema_version":"1.0"},"canonical_sha256":"bc8e82a0d38165822f695195492de0d876d72aa40ac88d96f8cebaa2dcb98055","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:02.505345Z","signature_b64":"vMoaHdIUcyFrDMvidiMwGhNnDICENCWKvnc2Rtnd0DRhYp/iPMTGC+7USMT0OIxprJDW4sDSf9/mRbMCE3ywDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc8e82a0d38165822f695195492de0d876d72aa40ac88d96f8cebaa2dcb98055","last_reissued_at":"2026-05-18T01:20:02.504764Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:02.504764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.07614","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":"2026-05-18T01:20:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g1O6uc0bsdGyHxa4OcJjNATr5VzK7NpoS1btjTgDO2/5yPEiBxecI0tq2GBesQC686Ce7e2JJH8JN9vw+AT6DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T13:11:52.973639Z"},"content_sha256":"47cefa208a0a17f7944e1c9138ef5c29fec7af803abcaffa4aff7407a2cb2899","schema_version":"1.0","event_id":"sha256:47cefa208a0a17f7944e1c9138ef5c29fec7af803abcaffa4aff7407a2cb2899"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:XSHIFIGTQFSYEL3JKGKUSLPA3B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniele Ramazzotti","submitted_at":"2016-02-15T16:33:39Z","abstract_excerpt":"Recently, there has been a resurgence of interest in rigorous algorithms for the inference of cancer progression from genomic data. The motivations are manifold: (i) growing NGS and single cell data from cancer patients, (ii) need for novel Data Science and Machine Learning algorithms to infer models of cancer progression, and (iii) a desire to understand the temporal and heterogeneous structure of tumor to tame its progression by efficacious therapeutic intervention. This thesis presents a multi-disciplinary effort to model tumor progression involving successive accumulation of genetic altera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07614","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":""},"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":"2026-05-18T01:20:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ruBbbkX5CtvcWRsu/WdydHIPsy3bx9IUdb2ydbnTrmMxmeV9hbLvPhowxM4PNj27lIhBVyRb9dmQDjdDicd+Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T13:11:52.973988Z"},"content_sha256":"7a57df9f283a522573566c8178a0fd8e29c1250b3e24c8a49f4f1cf44a106f4d","schema_version":"1.0","event_id":"sha256:7a57df9f283a522573566c8178a0fd8e29c1250b3e24c8a49f4f1cf44a106f4d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B/bundle.json","state_url":"https://pith.science/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B/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":"2026-05-28T13:11:52Z","links":{"resolver":"https://pith.science/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B","bundle":"https://pith.science/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B/bundle.json","state":"https://pith.science/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XSHIFIGTQFSYEL3JKGKUSLPA3B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:XSHIFIGTQFSYEL3JKGKUSLPA3B","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":"d97f9ef6a36607192df443a595adc4d46eb44879c73fccbb42f0c36bc1c5f8e9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-15T16:33:39Z","title_canon_sha256":"7195aba62fb79e9b3b78a89966afeac8c204c21b5a34e8bec4ea6aff4ffba7c2"},"schema_version":"1.0","source":{"id":"1602.07614","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.07614","created_at":"2026-05-18T01:20:02Z"},{"alias_kind":"arxiv_version","alias_value":"1602.07614v1","created_at":"2026-05-18T01:20:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.07614","created_at":"2026-05-18T01:20:02Z"},{"alias_kind":"pith_short_12","alias_value":"XSHIFIGTQFSY","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_16","alias_value":"XSHIFIGTQFSYEL3J","created_at":"2026-05-18T12:30:51Z"},{"alias_kind":"pith_short_8","alias_value":"XSHIFIGT","created_at":"2026-05-18T12:30:51Z"}],"graph_snapshots":[{"event_id":"sha256:7a57df9f283a522573566c8178a0fd8e29c1250b3e24c8a49f4f1cf44a106f4d","target":"graph","created_at":"2026-05-18T01:20:02Z","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":"Recently, there has been a resurgence of interest in rigorous algorithms for the inference of cancer progression from genomic data. The motivations are manifold: (i) growing NGS and single cell data from cancer patients, (ii) need for novel Data Science and Machine Learning algorithms to infer models of cancer progression, and (iii) a desire to understand the temporal and heterogeneous structure of tumor to tame its progression by efficacious therapeutic intervention. This thesis presents a multi-disciplinary effort to model tumor progression involving successive accumulation of genetic altera","authors_text":"Daniele Ramazzotti","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-15T16:33:39Z","title":"A Model of Selective Advantage for the Efficient Inference of Cancer Clonal Evolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07614","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":"sha256:47cefa208a0a17f7944e1c9138ef5c29fec7af803abcaffa4aff7407a2cb2899","target":"record","created_at":"2026-05-18T01:20:02Z","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":"d97f9ef6a36607192df443a595adc4d46eb44879c73fccbb42f0c36bc1c5f8e9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-15T16:33:39Z","title_canon_sha256":"7195aba62fb79e9b3b78a89966afeac8c204c21b5a34e8bec4ea6aff4ffba7c2"},"schema_version":"1.0","source":{"id":"1602.07614","kind":"arxiv","version":1}},"canonical_sha256":"bc8e82a0d38165822f695195492de0d876d72aa40ac88d96f8cebaa2dcb98055","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc8e82a0d38165822f695195492de0d876d72aa40ac88d96f8cebaa2dcb98055","first_computed_at":"2026-05-18T01:20:02.504764Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:02.504764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vMoaHdIUcyFrDMvidiMwGhNnDICENCWKvnc2Rtnd0DRhYp/iPMTGC+7USMT0OIxprJDW4sDSf9/mRbMCE3ywDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:02.505345Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.07614","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47cefa208a0a17f7944e1c9138ef5c29fec7af803abcaffa4aff7407a2cb2899","sha256:7a57df9f283a522573566c8178a0fd8e29c1250b3e24c8a49f4f1cf44a106f4d"],"state_sha256":"a4094d9a4c499843f9f0296fddd6b328e91c098dedfdf3367a6c93f4257ea089"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LXFaGvNABYlcEmqwoC35H33+JPakyxSF7QRDSMly7L8z+9qrGdf/Yr/j/3cLGrpN13OmekQtjlvV8W6/qpsFCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T13:11:52.976015Z","bundle_sha256":"28af95cb54b6b39b535da67b2de70564d5d18f36b611c425537264c389df2377"}}