{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VGDZOVG57DVWW2KFQH5GUU7NFF","short_pith_number":"pith:VGDZOVG5","schema_version":"1.0","canonical_sha256":"a9879754ddf8eb6b694581fa6a53ed296e738cf81102154e88b64d61086c6467","source":{"kind":"arxiv","id":"1904.01205","version":3},"attestation_state":"computed","paper":{"title":"Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mike Li, X. Rosalind Wang","submitted_at":"2019-04-02T04:16:45Z","abstract_excerpt":"We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of vario"},"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":"1904.01205","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-02T04:16:45Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"05a5c9aad6828a82cedb8645d1ecab2eb887ad0c1185c4633379485f0d2556e5","abstract_canon_sha256":"b0f314f107231b44514d75adc8a39ff80b151fd6e35128e4d769abbfad0563d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:25.913210Z","signature_b64":"4eoLoyMEP3Vvc7NcwkWnCeObNmS9Qm1P0SAe61bcZkGAaOwF+eOH3ALJcgXyuEF4ViJTcm9/WcAqeVK+KIG8Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9879754ddf8eb6b694581fa6a53ed296e738cf81102154e88b64d61086c6467","last_reissued_at":"2026-07-04T23:58:25.912773Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:25.912773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mike Li, X. Rosalind Wang","submitted_at":"2019-04-02T04:16:45Z","abstract_excerpt":"We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of vario"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01205","kind":"arxiv","version":3},"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/1904.01205/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":"1904.01205","created_at":"2026-07-04T23:58:25.912832+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.01205v3","created_at":"2026-07-04T23:58:25.912832+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01205","created_at":"2026-07-04T23:58:25.912832+00:00"},{"alias_kind":"pith_short_12","alias_value":"VGDZOVG57DVW","created_at":"2026-07-04T23:58:25.912832+00:00"},{"alias_kind":"pith_short_16","alias_value":"VGDZOVG57DVWW2KF","created_at":"2026-07-04T23:58:25.912832+00:00"},{"alias_kind":"pith_short_8","alias_value":"VGDZOVG5","created_at":"2026-07-04T23:58:25.912832+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/VGDZOVG57DVWW2KFQH5GUU7NFF","json":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF.json","graph_json":"https://pith.science/api/pith-number/VGDZOVG57DVWW2KFQH5GUU7NFF/graph.json","events_json":"https://pith.science/api/pith-number/VGDZOVG57DVWW2KFQH5GUU7NFF/events.json","paper":"https://pith.science/paper/VGDZOVG5"},"agent_actions":{"view_html":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF","download_json":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF.json","view_paper":"https://pith.science/paper/VGDZOVG5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.01205&json=true","fetch_graph":"https://pith.science/api/pith-number/VGDZOVG57DVWW2KFQH5GUU7NFF/graph.json","fetch_events":"https://pith.science/api/pith-number/VGDZOVG57DVWW2KFQH5GUU7NFF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF/action/storage_attestation","attest_author":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF/action/author_attestation","sign_citation":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF/action/citation_signature","submit_replication":"https://pith.science/pith/VGDZOVG57DVWW2KFQH5GUU7NFF/action/replication_record"}},"created_at":"2026-07-04T23:58:25.912832+00:00","updated_at":"2026-07-04T23:58:25.912832+00:00"}