{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NVQAAJGWOLMXPJP2XVCCEI2MIT","short_pith_number":"pith:NVQAAJGW","schema_version":"1.0","canonical_sha256":"6d600024d672d977a5fabd4422234c44eadc341b638e24892c5e658b1e7eb6e6","source":{"kind":"arxiv","id":"2208.09934","version":2},"attestation_state":"computed","paper":{"title":"A Graphical Model for Fusing Diverse Microbiome Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Alfred Hero, Haonan Zhu, Jo Handelsman, Julia Nepper, Marc G. Chevrette, Mehmet Aktukmak, Shruthi Magesh","submitted_at":"2022-08-21T17:54:39Z","abstract_excerpt":"This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multi"},"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":"2208.09934","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-08-21T17:54:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"da4fbe0068cb1c4555bfb0393d1b653c8e804f3685170c9322811c7faa316b50","abstract_canon_sha256":"ddb074dcac6c77315a4b172cd89bc6a092b0308ca3472aaaabfce715a870f193"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:28:46.925959Z","signature_b64":"WE6DQAE1fCw4e33bHm+NWpBBtpRcY3LsRb2C9yLGLGQW6A2yZn6ebnx/y1t168MNygK+INaiCuQAJcogLatMCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d600024d672d977a5fabd4422234c44eadc341b638e24892c5e658b1e7eb6e6","last_reissued_at":"2026-07-05T05:28:46.925604Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:28:46.925604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Graphical Model for Fusing Diverse Microbiome Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Alfred Hero, Haonan Zhu, Jo Handelsman, Julia Nepper, Marc G. Chevrette, Mehmet Aktukmak, Shruthi Magesh","submitted_at":"2022-08-21T17:54:39Z","abstract_excerpt":"This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09934","kind":"arxiv","version":2},"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/2208.09934/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":"2208.09934","created_at":"2026-07-05T05:28:46.925664+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09934v2","created_at":"2026-07-05T05:28:46.925664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09934","created_at":"2026-07-05T05:28:46.925664+00:00"},{"alias_kind":"pith_short_12","alias_value":"NVQAAJGWOLMX","created_at":"2026-07-05T05:28:46.925664+00:00"},{"alias_kind":"pith_short_16","alias_value":"NVQAAJGWOLMXPJP2","created_at":"2026-07-05T05:28:46.925664+00:00"},{"alias_kind":"pith_short_8","alias_value":"NVQAAJGW","created_at":"2026-07-05T05:28:46.925664+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/NVQAAJGWOLMXPJP2XVCCEI2MIT","json":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT.json","graph_json":"https://pith.science/api/pith-number/NVQAAJGWOLMXPJP2XVCCEI2MIT/graph.json","events_json":"https://pith.science/api/pith-number/NVQAAJGWOLMXPJP2XVCCEI2MIT/events.json","paper":"https://pith.science/paper/NVQAAJGW"},"agent_actions":{"view_html":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT","download_json":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT.json","view_paper":"https://pith.science/paper/NVQAAJGW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09934&json=true","fetch_graph":"https://pith.science/api/pith-number/NVQAAJGWOLMXPJP2XVCCEI2MIT/graph.json","fetch_events":"https://pith.science/api/pith-number/NVQAAJGWOLMXPJP2XVCCEI2MIT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT/action/storage_attestation","attest_author":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT/action/author_attestation","sign_citation":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT/action/citation_signature","submit_replication":"https://pith.science/pith/NVQAAJGWOLMXPJP2XVCCEI2MIT/action/replication_record"}},"created_at":"2026-07-05T05:28:46.925664+00:00","updated_at":"2026-07-05T05:28:46.925664+00:00"}