{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:G53P5B4D4OCNK225N4SQGHRWIK","short_pith_number":"pith:G53P5B4D","canonical_record":{"source":{"id":"2503.04483","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-06T14:32:00Z","cross_cats_sorted":["cs.LG","q-bio.QM"],"title_canon_sha256":"71016de3463902f4c3e2bfae7ee42341e0add2dcecb2926d33cb06d017f85a88","abstract_canon_sha256":"f2306884bc889e93d4ec31b19e4fdf61b5e9a245d7875e33760109551d5d607d"},"schema_version":"1.0"},"canonical_sha256":"3776fe8783e384d56b5d6f25031e3642a0b85a48ee91599bf367fd39457fdbea","source":{"kind":"arxiv","id":"2503.04483","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04483","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04483v2","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04483","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"pith_short_12","alias_value":"G53P5B4D4OCN","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"pith_short_16","alias_value":"G53P5B4D4OCNK225","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"pith_short_8","alias_value":"G53P5B4D","created_at":"2026-07-05T11:17:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:G53P5B4D4OCNK225N4SQGHRWIK","target":"record","payload":{"canonical_record":{"source":{"id":"2503.04483","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-06T14:32:00Z","cross_cats_sorted":["cs.LG","q-bio.QM"],"title_canon_sha256":"71016de3463902f4c3e2bfae7ee42341e0add2dcecb2926d33cb06d017f85a88","abstract_canon_sha256":"f2306884bc889e93d4ec31b19e4fdf61b5e9a245d7875e33760109551d5d607d"},"schema_version":"1.0"},"canonical_sha256":"3776fe8783e384d56b5d6f25031e3642a0b85a48ee91599bf367fd39457fdbea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:42.810649Z","signature_b64":"qFJT7532MXmXNOnbeoRLH1n0uAXp0dDBuVkt619EWRCMvoY4kU9Dlx9pXvsRnB6PFk1uHJQh1DnaI7Z7zw3kAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3776fe8783e384d56b5d6f25031e3642a0b85a48ee91599bf367fd39457fdbea","last_reissued_at":"2026-07-05T11:17:42.810100Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:42.810100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.04483","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wQOwI4bpXsVc6ry+owQj/xqoA1ieeV6bJDFIoulSESOE6OrQZBQUvCbQvCaHs/WRD+Il6josFr40lE9CfS7EAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T23:19:05.088224Z"},"content_sha256":"9db458106b64d5bb4fc7d29075a9dd66d222f21720af29943c109bf53ac245c8","schema_version":"1.0","event_id":"sha256:9db458106b64d5bb4fc7d29075a9dd66d222f21720af29943c109bf53ac245c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:G53P5B4D4OCNK225N4SQGHRWIK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"stat.ML","authors_text":"Artem Moskalev, Mangal Prakash, Rui Liao, Shuwei Li, Song-Jun Xu, Tianyu Cui, Tommaso Mansi","submitted_at":"2025-03-06T14:32:00Z","abstract_excerpt":"Inferring Gene Regulatory Networks (GRNs) from gene expression data is crucial for understanding biological processes. While supervised models are reported to achieve high performance for this task, they rely on costly ground truth (GT) labels and risk learning gene-specific biases, such as class imbalances of GT interactions, rather than true regulatory mechanisms. To address these issues, we introduce InfoSEM, an unsupervised generative model that leverages textual gene embeddings as informative priors, improving GRN inference without GT labels. InfoSEM can also integrate GT labels as an add"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04483","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/2503.04483/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":"2026-07-05T11:17:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zJvBhhxk3/HX7BQPnt+p8aGsvl+fjJ8+R//V2HJihgGXcHjk9M34WkKyWI4CePX5AcwRusAVRDm/rusvnQf9CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T23:19:05.089143Z"},"content_sha256":"c055c9838a8519f4c10d4b3a0217d87d16b321d9a9897848bfeb73f09d7eb614","schema_version":"1.0","event_id":"sha256:c055c9838a8519f4c10d4b3a0217d87d16b321d9a9897848bfeb73f09d7eb614"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G53P5B4D4OCNK225N4SQGHRWIK/bundle.json","state_url":"https://pith.science/pith/G53P5B4D4OCNK225N4SQGHRWIK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G53P5B4D4OCNK225N4SQGHRWIK/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":"2026-08-15T23:19:05Z","links":{"resolver":"https://pith.science/pith/G53P5B4D4OCNK225N4SQGHRWIK","bundle":"https://pith.science/pith/G53P5B4D4OCNK225N4SQGHRWIK/bundle.json","state":"https://pith.science/pith/G53P5B4D4OCNK225N4SQGHRWIK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G53P5B4D4OCNK225N4SQGHRWIK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:G53P5B4D4OCNK225N4SQGHRWIK","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":"f2306884bc889e93d4ec31b19e4fdf61b5e9a245d7875e33760109551d5d607d","cross_cats_sorted":["cs.LG","q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-06T14:32:00Z","title_canon_sha256":"71016de3463902f4c3e2bfae7ee42341e0add2dcecb2926d33cb06d017f85a88"},"schema_version":"1.0","source":{"id":"2503.04483","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04483","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04483v2","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04483","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"pith_short_12","alias_value":"G53P5B4D4OCN","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"pith_short_16","alias_value":"G53P5B4D4OCNK225","created_at":"2026-07-05T11:17:42Z"},{"alias_kind":"pith_short_8","alias_value":"G53P5B4D","created_at":"2026-07-05T11:17:42Z"}],"graph_snapshots":[{"event_id":"sha256:c055c9838a8519f4c10d4b3a0217d87d16b321d9a9897848bfeb73f09d7eb614","target":"graph","created_at":"2026-07-05T11:17:42Z","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/2503.04483/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Inferring Gene Regulatory Networks (GRNs) from gene expression data is crucial for understanding biological processes. While supervised models are reported to achieve high performance for this task, they rely on costly ground truth (GT) labels and risk learning gene-specific biases, such as class imbalances of GT interactions, rather than true regulatory mechanisms. To address these issues, we introduce InfoSEM, an unsupervised generative model that leverages textual gene embeddings as informative priors, improving GRN inference without GT labels. InfoSEM can also integrate GT labels as an add","authors_text":"Artem Moskalev, Mangal Prakash, Rui Liao, Shuwei Li, Song-Jun Xu, Tianyu Cui, Tommaso Mansi","cross_cats":["cs.LG","q-bio.QM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-06T14:32:00Z","title":"InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.04483","kind":"arxiv","version":2},"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":"sha256:9db458106b64d5bb4fc7d29075a9dd66d222f21720af29943c109bf53ac245c8","target":"record","created_at":"2026-07-05T11:17:42Z","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":"f2306884bc889e93d4ec31b19e4fdf61b5e9a245d7875e33760109551d5d607d","cross_cats_sorted":["cs.LG","q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-03-06T14:32:00Z","title_canon_sha256":"71016de3463902f4c3e2bfae7ee42341e0add2dcecb2926d33cb06d017f85a88"},"schema_version":"1.0","source":{"id":"2503.04483","kind":"arxiv","version":2}},"canonical_sha256":"3776fe8783e384d56b5d6f25031e3642a0b85a48ee91599bf367fd39457fdbea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3776fe8783e384d56b5d6f25031e3642a0b85a48ee91599bf367fd39457fdbea","first_computed_at":"2026-07-05T11:17:42.810100Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:42.810100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qFJT7532MXmXNOnbeoRLH1n0uAXp0dDBuVkt619EWRCMvoY4kU9Dlx9pXvsRnB6PFk1uHJQh1DnaI7Z7zw3kAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:42.810649Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.04483","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9db458106b64d5bb4fc7d29075a9dd66d222f21720af29943c109bf53ac245c8","sha256:c055c9838a8519f4c10d4b3a0217d87d16b321d9a9897848bfeb73f09d7eb614"],"state_sha256":"ef6a4277af2327eaebceda8e395a909836fc07a3f39b7d29ed83a1d4c639b4b0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sEB+rHg/X1zQDGT7PARKj5Pohd7KtcTOpy5nopHFTuxa3sKbGb/1UvSxP2ffn4TULwkYJadctZg/2z1dvLHFDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T23:19:05.094893Z","bundle_sha256":"2cd671c12404259adb44cd97d8dc4a85d487f1a2ebeb46ebc3844c8c88c723f7"}}