{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:LOBXJWBSZYMIL4ZR53ZPCC7CHR","short_pith_number":"pith:LOBXJWBS","schema_version":"1.0","canonical_sha256":"5b8374d832ce1885f331eef2f10be23c7d220f198c72ccfdca1a25a66ec29270","source":{"kind":"arxiv","id":"1006.1030","version":1},"attestation_state":"computed","paper":{"title":"Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.ML"],"primary_cat":"cs.AI","authors_text":"Andrej Kastrin, Borut Peterlin","submitted_at":"2010-06-05T08:27:29Z","abstract_excerpt":"Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this prob- lem is by using dimension reduction statistical techniques. Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data. In th"},"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":"1006.1030","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2010-06-05T08:27:29Z","cross_cats_sorted":["stat.AP","stat.ME","stat.ML"],"title_canon_sha256":"432ecb4defd9f57153128eae3b6de1ae3cbb158439f8028a42cbfe49ecf98fd8","abstract_canon_sha256":"66ee04493d1d9f59e0d2722d84964f0038efac841e23879014fb3a8b9745193d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:43.194029Z","signature_b64":"qo81mEZx8Wf0ZVcevZtzXcuTZOR1BFJRxRN0vh3G1879C+PEYZCUULdd+PQTimJUN7YDWyoRjnqAUBGh9ghPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b8374d832ce1885f331eef2f10be23c7d220f198c72ccfdca1a25a66ec29270","last_reissued_at":"2026-05-18T00:19:43.193405Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:43.193405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME","stat.ML"],"primary_cat":"cs.AI","authors_text":"Andrej Kastrin, Borut Peterlin","submitted_at":"2010-06-05T08:27:29Z","abstract_excerpt":"Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this prob- lem is by using dimension reduction statistical techniques. Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data. In th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1006.1030","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1006.1030","created_at":"2026-05-18T00:19:43.193500+00:00"},{"alias_kind":"arxiv_version","alias_value":"1006.1030v1","created_at":"2026-05-18T00:19:43.193500+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1006.1030","created_at":"2026-05-18T00:19:43.193500+00:00"},{"alias_kind":"pith_short_12","alias_value":"LOBXJWBSZYMI","created_at":"2026-05-18T12:26:10.704358+00:00"},{"alias_kind":"pith_short_16","alias_value":"LOBXJWBSZYMIL4ZR","created_at":"2026-05-18T12:26:10.704358+00:00"},{"alias_kind":"pith_short_8","alias_value":"LOBXJWBS","created_at":"2026-05-18T12:26:10.704358+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/LOBXJWBSZYMIL4ZR53ZPCC7CHR","json":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR.json","graph_json":"https://pith.science/api/pith-number/LOBXJWBSZYMIL4ZR53ZPCC7CHR/graph.json","events_json":"https://pith.science/api/pith-number/LOBXJWBSZYMIL4ZR53ZPCC7CHR/events.json","paper":"https://pith.science/paper/LOBXJWBS"},"agent_actions":{"view_html":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR","download_json":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR.json","view_paper":"https://pith.science/paper/LOBXJWBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1006.1030&json=true","fetch_graph":"https://pith.science/api/pith-number/LOBXJWBSZYMIL4ZR53ZPCC7CHR/graph.json","fetch_events":"https://pith.science/api/pith-number/LOBXJWBSZYMIL4ZR53ZPCC7CHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR/action/storage_attestation","attest_author":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR/action/author_attestation","sign_citation":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR/action/citation_signature","submit_replication":"https://pith.science/pith/LOBXJWBSZYMIL4ZR53ZPCC7CHR/action/replication_record"}},"created_at":"2026-05-18T00:19:43.193500+00:00","updated_at":"2026-05-18T00:19:43.193500+00:00"}