{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:KW4U2RH2TX54IAIJH5KJ46OORS","short_pith_number":"pith:KW4U2RH2","schema_version":"1.0","canonical_sha256":"55b94d44fa9dfbc401093f549e79ce8c9345f5edb03ef525aa71a3483231cc32","source":{"kind":"arxiv","id":"1610.02281","version":1},"attestation_state":"computed","paper":{"title":"Effective Classification of MicroRNA Precursors Using Combinatorial Feature Mining and AdaBoost Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"q-bio.GN","authors_text":"Jason T. L. Wang, Ling Zhong","submitted_at":"2016-10-06T04:35:37Z","abstract_excerpt":"MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides (nt) that are derived from precursor molecules. These precursor molecules or pre-miRNAs often fold into stem-loop hairpin structures. However, a large number of sequences with pre-miRNA-like hairpins can be found in genomes. It is a challenge to distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (referred to as pseudo pre-miRNAs). Several computational methods have been developed to tackle this challenge. In this paper we propose a new method, called MirID, for identifying and classifying"},"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":"1610.02281","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2016-10-06T04:35:37Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"d138e0f3046cde50fc2d154bd2fb2a5ea40494f79d056b6d45f989a8b6778780","abstract_canon_sha256":"66033de33c3e9daf926f47dd84fce229bbb0d2c77b15953b0939ef33eb120c2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:57.882549Z","signature_b64":"r41osINVzbK5FGDcnExy4wWyaYJ0OL8IU0N3etyOCY7zkC4EsDjOaoWIpyF9c+zJfiEBYpFvYZkIzP6uYt+ECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55b94d44fa9dfbc401093f549e79ce8c9345f5edb03ef525aa71a3483231cc32","last_reissued_at":"2026-05-18T01:02:57.882087Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:57.882087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Effective Classification of MicroRNA Precursors Using Combinatorial Feature Mining and AdaBoost Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"q-bio.GN","authors_text":"Jason T. L. Wang, Ling Zhong","submitted_at":"2016-10-06T04:35:37Z","abstract_excerpt":"MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides (nt) that are derived from precursor molecules. These precursor molecules or pre-miRNAs often fold into stem-loop hairpin structures. However, a large number of sequences with pre-miRNA-like hairpins can be found in genomes. It is a challenge to distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (referred to as pseudo pre-miRNAs). Several computational methods have been developed to tackle this challenge. In this paper we propose a new method, called MirID, for identifying and classifying"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02281","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":"1610.02281","created_at":"2026-05-18T01:02:57.882154+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.02281v1","created_at":"2026-05-18T01:02:57.882154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02281","created_at":"2026-05-18T01:02:57.882154+00:00"},{"alias_kind":"pith_short_12","alias_value":"KW4U2RH2TX54","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"KW4U2RH2TX54IAIJ","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"KW4U2RH2","created_at":"2026-05-18T12:30:29.479603+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/KW4U2RH2TX54IAIJH5KJ46OORS","json":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS.json","graph_json":"https://pith.science/api/pith-number/KW4U2RH2TX54IAIJH5KJ46OORS/graph.json","events_json":"https://pith.science/api/pith-number/KW4U2RH2TX54IAIJH5KJ46OORS/events.json","paper":"https://pith.science/paper/KW4U2RH2"},"agent_actions":{"view_html":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS","download_json":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS.json","view_paper":"https://pith.science/paper/KW4U2RH2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.02281&json=true","fetch_graph":"https://pith.science/api/pith-number/KW4U2RH2TX54IAIJH5KJ46OORS/graph.json","fetch_events":"https://pith.science/api/pith-number/KW4U2RH2TX54IAIJH5KJ46OORS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS/action/storage_attestation","attest_author":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS/action/author_attestation","sign_citation":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS/action/citation_signature","submit_replication":"https://pith.science/pith/KW4U2RH2TX54IAIJH5KJ46OORS/action/replication_record"}},"created_at":"2026-05-18T01:02:57.882154+00:00","updated_at":"2026-05-18T01:02:57.882154+00:00"}