{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4JLQO36OR76URWFBDHZK3EJUEF","short_pith_number":"pith:4JLQO36O","schema_version":"1.0","canonical_sha256":"e257076fce8ffd48d8a119f2ad9134214b666f4bddb9bbe587ef80db1ed2ec32","source":{"kind":"arxiv","id":"2605.27986","version":1},"attestation_state":"computed","paper":{"title":"An Evolutionary Approach for Designing Stable and Highly Expressible Low-Immunogenicity Therapeutic mRNA Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.CL","authors_text":"Dhawa Sang Dong, Mausam Gurung, Suraj Kandel","submitted_at":"2026-05-27T05:20:17Z","abstract_excerpt":"Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicity. This study presents a two-stage in-silico framework that integrates deep learning and evolutionary computation for rational mRNA optimization instead of existing state-of-the-art models. In the first stage, a pretrained CodonTransformer (BERT-like Large Language Model) generates biologically coherent mRNA sequences encoding the target antigen. In the second stage, a genetic algorithm (GA) evolves these candidate sequences through codon-aware"},"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":"2605.27986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-27T05:20:17Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"bd8618b0bd67cf09afbf280b84de43e2f5b7b9c0b440b57760f30e6d4fb2f3d2","abstract_canon_sha256":"07eb1c8583c69a92f5edde1ac418b4e390dd3b99f7d34640975f9ff4071c5ad6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:55.288143Z","signature_b64":"5opIjHWtVp9UbkqZ8SiHp3KrVf8vxWjZ3rAi412CkvWql/l0QPtGYQamDJTeXLcBUBNS6gGTAKmwacckeoesBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e257076fce8ffd48d8a119f2ad9134214b666f4bddb9bbe587ef80db1ed2ec32","last_reissued_at":"2026-05-28T01:04:55.287650Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:55.287650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Evolutionary Approach for Designing Stable and Highly Expressible Low-Immunogenicity Therapeutic mRNA Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.CL","authors_text":"Dhawa Sang Dong, Mausam Gurung, Suraj Kandel","submitted_at":"2026-05-27T05:20:17Z","abstract_excerpt":"Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicity. This study presents a two-stage in-silico framework that integrates deep learning and evolutionary computation for rational mRNA optimization instead of existing state-of-the-art models. In the first stage, a pretrained CodonTransformer (BERT-like Large Language Model) generates biologically coherent mRNA sequences encoding the target antigen. In the second stage, a genetic algorithm (GA) evolves these candidate sequences through codon-aware"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27986","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.27986/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":"2605.27986","created_at":"2026-05-28T01:04:55.287740+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.27986v1","created_at":"2026-05-28T01:04:55.287740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27986","created_at":"2026-05-28T01:04:55.287740+00:00"},{"alias_kind":"pith_short_12","alias_value":"4JLQO36OR76U","created_at":"2026-05-28T01:04:55.287740+00:00"},{"alias_kind":"pith_short_16","alias_value":"4JLQO36OR76URWFB","created_at":"2026-05-28T01:04:55.287740+00:00"},{"alias_kind":"pith_short_8","alias_value":"4JLQO36O","created_at":"2026-05-28T01:04:55.287740+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/4JLQO36OR76URWFBDHZK3EJUEF","json":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF.json","graph_json":"https://pith.science/api/pith-number/4JLQO36OR76URWFBDHZK3EJUEF/graph.json","events_json":"https://pith.science/api/pith-number/4JLQO36OR76URWFBDHZK3EJUEF/events.json","paper":"https://pith.science/paper/4JLQO36O"},"agent_actions":{"view_html":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF","download_json":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF.json","view_paper":"https://pith.science/paper/4JLQO36O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.27986&json=true","fetch_graph":"https://pith.science/api/pith-number/4JLQO36OR76URWFBDHZK3EJUEF/graph.json","fetch_events":"https://pith.science/api/pith-number/4JLQO36OR76URWFBDHZK3EJUEF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF/action/storage_attestation","attest_author":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF/action/author_attestation","sign_citation":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF/action/citation_signature","submit_replication":"https://pith.science/pith/4JLQO36OR76URWFBDHZK3EJUEF/action/replication_record"}},"created_at":"2026-05-28T01:04:55.287740+00:00","updated_at":"2026-05-28T01:04:55.287740+00:00"}