{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:E26XU2MQ2MWLIDVIMKXEGRNS7W","short_pith_number":"pith:E26XU2MQ","schema_version":"1.0","canonical_sha256":"26bd7a6990d32cb40ea862ae4345b2fd8df9473cc10a299687124be0d0721870","source":{"kind":"arxiv","id":"1806.09723","version":1},"attestation_state":"computed","paper":{"title":"Automatic Feature Selection in Markov State Models Using Genetic Algorithm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.BM","authors_text":"Diwakar Shukla, Jiangyan Feng, Qihua Chen, Shriyaa Mittal","submitted_at":"2018-06-25T23:01:57Z","abstract_excerpt":"Markov State Models (MSMs) are a powerful framework to reproduce the long-time conformational dynamics of biomolecules using a set of short Molecular Dynamics (MD) simulations. However, precise kinetics predictions of MSMs heavily rely on the features selected to describe the system. Despite the importance of feature selection for large system, determining an optimal set of features remains a difficult unsolved problem. Here, we introduce an automatic approach to optimize feature selection based on genetic algorithms (GA), which adaptively evolves the most fitted solution according to natural "},"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":"1806.09723","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.BM","submitted_at":"2018-06-25T23:01:57Z","cross_cats_sorted":[],"title_canon_sha256":"e0ba5622d676fdc40341cd2175a9379cde5c3bfe7ae3c97e3c550aa40d48c5ac","abstract_canon_sha256":"15e728720f0255eeb0740772c45d6aff91b470e375c813f8c6deef0cd1055e9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:23.615415Z","signature_b64":"tTibdqwcLIwWTA8dF6YxYGUT/O1/Af4Hf8g3QZgW9SCy3BFgmAfRqgmfJKw5HXNJzISlGoQHQNhYi/toZfldDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26bd7a6990d32cb40ea862ae4345b2fd8df9473cc10a299687124be0d0721870","last_reissued_at":"2026-05-18T00:12:23.614594Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:23.614594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Feature Selection in Markov State Models Using Genetic Algorithm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.BM","authors_text":"Diwakar Shukla, Jiangyan Feng, Qihua Chen, Shriyaa Mittal","submitted_at":"2018-06-25T23:01:57Z","abstract_excerpt":"Markov State Models (MSMs) are a powerful framework to reproduce the long-time conformational dynamics of biomolecules using a set of short Molecular Dynamics (MD) simulations. However, precise kinetics predictions of MSMs heavily rely on the features selected to describe the system. Despite the importance of feature selection for large system, determining an optimal set of features remains a difficult unsolved problem. Here, we introduce an automatic approach to optimize feature selection based on genetic algorithms (GA), which adaptively evolves the most fitted solution according to natural "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09723","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":"1806.09723","created_at":"2026-05-18T00:12:23.614697+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.09723v1","created_at":"2026-05-18T00:12:23.614697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.09723","created_at":"2026-05-18T00:12:23.614697+00:00"},{"alias_kind":"pith_short_12","alias_value":"E26XU2MQ2MWL","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"E26XU2MQ2MWLIDVI","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"E26XU2MQ","created_at":"2026-05-18T12:32:19.392346+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/E26XU2MQ2MWLIDVIMKXEGRNS7W","json":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W.json","graph_json":"https://pith.science/api/pith-number/E26XU2MQ2MWLIDVIMKXEGRNS7W/graph.json","events_json":"https://pith.science/api/pith-number/E26XU2MQ2MWLIDVIMKXEGRNS7W/events.json","paper":"https://pith.science/paper/E26XU2MQ"},"agent_actions":{"view_html":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W","download_json":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W.json","view_paper":"https://pith.science/paper/E26XU2MQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.09723&json=true","fetch_graph":"https://pith.science/api/pith-number/E26XU2MQ2MWLIDVIMKXEGRNS7W/graph.json","fetch_events":"https://pith.science/api/pith-number/E26XU2MQ2MWLIDVIMKXEGRNS7W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W/action/storage_attestation","attest_author":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W/action/author_attestation","sign_citation":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W/action/citation_signature","submit_replication":"https://pith.science/pith/E26XU2MQ2MWLIDVIMKXEGRNS7W/action/replication_record"}},"created_at":"2026-05-18T00:12:23.614697+00:00","updated_at":"2026-05-18T00:12:23.614697+00:00"}