arXiv (All)
open access
Feedback-Calibrated Protein Optimization with Batch-Aligned Tail Arbitration
Protein optimization aims to discover high-fitness sequences under a limited experimental budget. Existing machine-learning methods use task-specific predictors, biological priors, or ranking-aware objectives to guide which variants are tested in the next experimental round. However, these methods c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Generative AI for controllable protein sequence design: A survey.#342449
- MetaMP Ecosystem for Unified, Auditable, and Benchmark-Ready Data for Reliable Membrane Protein Annotation.#476852
- MetaMP ecosystem for unified, auditable, and benchmark-ready data for reliable membrane protein annotation#391968
- STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets#6378
- featureCounts: an efficient general purpose program for assigning sequence reads to genomic features#6322
Record · ID 1142116
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive