ArticleInternational journal of molecular sciences2023
Generation and Next-Generation Sequencing-Based Characterization of a Large Human Combinatorial Antibody Library.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- High-throughput strategies for monoclonal antibody screening: advances and challenges.Journal of biological engineering · 2025Review
- Single-Stranded Variable Fragment Gene Libraries Built for Phage Display: An Updated Review of Design, Selection and Application.Journal of microbiology and biotechnology · 2024Review
- The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design.Journal of biomedical science · 2024Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Antibody phage display is a key technology for the discovery and development of target-specific monoclonal antibodies (mAbs) for use in research, diagnostics, and therapy. The construction of a high-quality antibody library, with larger and more diverse antibody repertoires, is essential for the successful development of phage display-derived mAbs. In this study, a large human combinatorial single-chain variable fragment library (1.5 × 10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.