ArticlePeerJ2019
PhD7Faster 2.0: predicting clones propagating faster from the Ph.D.-7 phage display library by coupling PseAAC and tripeptide composition.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- A comparative analysis of sequence composition in different lots of a phage display peptide library during amplification.Virology journal · 2025Article
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
- AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains.Antibody therapeutics · 2023Article
- A White Plaque, Associated with Genomic Deletion, Derived from M13KE-Based Peptide Library Is Enriched in a Target-Unrelated Manner during Phage Display Biopanning Due to Propagation Advantage.International journal of molecular sciences · 2022Article
- SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody.Frontiers in genetics · 2022Article
- BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides.Frontiers in genetics · 2022Article
- Article
- PDL1Binder: Identifying programmed cell death ligand 1 binding peptides by incorporating next-generation phage display data and different peptide descriptors.Frontiers in microbiology · 2022Article
- In vivo phage display: identification of organ-specific peptides using deep sequencing and differential profiling across tissues.Nucleic acids research · 2021Article
- Article
- Article
- SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences.BioMed research international · 2020Article
- SAROTUP: a suite of tools for finding potential target-unrelated peptides from phage display data.International journal of biological sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selection from phage display libraries empowers isolation of high-affinity ligands for various targets. However, this method also identifies propagation-related target-unrelated peptides (PrTUPs). These false positive hits appear because of their amplification advantages. In this report, we present PhD7Faster 2.0 for predicting fast-propagating clones from the Ph.D.-7 phage display library, which was developed based on the support vector machine. Feature selection was performed against PseAAC and tripeptide composition using the incremental feature selection method. Ten-fold cross-validation results show that PhD7Faster 2.0 succeeds a decent performance with the accuracy of 81.84%, the Matthews correlation coefficient of 0.64 and the area under the ROC curve of 0.90. The permutation test with 1,000 shuffles resulted in
Indexed as
Identifiers
What OpenQuestion holds
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.