Y-Type Branch Waveguide Used in Resonant Micro-Optic Gyro

Wang, Jun Jie; Feng, Li Shuang; Wang, Wei; Liu, Hui Lan; Li, Ru Ya

doi:10.4028/www.scientific.net/kem.562-565.222

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All these three methods applied the multi-class classification paradigm for function prediction: first predicting a score between 0 and 1 as the predicted probability that the input enzyme has a particular EC number and then generating all EC numbers with predicted probability greater than 0.5 (except for DeepFRI which used 0.1 as cutoff) as the final predicted function annotations for the input enzyme. We evaluated all methods using metrics F1 score, which assesses prediction accuracy considering both precision and recall, and the normalized discounted cumulative gain (nDCG) [33], which rewards higher rankings of true positives over false negatives in the prediction set (S1 Text). On a more challenging test set (test proteins with [0, 30%) sequence identity to training proteins), we further evaluated all methods by drawing the micro-averaged precision-recall curves.…”

Section: Cpec Achieves Accurate Enzyme Function Predictionsmentioning

confidence: 99%

Leveraging conformal prediction to annotate enzyme function space with limited false positives

Ding,

Luo,

Luo

2024

PLoS Comput Biol

View full text Add to dashboard Cite

Machine learning (ML) is increasingly being used to guide biological discovery in biomedicine such as prioritizing promising small molecules in drug discovery. In those applications, ML models are used to predict the properties of biological systems, and researchers use these predictions to prioritize candidates as new biological hypotheses for downstream experimental validations. However, when applied to unseen situations, these models can be overconfident and produce a large number of false positives. One solution to address this issue is to quantify the model’s prediction uncertainty and provide a set of hypotheses with a controlled false discovery rate (FDR) pre-specified by researchers. We propose CPEC, an ML framework for FDR-controlled biological discovery. We demonstrate its effectiveness using enzyme function annotation as a case study, simulating the discovery process of identifying the functions of less-characterized enzymes. CPEC integrates a deep learning model with a statistical tool known as conformal prediction, providing accurate and FDR-controlled function predictions for a given protein enzyme. Conformal prediction provides rigorous statistical guarantees to the predictive model and ensures that the expected FDR will not exceed a user-specified level with high probability. Evaluation experiments show that CPEC achieves reliable FDR control, better or comparable prediction performance at a lower FDR than existing methods, and accurate predictions for enzymes under-represented in the training data. We expect CPEC to be a useful tool for biological discovery applications where a high yield rate in validation experiments is desired but the experimental budget is limited.

show abstract

Section: Cpec Achieves Accurate Enzyme Function Predictionsmentioning

confidence: 99%

Leveraging conformal prediction to annotate enzyme function space with limited false positives

Ding,

Luo,

Luo

2024

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…[111][112][113] The field has matured greatly in recent years, insofar that diverse strategies have been developed to allow expression of fluorescent proteins in specific locations within live cells, produce fusion with different targets for study of specific cellular activities, cover the full visible spectrum, and accommodate diverse detection schemes, as highlighted by the recently published reviews. 107,114,115 While the main applications of fluorescent proteins are for imaging cellular activities, they have also been employed in cell-free bioassays for target detection. For example, Liao et.al constructed a specific and biocompatible fluorescent sensor based on the hybrid of GFP chromophore and peptide for HSA detection.…”

Section: Fluorescent Proteinsmentioning

confidence: 99%