Uncertainty quantification in drug design
Highlights•Review of the state-of-the-art in uncertainty quantification in drug
design.•Examples from drug-design settings are provided.•Impact on decision making is …
design.•Examples from drug-design settings are provided.•Impact on decision making is …
Machine learning for enzyme engineering, selection and design
R Feehan, D Montezano… - … Engineering, Design and …, 2021 - academic.oup.com
Abstract Machine learning is a useful computational tool for large and complex tasks such as
those in the field of enzyme engineering, selection and design. In this review, we examine …
those in the field of enzyme engineering, selection and design. In this review, we examine …
Learning the regulatory code of gene expression
Data-driven machine learning is the method of choice for predicting molecular phenotypes
from nucleotide sequence, modeling gene expression events including protein-DNA …
from nucleotide sequence, modeling gene expression events including protein-DNA …
G4Boost: a machine learning-based tool for quadruplex identification and stability prediction
Background G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary
structures involved in important biological processes. Although every G4 motif has the …
structures involved in important biological processes. Although every G4 motif has the …
Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning
Accurate prediction of the optimal catalytic temperature (T opt) of enzymes is vital in
biotechnology, as enzymes with high T opt values are desired for enhanced reaction rates …
biotechnology, as enzymes with high T opt values are desired for enhanced reaction rates …
Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network
Integrating wind energy forecasting into urban city energy management systems offers
significant potential for optimizing energy usage, reducing the carbon footprint, and …
significant potential for optimizing energy usage, reducing the carbon footprint, and …
Predicting gestational diabetes mellitus risk at 11–13 weeks' gestation: the role of extrachromosomal circular DNA
J Wang, P Huang, F Hou, D Hao, W Li, H Jin - Cardiovascular Diabetology, 2024 - Springer
Background Gestational diabetes mellitus (GDM) significantly impacts maternal and infant
health both immediately and over the long term, yet effective early diagnostic biomarkers are …
health both immediately and over the long term, yet effective early diagnostic biomarkers are …
The statistical advantage of automatic NLG metrics at the system level
Estimating the expected output quality of generation systems is central to NLG. This paper
qualifies the notion that automatic metrics are not as good as humans in estimating system …
qualifies the notion that automatic metrics are not as good as humans in estimating system …
Is the performance of my deep network too good to be true? A direct approach to estimating the Bayes error in binary classification
There is a fundamental limitation in the prediction performance that a machine learning
model can achieve due to the inevitable uncertainty of the prediction target. In classification …
model can achieve due to the inevitable uncertainty of the prediction target. In classification …
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
Measurements of protein–ligand interactions have reproducibility limits due to experimental
errors. Any model based on such assays will consequentially have such unavoidable errors …
errors. Any model based on such assays will consequentially have such unavoidable errors …