Deep learning for computational chemistry
The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …
both computer science and computational chemistry. Yet almost two decades later, we are …
Navigating the landscape of enzyme design: from molecular simulations to machine learning
J Zhou, M Huang - Chemical Society Reviews, 2024 - pubs.rsc.org
Global environmental issues and sustainable development call for new technologies for fine
chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the …
chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the …
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
Abstract In the Life Sciences 'omics' data is increasingly generated by different high-
throughput technologies. Often only the integration of these data allows uncovering …
throughput technologies. Often only the integration of these data allows uncovering …
Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis
S Chen, R Yang, M Zhong - Control Engineering Practice, 2021 - Elsevier
Random forest (RF) is an effective method for diagnosing faults of rotating machinery.
However, the diagnosis accuracy enhancement under insufficient labeled samples is still …
However, the diagnosis accuracy enhancement under insufficient labeled samples is still …
Predicting protein residue–residue contacts using deep networks and boosting
J Eickholt, J Cheng - Bioinformatics, 2012 - academic.oup.com
Motivation: Protein residue–residue contacts continue to play a larger and larger role in
protein tertiary structure modeling and evaluation. Yet, while the importance of contact …
protein tertiary structure modeling and evaluation. Yet, while the importance of contact …
MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
We have developed a novel machine-learning approach, MutPred Splice, for the
identification of coding region substitutions that disrupt pre-mRNA splicing. Applying …
identification of coding region substitutions that disrupt pre-mRNA splicing. Applying …
Evaluation of residue–residue contact prediction in CASP10
B Monastyrskyy, D d'Andrea, K Fidelis… - Proteins: Structure …, 2014 - Wiley Online Library
We present the results of the assessment of the intramolecular residue‐residue contact
predictions from 26 prediction groups participating in the 10th round of the CASP …
predictions from 26 prediction groups participating in the 10th round of the CASP …
Generating tertiary protein structures via interpretable graph variational autoencoders
Motivation Modeling the structural plasticity of protein molecules remains challenging. Most
research has focused on obtaining one biologically active structure. This includes the recent …
research has focused on obtaining one biologically active structure. This includes the recent …
Vaccine design and development: exploring the interface with computational biology and AI
Ananya, DC Panchariya, A Karthic… - International Reviews …, 2024 - Taylor & Francis
Computational biology involves applying computer science and informatics techniques in
biology to understand complex biological data. It allows us to collect, connect, and analyze …
biology to understand complex biological data. It allows us to collect, connect, and analyze …
PROTS-RF: a robust model for predicting mutation-induced protein stability changes
The ability to improve protein thermostability via protein engineering is of great scientific
interest and also has significant practical value. In this report we present PROTS-RF, a …
interest and also has significant practical value. In this report we present PROTS-RF, a …