Guiding questions to avoid data leakage in biological machine learning applications
Abstract Machine learning methods for extracting patterns from high-dimensional data are
very important in the biological sciences. However, in certain cases, real-world applications …
very important in the biological sciences. However, in certain cases, real-world applications …
A first computational frame for recognizing heparin-binding protein
W Zhu, SS Yuan, J Li, CB Huang, H Lin, B Liao - Diagnostics, 2023 - mdpi.com
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear
neutrophils and an important biomarker of infectious diseases. The correct identification of …
neutrophils and an important biomarker of infectious diseases. The correct identification of …
A statistical analysis of the sequence and structure of thermophilic and non-thermophilic proteins
Thermophilic proteins have various practical applications in theoretical research and in
industry. In recent years, the demand for thermophilic proteins on an industrial scale has …
industry. In recent years, the demand for thermophilic proteins on an industrial scale has …
[HTML][HTML] Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These
proteins selectively and non-covalently interact with hormones and promote growth …
proteins selectively and non-covalently interact with hormones and promote growth …
DeepTP: a deep learning model for thermophilic protein prediction
J Zhao, W Yan, Y Yang - International Journal of Molecular Sciences, 2023 - mdpi.com
Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme
engineering. Most existing thermophilic protein prediction models are based on traditional …
engineering. Most existing thermophilic protein prediction models are based on traditional …
Superior protein thermophilicity prediction with protein language model embeddings
Protein thermostability is important in many areas of biotechnology, including enzyme
engineering and protein-hybrid optoelectronics. Ever-growing protein databases and …
engineering and protein-hybrid optoelectronics. Ever-growing protein databases and …
TemStaPro: protein thermostability prediction using sequence representations from protein language models
Motivation Reliable prediction of protein thermostability from its sequence is valuable for
both academic and industrial research. This prediction problem can be tackled using …
both academic and industrial research. This prediction problem can be tackled using …
Identification of thermophilic proteins based on sequence-based bidirectional representations from transformer-embedding features
Thermophilic proteins have great potential to be utilized as biocatalysts in biotechnology.
Machine learning algorithms are gaining increasing use in identifying such enzymes …
Machine learning algorithms are gaining increasing use in identifying such enzymes …
Discrimination of psychrophilic enzymes using machine learning algorithms with amino acid composition descriptor
A Huang, F Lu, F Liu - Frontiers in Microbiology, 2023 - frontiersin.org
Introduction Psychrophilic enzymes are a class of macromolecules with high catalytic activity
at low temperatures. Cold-active enzymes possessing eco-friendly and cost-effective …
at low temperatures. Cold-active enzymes possessing eco-friendly and cost-effective …
[HTML][HTML] Data-driven strategies for the computational design of enzyme thermal stability: Trends, perspectives, and prospects: Data-driven strategies for enzyme …
Thermal stability is one of the most important properties of enzymes, which sustains life and
determines the potential for the industrial application of biocatalysts. Although traditional …
determines the potential for the industrial application of biocatalysts. Although traditional …