首页|Fuzhou University Reports Findings in Machine Learning (PlantNh- Kcr: a deep learning model for predicting non-histone crotonylation sites in plants)
Fuzhou University Reports Findings in Machine Learning (PlantNh- Kcr: a deep learning model for predicting non-histone crotonylation sites in plants)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
New research on Machine Learning is the subject of a report. According to news originating from Fuzhou, People's Republic of China, by NewsRx correspondents, research stated, "Lysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis." Our news journalists obtained a quote from the research from Fuzhou University, "Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants. Most plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models. PlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites."
FuzhouPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesHistonesMachine LearningNucleoproteinsProteins
NETL
NSTL
Springer Nature
