A Transfer Learning-Based Approach for Lysine Propionylation Prediction

Post-translational acylation of proteins in cardiac hypertrophy

Acylations are post-translational modifications in which functional groups are attached to amino acids on proteins. Most acylations (acetylation, butyrylation, crotonylation, lactylation, malonylation, propionylation and succinylation) involve lysine but cysteine (palmitoylation) and glycine (myristoylation) residues can also be altered. Acylations have important roles in physiological and pathophysiological processes, including cardiac hypertrophy and related cardiovascular diseases. These post-translational modifications influence chromatin architecture, transcriptional regulation and metabolic pathways, thereby affecting cardiomyocyte function and pathology. The dynamic interaction between these acylations and their regulatory enzymes, such as histone acetyltransferases, histone deacetylases and sirtuins, underscores the complexity of cellular homeostasis and pathological processes. Emerging evidence highlights the therapeutic potential of targeting acylations to modulate enzyme activity and metabolite levels, offering promising avenues for novel treatments. In this Review, we explore the diverse mechanisms through which acylations contribute to cardiac hypertrophy, highlighting the complexity and potential therapeutic targets in this regulatory network.

A hybrid feature extraction scheme for efficient malonylation site prediction

Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher's score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction.

A survey on extraction of causal relations from natural language text

As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.