Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Exploring the therapeutic potential of quercetin in cancer treatment: Targeting long non-coding RNAs

The escalating global incidence of cancer, which results in millions of fatalities annually, underscores the pressing need for effective pharmacological interventions across diverse cancer types. Long noncoding RNAs (lncRNAs), a class of RNA molecules that lack protein-coding capacity but profoundly impact gene expression regulation, have emerged as pivotal players in key cellular processes, including proliferation, apoptosis, metastasis, cellular metabolism, and drug resistance. Among natural compounds, quercetin, a phenolic compound abundantly present in fruits and vegetables has garnered attention due to its significant anticancer properties. Quercetin demonstrates the ability to inhibit cancer cell growth and induce apoptosis-a process often impaired in malignant cells. In this comprehensive review, we delve into the therapeutic potential of quercetin in cancer treatment, with a specific focus on its intricate interactions with lncRNAs. We explore how quercetin modulates lncRNA expression and function to exert its anticancer effects. Notably, quercetin suppresses oncogenic lncRNAs that drive cancer development and progression while enhancing tumor-suppressive lncRNAs that impede cancer growth and dissemination. Additionally, we discuss quercetin's role as a chemopreventive agent, which plays a crucial role in mitigating cancer risk. We address research challenges and future directions, emphasizing the necessity for in-depth mechanistic studies and strategies to enhance quercetin's bioavailability and target specificity. By synthesizing existing knowledge, this review underscores quercetin's promising potential as a novel therapeutic strategy in the ongoing battle against cancer, offering fresh insights and avenues for further investigation in this critical field.

miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

MicroRNAs (miRNAs) are an essential type of small molecule RNAs that play significant regulatory roles in organisms. Recent studies have demonstrated that small open reading frames (sORFs) harbored in primary miRNAs (pri-miRNAs) can encode small peptides, known as miPEPs. Plant miPEPs can increase the abundance and activity of cognate miRNAs by promoting the transcription of their corresponding pri-miRNAs, thereby modulating plant traits. Biological experiments are the most effective way to accurately identify miPEPs; however, they are time-consuming and expensive. Hence, an efficient computational method for the identification of miPEPs on a large scale is highly desirable. Up to now, there have been no specialized computational tools for identifying miPEPs. In this work, a novel predictor named miPEPPred-FRL based on an adaptive feature representation learning framework that consists of the feature transformation module and the cascade architecture has been proposed. The feature transformation module integrating a newly designed feature selection method and classifier selection rule is developed to convert sequence-based features into primary class and probabilistic features, which are then fed into the improved cascade architecture to obtain more stable and discriminative augmented features. Finally, the augmented features are utilized to construct the final predictor. Cross-validation experiments illustrate that the novel feature selection method and classifier selection rule contribute to boosting the feature representation ability of the framework. Furthermore, the high accuracy of miPEPPred-FRL on independent testing data suggests that it is a trustworthy and valuable tool for the identification of miPEPs.

Unveiling the secrets of non-coding RNA-encoded peptides in plants: A comprehensive review of mining methods and research progress

Non-coding RNAs (ncRNAs) are not conventionally involved in protein encoding. However, recent findings indicate that ncRNAs possess the capacity to code for proteins or peptides. These ncRNA-encoded peptides (ncPEPs) are vital for diverse plant life processes and exhibit significant potential value. Despite their importance, research on plant ncPEPs is limited, with only a few studies conducted and less information on the underlying mechanisms, and the field remains in its nascent stage. This manuscript provides a comprehensive overview of ncPEPs mining methods in plants, focusing on prediction, identification, and functional analysis. We discuss the strengths and weaknesses of various techniques, identify future research directions in the ncPEPs domain, and elucidate the biological functions and agricultural application prospects of plant ncPEPs. By highlighting the immense potential and research value of ncPEPs, we aim to lay a solid foundation for more in-depth studies in plant science.

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.