Article
Biochemical Research Methods
Ying Zhang, Fang Ge, Fuyi Li, Xibei Yang, Jiangning Song, Dong-Jun Yu
Summary: In this study, a method called MRM-BERT is developed, which utilizes the BERT model and fine-tuning to accurately identify multiple RNA modifications, providing valuable information for further research.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Review
Biochemical Research Methods
Pablo Acera Mateos, You Zhou, Kathi Zarnack, Eduardo Eyras
Summary: The development of new high-throughput techniques has played a crucial role in discovering the properties of RNA modifications with the help of machine learning. Despite the challenges, this review provides a comprehensive survey of machine learning methods for detecting RNA modifications and strategies for training and testing these methods. The review also highlights current challenges and open questions in RNA modification analysis, and emphasizes the potential of machine learning in addressing these limitations.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Computer Science, Information Systems
Syed Danish Ali, Jee Hong Kim, Hilal Tayara, Kil To Chong
Summary: A new efficient computational model iRhm5CNN has been developed for the identification of RNA 5hmC sites, showing superior performance compared to existing models.
Article
Acoustics
Leyang Cui, Yafu Li, Yue Zhang
Summary: Sequence labeling, a fundamental problem in NLP, assigns labels to tokens in a sequence. We propose a label attention network (LAN) that hierarchically refines label distributions, improving tagging accuracy and speeding up training and testing.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2022)
Article
Chemistry, Multidisciplinary
Daiva Gostautaite, Leonidas Sakalauskas
Summary: This paper discusses the use of multi-label classification methods and machine learning techniques in combination with cognitive-behavioral approaches to predict learners' preferences. By analyzing students' activities in virtual learning environments, teachers can gain insights into cognitive traits and personalize the learning experience accordingly.
APPLIED SCIENCES-BASEL
(2022)
Article
Biochemical Research Methods
Xiuquan Du, Jiajia Hu
Summary: In this study, a novel deep multi-label joint learning framework is proposed to leverage the relationship between multiple labels and binding proteins. A multi-label variant network is designed to explore multi-scale context hidden information, and a multi-label Long Short-Term Memory (multiLSTM) is used to mine the potential relationship between labels. Extensive experiments are carried out to compare the proposed method with other existing methods.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Endocrinology & Metabolism
Peixian Zheng, Na Li, Xianquan Zhan
Summary: Ovarian cancer patients were classified into two subtypes based on RNA-modification regulatory genes, and a specific gene signature was identified with prognostic and predictive abilities for immunotherapy response.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Environmental Sciences
Yuanyuan Liu, Shaoqiang Wang, Jinghua Chen, Bin Chen, Xiaobo Wang, Dongze Hao, Leigang Sun
Summary: In this study, the researchers proposed a transformer-based model, Informer, to predict rice yield in the Indian Indo-Gangetic Plains. By integrating time-series satellite data, environmental variables, and rice yield records, Informer achieved better performance than other models for end-of-season prediction. The model was also able to achieve stable performances for within-season prediction after late September.
Article
Biochemical Research Methods
Kai Zheng, Xin-Lu Zhang, Lei Wang, Zhu-Hong You, Bo-Ya Ji, Xiao Liang, Zheng-Wei Li
Summary: This study proposes a novel computational model called SPRDA based on the structural perturbation method to predict potential disease-associated piRNAs. SPRDA shows high performance on the benchmark dataset and demonstrates the robustness of the method for predicting 10 diseases. This approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Necla Nisa Soylu, Emre Sefer
Summary: Recent work on language models has achieved state-of-the-art performance on various language tasks, with a focus on contextualizing word embeddings. We propose an efficient method called Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences, combining BERT-based model with convolutional neural networks. Our transformer-based approach demonstrates high accuracy and outperforms existing methods across different datasets and species.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biology
Sirui Liang, Yanxi Zhao, Junru Jin, Jianbo Qiao, Ding Wang, Yu Wang, Leyi Wei
Summary: Recent research highlights the crucial role of RNA post-transcriptional modifications in regulating RNA expression and function. Accurate identification of RNA modification sites is essential for understanding RNA function. In this study, a novel RNA modification prediction method called Rm-LR is proposed using deep learning and RNA sequences. Rm-LR outperforms existing methods in predicting multiple types of RNA modifications and contributes to the development of accurate computational models for RNA modification prediction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Analytical
Lin Lin, Changsheng Tong, Feng Guo, Song Fu, Yancheng Lv, Wenhui He
Summary: This paper proposes a novel machine learning prediction method that accurately predicts landing gear performance by selecting key features, using different base learners, and adaptively adjusting weights. The excellent prediction performance of the proposed method is validated through a series of experiments.
Article
Engineering, Civil
Hongyu Hu, Qi Wang, Ming Cheng, Zhenhai Gao
Summary: This paper proposes a trajectory prediction network based on temporal pattern attention, which improves prediction performance and accuracy. By establishing a vehicle of interest model, an interaction model among vehicles is constructed. Experimental results demonstrate that temporal pattern attention can extract hidden features and contribute to understanding vehicle behavior.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Biochemical Research Methods
Fuxu Wang, Haoyan Wang, Lizhuang Wang, Haoyu Lu, Shizheng Qiu, Tianyi Zang, Xinjun Zhang, Yang Hu
Summary: This study introduces the MHCRoBERTa method, which uses RoBERTa pre-training approach to predict the binding affinity between type I MHC and peptides. Experimental results show that MHCRoBERTa outperforms other prediction methods, with a significant improvement on IC50 value, demonstrating its potential in cancer immunotherapy.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Junyi Li, Huinian Li, Xiao Ye, Li Zhang, Qingzhe Xu, Yuan Ping, Xiaozhu Jing, Wei Jiang, Qing Liao, Bo Liu, Yadong Wang
Summary: In this study, we developed a lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. Our method, which includes 6 novel features generated from generalized topological entropy, achieves a higher area under the curve compared to methods with more K-mer features. Our approach is accurate, efficient, and extendable for research on functional elements in DNA sequences.
BMC BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Bowen Song, Xuan Wang, Zhanmin Liang, Jiongming Ma, Daiyun Huang, Yue Wang, Joao Pedro de Magalhaes, Daniel J. Rigden, Jia Meng, Gang Liu, Kunqi Chen, Zhen Wei
Summary: Recent advances in epitranscriptomics have revealed the functional associations between RNA modifications (RMs) and multiple human diseases. This study presents an updated database, RMDisease v2.0, which identifies RM-associated genetic variants that may affect different types of RNA modifications in various organisms. These variants, including disease- and trait-associated genetic variants, may function through perturbations of epitranscriptomic markers. RMDisease v2.0 serves as a valuable resource for studying the genetic drivers of phenotypes in the epitranscriptome layer circuitry.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Cell Biology
Dominic F. Bennett, Anita Goyala, Cyril Statzer, Charles W. Beckett, Alexander Tyshkovskiy, Vadim N. Gladyshev, Collin Y. Ewald, Joao Pedro de Magalhaes
Summary: Searching for drugs with similar gene expression patterns, rilmenidine was found to extend the lifespan of nematodes and rats, mediated by the I1-imidazoline receptor. This study suggests the potential of rilmenidine as a longevity-promoting drug.
Article
Biochemical Research Methods
Yiyou Song, Yue Wang, Xuan Wang, Daiyun Huang, Anh Nguyen, Jia Meng
Summary: In this study, a multi-task computational method called AdaptRM was proposed for synergetic learning of multi-tissue, type, and species RNA modifications from both high- and low-resolution epitranscriptome datasets. The AdaptRM approach outperformed existing models and deep-learning architectures in three case studies, demonstrating its effectiveness and generalization ability. Furthermore, by interpreting the learned models, the potential association between different tissues in terms of epitranscriptome sequence patterns was revealed for the first time.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Marcelo Rodrigues de Holanda Maia, Alexandre Plastino, Alex Freitas, Joao Pedro de Magalhaes
Summary: This paper proposes two new approaches for dealing with data uncertainty. One approach is to select training instances for each model in an ensemble, and the other is to sample features when splitting a node in a Random Forest training. These approaches are applied to classify ageing-related genes and predict drugs' side effects, and the results show that ensembles based on these approaches achieve better predictive performance.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Editorial Material
Genetics & Heredity
Kunqi Chen, Ernesto Picardi, Xiao Han, Giovanni Nigita
FRONTIERS IN GENETICS
(2023)
Article
Biochemistry & Molecular Biology
Zhanmin Liang, Haokai Ye, Jiongming Ma, Zhen Wei, Yue Wang, Yuxin Zhang, Daiyun Huang, Bowen Song, Jia Meng, Daniel J. Rigden, Kunqi Chen
Summary: m(6)A-Atlas v2.0 is a valuable research tool that includes a large number of m(6)A sites and their functional annotations from different technologies and experimental conditions, to facilitate the study of this important RNA modification.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Xuan Wang, Yuxin Zhang, Kunqi Chen, Zhanmin Liang, Jiongming Ma, Rong Xia, Joao Pedro de Magalhaes, Daniel J. Rigden, Jia Meng, Bowen Song
Summary: With the advancement in mapping N7-methylguanosine (m(7)G) RNA methylation sites, a comprehensive resource (m7Ghub v.2.0) has been developed to study m(7)G modification under various physiological contexts. The resource includes the m7GDB database collecting hundreds of thousands of putative m(7)G sites identified in 23 species, the m7GDiseaseDB hosting m(7)G-associated variants including disease-relevant m(7)G-SNPs, and two enhanced analysis modules for interactive analyses.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Medicine, Research & Experimental
Xiaoying Chen, Hong Hu, Xiaohuang Lin, Mengting Chen, Wenqiang Bao, Yajiao Wu, Chutao Li, Yadong Gao, Shaozhang Hou, Qiaomei Yang, Li Chen, Jian Zhang, Kunqi Chen, Qi Wang, An Zhu
Summary: This study assessed the cytotoxicity of Euphorbia factor L1 (EFL1) in human colon adenocarcinoma cells. The findings revealed that EFL1 caused mitochondrial damage, inhibited energy metabolism, suppressed ion and water molecule transporters, and down-regulated tight junction and cytoskeleton proteins, leading to intestinal barrier injury and cytotoxicity.
BIOMEDICINE & PHARMACOTHERAPY
(2023)
Article
Biochemistry & Molecular Biology
Joao Pedro de Magalhaes, Zoya Abidi, Gabriel Arantes dos Santos, Roberto A. Avelar, Diogo Barardo, Kasit Chatsirisupachai, Peter Clark, Evandro A. De-Souza, Emily J. Johnson, Ines Lopes, Guy Novoa, Ludovic Senez, Angelo Talay, Daniel Thornton, Paul Ka Po To
Summary: This article introduces the key features and recent enhancements of the Human Ageing Genomic Resources (HAGR), focusing on its six main databases. These databases cover information related to genes and ageing, longevity, life-history, cellular senescence, and genetic variants associated with human longevity. HAGR also provides various tools and gene expression signatures.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Yue Wang, Zhen Wei, Jionglong Su, Frans Coenen, Jia Meng
Summary: RgnTX is an R/Bioconductor tool for colocalization analysis of transcriptome elements. It considers transcriptome heterogeneity and isoform ambiguity, and directly utilizes transcriptome annotation. It offers various permutation test models to simulate realistic transcriptome-wide backgrounds. The tool supports testing of transcriptome elements without clear isoform association and provides pre-defined functions for visualization and multiple hypothesis testing.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Cell Biology
Cyril Lagger, Eugen Ursu, Anais Equey, Roberto A. Avelar, Angela Oliveira Pisco, Robi Tacutu, Joao Pedro de Magalhaes
Summary: Dysregulation of intercellular communication is a hallmark of aging. Here the authors provide a bioinformatics tool to infer changes in cell-cell signaling and an atlas of age-related communication changes in 23 mouse tissues.
