Article
Immunology
Hani Sabaie, Zoha Salkhordeh, Mohammad Reza Asadi, Soudeh Ghafouri-Fard, Nazanin Amirinejad, Mahla Askarinejad Behzadi, Bashdar Mahmud Hussen, Mohammad Taheri, Maryam Rezazadeh
Summary: This study aimed to clarify the molecular regulatory mechanisms involved in T-cells responses in MS by exploring the lncRNA-associated ceRNA axes. The research identified a ceRNA regulatory relationship among SNHG1, hsa-miR-197-3p, YOD1, ZNF101 and downstream connected genes, potentially playing a role in the pathogenesis of MS. Pathway enrichment analysis showed that differentially expressed mRNAs were enriched in Protein processing in endoplasmic reticulum and Herpes simplex virus 1 infection pathways.
FRONTIERS IN IMMUNOLOGY
(2021)
Article
Biotechnology & Applied Microbiology
Sebastien Riquier, Marc Mathieu, Chloe Bessiere, Anthony Boureux, Florence Ruffle, Jean-Marc Lemaitre, Farida Djouad, Nicolas Gilbert, Therese Commes
Summary: The study developed a dedicated bioinformatics pipeline to identify unannotated lncRNAs in MSCs, highlighting novel lncRNAs with high cell specificity. Through original and efficient methods for functional prediction, it was demonstrated that each candidate represents a specific state of MSC biology. The approach showed potential for utilizing lncRNAs as cell markers and suggested promising directions for future experimental investigations.
Article
Biochemistry & Molecular Biology
Guanzhong Chen, Bowen Liu, Shiqun Chen, Huanqiang Li, Jin Liu, Ziling Mai, Enzhao Chen, Chunyun Zhou, Guoli Sun, Zhaodong Guo, Li Lei, Shanyi Huang, Liyao Zhang, Min Li, Ning Tan, Hong Li, Yulin Liao, Jia Liu, Jiyan Chen, Yong Liu
Summary: This study identified 357 differentially expressed lncRNAs in PC-AKI, with lnc-HILPDA and lnc-PRND emerging as potential early detection biomarkers in both rat models and clinical samples. These lncRNAs showed promising accuracy in distinguishing PC-AKI patients, with a high sensitivity and specificity. Time-course experiments revealed the optimal timing for detecting these lncRNAs in PC-AKI rats, highlighting their clinical implications for early detection and stratification of PC-AKI.
INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES
(2021)
Review
Cell Biology
Lina Ma, Zhang Zhang
Summary: This comment provides an overview of the types and features of lncRNA databases worldwide, and discusses their contribution to lncRNA research.
NATURE REVIEWS MOLECULAR CELL BIOLOGY
(2023)
Article
Psychiatry
Hani Sabaie, Mahdi Gholipour, Mohammad Reza Asadi, Samin Abed, Mirmohsen Sharifi-Bonab, Mohammad Taheri, Bashdar Mahmud Hussen, Serge Brand, Seyedeh Morvarid Neishabouri, Maryam Rezazadeh
Summary: This study used bioinformatics to identify ceRNA axes associated with long non-coding RNA (lncRNA) in the BA10 region of the brain in patients with schizophrenia. The study identified DElncRNA-miRNA-DEmRNA axes and identified key genes and miRNAs related to the pathophysiology of schizophrenia.
FRONTIERS IN PSYCHIATRY
(2022)
Article
Biochemistry & Molecular Biology
Yongqu Lu, Wendong Wang, Zhenzhen Liu, Junren Ma, Xin Zhou, Wei Fu
Summary: Our study established a metabolism-related lncRNA signature for predicting outcomes of CRC patients, which may contribute to individualized prevention and treatment.
MOLECULAR MEDICINE
(2021)
Article
Oncology
Dongfang Jiang, Tiange Wu, Naipeng Shi, Yong Shan, Jinfeng Wang, Hua Jiang, Yuqing Wu, Mengxue Wang, Jian Li, Hui Liu, Ming Chen
Summary: This study identified a lncRNA model (GILncs) based on genomic instability for predicting the overall survival of ccRCC patients. GILncSig was confirmed as an independent predictor for the prognosis of ccRCC patients, with higher efficiency and accuracy compared to other RCC prognostic models.
FRONTIERS IN ONCOLOGY
(2022)
Review
Cell Biology
Jihui Lee, Hara Kang
Summary: Sarcopenia is an age-related disease that can be regulated by alterations in the expression levels of non-coding RNAs, leading to muscle atrophy and dysfunction. Exercise can affect the expression patterns of non-coding RNAs involved in sarcopenia.
Article
Genetics & Heredity
An-Cheng Qin, Yi Qian, Yu-Yuan Ma, Yong Jiang, Wei-Feng Qian
Summary: This study identified RP11-395G23.3 as a potential target for the diagnosis of anaplastic thyroid carcinoma (ATC) by increasing ROR1 via sponging miR-124-3p.
FRONTIERS IN GENETICS
(2021)
Review
Oncology
Gaurav Kumar Pandey, Chandrasekhar Kanduri
Summary: This article reviews the functional role and potential therapeutic applications of long non-coding RNA (lncRNA) in tumor development and discusses the extent of their decisive contribution to cancer development.
Review
Plant Sciences
Edmundo Dominguez-Rosas, Miguel angel Hernandez-Onate, Selene-Lizbeth Fernandez-Valverde, Martin Ernesto Tiznado-Hernandez
Summary: Eukaryotic genomes contain thousands of RNA molecules, with only a small portion being translated into proteins. Among these non-coding elements, long non-coding RNAs (lncRNAs) play important roles in various biological processes. However, studying lncRNAs is challenging due to limited conservation at the sequence level and tissue-specific expression patterns. Recent advancements in crop plant genomes and computational tools provide promising resources for studying plant lncRNAs. Understanding lncRNAs can help improve crop productivity and nutritional content, as well as enhance postharvest characteristics of fruits and vegetables.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Plant Sciences
Marianne C. Kramer, Hee Jong Kim, Kyle R. Palos, Benjamin A. Garcia, Eric Lyons, Mark A. Beilstein, Andrew D. L. Nelson, Brian D. Gregory
Summary: This article investigates a previously unstudied group of lncRNAs, lncCOBRA, and demonstrates that one member, lncCOBRA1, shows tissue and developmental specific expression in Arabidopsis thaliana. It is shown that plants lacking lncCOBRA1 exhibit delayed germination and stunted growth, indicating the important role of lncCOBRA1 in plant development.
FRONTIERS IN PLANT SCIENCE
(2022)
Review
Environmental Sciences
Zhuo Zhang, Sophia Shi, Jingxia Li, Max Costa
Summary: Most transcripts from human genomes are non-coding RNAs (ncRNAs) divided into long (lncRNAs) and small non-coding RNAs (sncRNAs). Maternally expressed gene 3 (MEG3) is a lncRNA that functions as a tumor suppressor and is downregulated in various cancers. Heavy metals like hexavalent chromium (Cr(VI)), arsenic, nickel, and cadmium are human carcinogens that can cause lung cancer. In vitro studies have shown that chronic exposure to these metals can transform normal cells into cancer cells, and MEG3 plays a role in suppressing this transformation.
Review
Agriculture, Dairy & Animal Science
Chhavi Choudhary, Shivasmi Sharma, Keshav Kumar Meghwanshi, Smit Patel, Prachi Mehta, Nidhi Shukla, Duy Ngoc Do, Subhash Rajpurohit, Prashanth Suravajhala, Jayendra Nath Shukla
Summary: Long-non-coding RNAs (lncRNAs) are transcripts of more than 200 nucleotides without protein-coding potential, and while their functions in insects are not well understood, they are believed to play important roles in insect development and regulation. Studies have shown that lncRNAs are involved in various aspects of insect biology, and their regulatory roles are gaining attention in both animals and plants.
Article
Multidisciplinary Sciences
Lei Shi, Peter Magee, Matteo Fassan, Sudhakar Sahoo, Hui Sun Leong, Dave Lee, Robert Sellers, Laura Brulle-Soumare, Stefano Cairo, Tiziana Monteverde, Stefano Volinia, Duncan D. Smith, Gianpiero Di Leva, Francesca Galuppini, Athanasios R. Paliouras, Kang Zeng, Raymond O'Keefe, Michela Garofalo
Summary: This study reveals the role of KIMAT1 in maintaining KRAS signaling in lung cancer progression, suggesting that targeting it may be a strategy to prevent KRAS-induced tumorigenesis.
NATURE COMMUNICATIONS
(2021)
Article
Biochemical Research Methods
Ronghui You, Yuxuan Liu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: With the rapid increase in biomedical articles, the need for large-scale automatic Medical Subject Headings (MeSH) indexing has grown significantly. This study proposes a computationally lighter method, BERTMeSH, for MeSH indexing using full text and deep learning, outperforming existing methods like FullMeSH in terms of efficiency and flexibility. BERTMeSH utilizes the state-of-the-art BERT model and a transfer learning strategy to achieve superior performance in indexing.
Article
Computer Science, Artificial Intelligence
Kishan Wimalawarne, Hiroshi Mamitsuka
Summary: We investigate optimal conditions for inducing low-rankness of higher order tensors using convex tensor norms with reshaped tensors. Proposed reshaped tensor nuclear norm and reshaped latent tensor nuclear norm for regularization and combining multiple tensors, respectively. Through generalization bounds and experiments, the novel reshaping norms are shown to lead to lower complexities, favorably compared to existing tensor norms.
Article
Multidisciplinary Sciences
Hiroto Kaneko, Romain Blanc-Mathieu, Hisashi Endo, Samuel Chaffron, Tom O. Delmont, Morgan Gaia, Nicolas Henry, Rodrigo Hernandez-Velazquez, Canh Hao Nguyen, Hiroshi Mamitsuka, Patrick Forterre, Olivier Jaillon, Colomban de Vargas, Matthew B. Sullivan, Curtis A. Suttle, Lionel Guidi, Hiroyuki Ogata
Summary: There is a significant association between viral community composition and carbon export efficiency on a global scale, with viruses predicted to infect ecologically important hosts playing a crucial role in this process. These findings suggest that viruses likely act in the carbon pump process at a large scale in a manner dependent on their hosts and ecosystem dynamics.
Article
Biochemical Research Methods
Menglan Cai, Canh Hao Nguyen, Hiroshi Mamitsuka, Limin Li
Summary: The study introduces a method called CROSS-species gene set enrichment analysis (XGSEA) to predict the enrichment significance of a given gene set in a target species based on gene expression data from a source species, using domain adaptation and regression analysis to improve accuracy. Experimental results show that XGSEA significantly outperforms three baseline methods in most cases.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: Exploring the relationship between human proteins and abnormal phenotypes is crucial for disease prevention, diagnosis and treatment. HPOFiller, a graph convolutional network-based approach, aims to predict missing HPO annotations and outperforms other state-of-the-art methods through stringent evaluations.
Article
Biochemical Research Methods
Ronghui You, Shuwei Yao, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: DeepGraphGO is a multispecies graph neural network-based method aimed at solving the problem of automated function prediction of proteins. By utilizing protein sequence and high-order protein network information, a single model can be trained for all species, providing more training samples for AFP. Experimental results demonstrate that DeepGraphGO significantly outperforms other state-of-the-art methods, including network-based GeneMANIA, deepNF, and clusDCA.
Review
Biochemical Research Methods
Betul Guvenc Paltun, Samuel Kaski, Hiroshi Mamitsuka
Summary: Drug combination therapy is a promising strategy for treating complex diseases, especially in cancer patients where knowledge is limited. Machine learning methods offer an effective way to improve therapeutic efficacy and overcome drug resistance. Data integration and experimental comparisons play a crucial role in drug combination analysis.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Summary: This paper proposes a method for learning the weights of subtree patterns within the framework of WWL kernels, demonstrating its effectiveness in graph classification tasks and showcasing its validity through experiments on synthetic and real-world datasets.
Article
Biochemical Research Methods
Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: Deciphering the relationship between human genes/proteins and abnormal phenotypes is crucial for disease prevention and treatment, requiring computational predictions. The HPODNets model, with features including multiple network input, semi-supervised learning, and deep graph convolutional network, outperforms other methods in predicting human protein-phenotype associations.
Article
Multidisciplinary Sciences
Santosh Hiremath, Samantha Wittke, Taru Palosuo, Jere Kaivosoja, Fulu Tao, Maximilian Proll, Eetu Puttonen, Pirjo Peltonen-Sainio, Pekka Marttinen, Hiroshi Mamitsuka
Summary: This study investigates the feasibility of using satellite images and machine learning models to classify agricultural field parcels into those with and without crop loss. Despite the poor quality of data, the random forest model shows promising results in identifying new crop-loss fields based on reference fields of the same year. There is potential for various applications in efficient agricultural monitoring and verifying crop-loss claims.
Article
Biochemical Research Methods
Ronghui You, Wei Qu, Hiroshi Mamitsuka, Shanfeng Zhu
Summary: This study proposes a deep learning model, DeepMHCII, based on peptide binding cores and introduces a binding interaction convolution layer to better model the biological interactions between peptides and MHC II molecules. Extensive experiments demonstrate that DeepMHCII outperforms existing methods in terms of performance and can effectively predict binding cores.
Article
Biochemical Research Methods
Duc Anh Nguyen, Canh Hao Nguyen, Peter Petschner, Hiroshi Mamitsuka
Summary: Predicting the side effects of drug-drug interactions is an important task in pharmacology. Existing methods use hypergraph neural networks to learn the relationships between drugs and side effects but cannot accurately represent the multiple mechanisms of side effects. In this paper, we propose SPARSE, a method that encodes the DDI hypergraph and drug features to learn multiple combinations of latent features of drugs and side effects. By controlling model sparsity through a sparse prior, SPARSE achieves superior predictive performance and interpretability advantage.
Article
Biochemical Research Methods
Betul Guvenc Paltun, Samuel Kaski, Hiroshi Mamitsuka
Summary: Detecting predictive biomarkers from multi-omics data is crucial for precision medicine, but choosing reliable data sources is challenging. We propose the DIVERSE framework which integrates diverse data sets to predict drug responses, and it outperforms other methods in empirical experiments.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Duc Anh Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Summary: Predicting drug-drug interactions (DDIs) involves predicting the side effects of drug pairs using drug information and known side effects. This problem can be solved by predicting labels for each drug pair in a DDI graph, where drugs are nodes and interacting drugs with known labels are edges. Graph neural networks (GNNs) are commonly used for this problem, but they may not perform well for infrequent labels and complicated label relationships. To address this, the authors propose CentSmoothie, a hypergraph neural network (HGNN) that learns representations of drugs and labels using a central-smoothing formulation. Experimental results on simulations and real datasets demonstrate the superiority of CentSmoothie.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Canh Hao Nguyen, Hiroshi Mamitsuka
Summary: Hypergraph is a generalization of graph for representing high-order relations on a set of objects. To address the issue of irrelevant or noisy data, a sparse learning framework is incorporated into learning on hypergraphs. Sparse smooth formulations are proposed to learn smooth functions and induce sparsity on both hyperedges and nodes of hypergraphs.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)