Article
Genetics & Heredity
Min Chen, Yingwei Deng, Ang Li, Yan Tan
Summary: This article presents an integrated method called LPARP, based on label-propagation algorithm and random projection, for predicting lncRNA-disease associations. Empirical experiments show that LPARP outperforms existing methods and is validated in case studies of various diseases. LPARP can serve as an effective and reliable tool for biomedical research.
FRONTIERS IN GENETICS
(2022)
Article
Biochemical Research Methods
Qiang Yang, Xiaokun Li
Summary: This study proposed a computational model named BiGAN for predicting lncRNA-disease associations. By utilizing disease semantic similarity, lncRNA sequence similarity, and Gaussian interaction profile kernel similarities, features were constructed to predict unverified associations, showing significantly better performance than other methods.
BMC BIOINFORMATICS
(2021)
Review
Pharmacology & Pharmacy
Jing Yan, Ruobing Wang, Jianjun Tan
Summary: Mutations and dysregulation of lncRNAs play a significant role in the development of complex human diseases. Predicting new potential LDAs can aid in understanding disease pathogenesis, detecting disease markers, and improving disease diagnosis, prevention, and treatment. Computational methods have proven to be effective in narrowing down the screening scope of biological experiments, reducing their duration and cost. This review highlights recent advances in computational methods for predicting LDAs, including LDA databases, lncRNA/disease similarity calculations, and advanced computational models. The limitations of various computational models are analyzed, and future challenges and directions for development are discussed.
DRUG DISCOVERY TODAY
(2023)
Article
Computer Science, Information Systems
Yingjun Ma
Summary: Long non-coding RNA (lncRNA) plays an important role in various biological processes and its mutations and disorders are related to the pathogenesis of multiple human diseases. Accurately predicting potential lncRNA-disease associations, especially for new lncRNAs and diseases, remains a challenge. In this study, a new method called DeepMNE is proposed, which utilizes deep multi-network embedding to discover potential associations between lncRNAs and diseases.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Mei-Neng Wang, Zhu-Hong You, Lei Wang, Li-Ping Li, Kai Zheng
Summary: In this study, a new computational approach LDGRNMF was developed to predict lncRNA-disease associations, considering similarity calculation based on Gaussian interaction profile kernel and disease semantic information, as well as nearest known neighbor interaction profiles weighting to reconstruct association matrix. LDGRNMF outperformed other methods with an AUC of 0.8985 in cross-validation experiments, and showed high accuracy in predicting potential associations in case studies for stomach cancer, breast cancer, and lung cancer.
Article
Biology
Nan Sheng, Lan Huang, Yuting Lu, Hao Wang, Lili Yang, Ling Gao, Xuping Xie, Yuan Fu, Yan Wang
Summary: This review introduces computational methods and resources related to long non-coding RNA (lncRNA) and disease associations, discusses current challenges and future trends in the field.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Li Wang, Cheng Zhong
Summary: This paper proposes a novel computational method (gGATLDA) based on graph attention network for predicting lncRNA-disease associations. Experimental results show that the method outperforms state-of-the-art methods in terms of prediction accuracy and performance evaluation metrics.
BMC BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Hong Shi, Xiaomeng Zhang, Lin Tang, Lin Liu
Summary: In this study, a novel method called HGNNLDA is proposed for predicting lncRNA-disease associations. The method constructs a heterogeneous network and utilizes a heterogeneous graph neural network with attention mechanism to effectively identify potential associations in multi-source data. Compared to existing models, HGNNLDA shows better prediction performance and demonstrates the ability to predict new diseases.
SCIENTIFIC REPORTS
(2022)
Article
Biochemical Research Methods
Guobo Xie, Jiawei Jiang, Yuping Sun
Summary: Increasing experiments have shown that lncRNAs play a role in various biological processes, and their mutations and disorders are associated with multiple diseases. However, verifying the relationships between lncRNAs and diseases is time-consuming and laborious. This study proposes a computational method called LDA-LNSUBRW for predicting lncRNA-disease associations. The method achieved effective performance and outperformed other state-of-the-art methods in experimental results. Case studies further demonstrated the ability of LDA-LNSUBRW in predicting relevant lncRNAs for lung cancer, breast cancer, and osteosarcoma.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Jiaxin Zhang, Quanmeng Sun, Cheng Liang
Summary: The paper presents a novel computational framework for predicting lncRNA-disease associations by combining l1-norm graph and multi-label learning, achieving robustness and comparable performance. Experimental results demonstrate the effectiveness of the method in prediction tasks, particularly in the case of prostate cancer where it shows practicality in identifying potential prognostic biomarkers.
CURRENT BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Guobo Xie, Zelin Jiang, Zhiyi Lin, Guosheng Gu, Yuping Sun, Qing Su, Ji Cui, Huizhe Zhang
Summary: Long non-coding RNAs (lncRNAs) play a vital role in biological regulation and understanding human diseases. In order to improve prediction performance, we developed a computational method that incorporates higher-order similarity information into the similarity network, achieving better results through a decay function designed by random walk with restart.
CURRENT BIOINFORMATICS
(2023)
Article
Computer Science, Information Systems
Yi Zhang, Min Chen, Xiaolan Xie, Xianhao Shen, Yu Wang
Summary: Long non-coding RNAs play important roles in biological processes, especially in cancer. This study introduces a novel two-stage prediction model (DRW-BNSP) to infer lncRNA-disease associations, achieving high AUC values on two datasets and demonstrating predictive dependability.
Article
Oncology
Anqi Feng, Lingnan He, Tao Chen, Meidong Xu
Summary: This study identified a novel signature of cuproptosis-related lncRNAs that has an impact on the prognosis and immunological features of gastric adenocarcinoma (STAD). The findings provide new biomarkers for the assessment of prognosis and immune therapy in STAD.
FRONTIERS IN ONCOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Alejandro Martinez-Mingo, Guillermo Jorge-Botana, Jose Angel Martinez-Huertas, Ricardo Olmos Albacete
Summary: One of the main challenges in cognitive science is explaining how conceptual knowledge is represented and the mechanisms involved in evaluating the similarity between these representations. Traditional approaches, such as Semantic-Vector Space Models, have limitations in capturing human biases and context effects. Recent theories propose using sequential projections of subspaces based on Quantum Probability Theory to address these limitations. This paper presents a data-driven method to generate multidimensional conceptual subspaces using a traditional Semantic-Vector Space Model and illustrates its effects on various conceptual phenomena.
COGNITIVE SYSTEMS RESEARCH
(2023)
Article
Biochemical Research Methods
Liugen Wang, Min Shang, Qi Dai, Ping-an He
Summary: This study proposes a universal network model called LRWRHLDA for predicting human lncRNA-disease associations. The model constructs four similarity networks and six association networks, and uses the Laplace normalized random walk with restart algorithm for prediction. Experimental results show that the model outperforms other methods and is able to predict isolated lncRNAs related to diseases.
BMC BIOINFORMATICS
(2022)