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Zhang W, Zhang P, Sun W, Xu J, Liao L, Cao Y, Han Y. Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network. PeerJ 2024; 12:e17396. [PMID: 38799058 PMCID: PMC11122044 DOI: 10.7717/peerj.17396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.
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Affiliation(s)
- Weihan Zhang
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, The Innovative Academy of Seed Design of Chinese Academy of Sciences, Wuhan, Hubei Province, China
- Sino-African Joint Research Center, Chinese Academy of Sciences, Wuhan, Hubei Province, China
| | - Ping Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Weicheng Sun
- College of Informatics, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Jinsheng Xu
- College of Informatics, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Liao Liao
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, The Innovative Academy of Seed Design of Chinese Academy of Sciences, Wuhan, Hubei Province, China
- Sino-African Joint Research Center, Chinese Academy of Sciences, Wuhan, Hubei Province, China
| | - Yunpeng Cao
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, The Innovative Academy of Seed Design of Chinese Academy of Sciences, Wuhan, Hubei Province, China
- Sino-African Joint Research Center, Chinese Academy of Sciences, Wuhan, Hubei Province, China
| | - Yuepeng Han
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, The Innovative Academy of Seed Design of Chinese Academy of Sciences, Wuhan, Hubei Province, China
- Sino-African Joint Research Center, Chinese Academy of Sciences, Wuhan, Hubei Province, China
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2
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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3
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Lu H, Zhang J, Cao Y, Wu S, Wei Y, Yin R. Advances in applications of artificial intelligence algorithms for cancer-related miRNA research. Zhejiang Da Xue Xue Bao Yi Xue Ban 2024; 53:231-243. [PMID: 38650448 PMCID: PMC11057993 DOI: 10.3724/zdxbyxb-2023-0511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/30/2024] [Indexed: 04/25/2024]
Abstract
MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.
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Affiliation(s)
- Hongyu Lu
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| | - Jia Zhang
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Yixin Cao
- Department of Medical Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Shuming Wu
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China
| | - Yuan Wei
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
| | - Runting Yin
- School of Pharmacy, Jiangsu University, Zhenjiang 212013, Jiangsu Province, China.
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4
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Yin R, Zhao H, Li L, Yang Q, Zeng M, Yang C, Bian J, Xie M. Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589599. [PMID: 38659732 PMCID: PMC11042274 DOI: 10.1101/2024.04.15.589599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.
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Affiliation(s)
- Rui Yin
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- These authors contributed equally
| | - Hongru Zhao
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
- These authors contributed equally
| | - Lu Li
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
| | - Qiang Yang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Carl Yang
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Mingyi Xie
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
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5
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Hadad E, Rokach L, Veksler-Lublinsky I. Empowering prediction of miRNA-mRNA interactions in species with limited training data through transfer learning. Heliyon 2024; 10:e28000. [PMID: 38560149 PMCID: PMC10981012 DOI: 10.1016/j.heliyon.2024.e28000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
MicroRNAs (miRNAs) play a crucial role in mRNA regulation. Identifying functionally important mRNA targets of a specific miRNA is essential for uncovering its biological function and assisting miRNA-based drug development. Datasets of high-throughput direct bona fide miRNA-target interactions (MTIs) exist only for a few model organisms, prompting the need for computational prediction. However, the scarcity of data poses a challenge in training accurate machine learning models for MTI prediction. In this study, we explored the potential of transfer learning technique (with ANN and XGB) to address the limited data challenge by leveraging the similarities in interaction rules between species. Furthermore, we introduced a novel approach called TransferSHAP for estimating the feature importance of transfer learning in tabular dataset tasks. We demonstrated that transfer learning improves MTI prediction accuracy for species with limited datasets and identified the specific interaction features the models employed to transfer information across different species.
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Affiliation(s)
- Eyal Hadad
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben-Gurion Blvd. 1, Beer-Sheva 8410501, Israel
| | - Lior Rokach
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben-Gurion Blvd. 1, Beer-Sheva 8410501, Israel
| | - Isana Veksler-Lublinsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, David Ben-Gurion Blvd. 1, Beer-Sheva 8410501, Israel
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6
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Yang TH, Chen JC, Lee YH, Lu SY, Wu SH, Chang FY, Huang YC, Lee MH, Tseng YY, Wu WS. Identifying Human miRNA Target Sites via Learning the Interaction Patterns between miRNA and mRNA Segments. J Chem Inf Model 2024; 64:2445-2453. [PMID: 37903033 DOI: 10.1021/acs.jcim.3c01150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
miRNAs (microRNAs) target specific mRNA (messenger RNA) sites to regulate their translation expression. Although miRNA targeting can rely on seed region base pairing, animal miRNAs, including human miRNAs, typically cooperate with several cofactors, leading to various noncanonical pairing rules. Therefore, identifying the binding sites of animal miRNAs remains challenging. Because experiments for mapping miRNA targets are costly, computational methods are preferred for extracting potential miRNA-mRNA fragment binding pairs first. However, existing prediction tools can have significant false positives due to the prevalent noncanonical miRNA binding behaviors and the information-biased training negative sets that were used while constructing these tools. To overcome these obstacles, we first prepared an information-balanced miRNA binding pair ground-truth data set. A miRNA-mRNA interaction-aware model was then designed to help identify miRNA binding events. On the test set, our model (auROC = 94.4%) outperformed existing models by at least 2.8% in auROC. Furthermore, we showed that this model can suggest potential binding patterns for miRNA-mRNA sequence interacting pairs. Finally, we made the prepared data sets and the designed model available at http://cosbi2.ee.ncku.edu.tw/mirna_binding/download.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
- Medical Device Innovation Center, National Cheng Kung University, No.1 University Road, Tainan 701, Taiwan
| | - Jhih-Cheng Chen
- Department of Electrical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
| | - Yuan-Han Lee
- Department of Electrical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
| | - Shang-Yi Lu
- Department of Electrical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
| | - Sheng-Hang Wu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, Kaohsiung 811, Taiwan
| | - Fang-Yuan Chang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, Kaohsiung 811, Taiwan
| | - Yan-Cheng Huang
- Department of Electrical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
| | - Mei-Hsien Lee
- Department of Mathematics, University of Taipei, No.1, Ai-Guo West Road, Taipei 100234, Taiwan
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, Wayne State University, School of Medicine, Detroit, Michigan 48201, United States
| | - Wei-Sheng Wu
- Department of Electrical Engineering, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan
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7
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Przybyszewski J, Malawski M, Lichołai S. GraphTar: applying word2vec and graph neural networks to miRNA target prediction. BMC Bioinformatics 2023; 24:436. [PMID: 37978418 PMCID: PMC10657114 DOI: 10.1186/s12859-023-05564-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate gene expression by binding to specific mRNAs, inhibiting their translation. They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological, gastrointestinal diseases, and viral infections. Computational methods that can identify potential miRNA-mRNA interactions from raw data use one-dimensional miRNA-mRNA duplex representations and simple sequence encoding techniques, which may limit their performance. RESULTS We have developed GraphTar, a new target prediction method that uses a novel graph-based representation to reflect the spatial structure of the miRNA-mRNA duplex. Unlike existing approaches, we use the word2vec method to accurately encode RNA sequence information. In conjunction with the novel encoding method, we use a graph neural network classifier that can accurately predict miRNA-mRNA interactions based on graph representation learning. As part of a comparative study, we evaluate three different node embedding approaches within the GraphTar framework and compare them with other state-of-the-art target prediction methods. The results show that the proposed method achieves similar performance to the best methods in the field and outperforms them on one of the datasets. CONCLUSIONS In this study, a novel miRNA target prediction approach called GraphTar is introduced. Results show that GraphTar is as effective as existing methods and even outperforms them in some cases, opening new avenues for further research. However, the expansion of available datasets is critical for advancing the field towards real-world applications.
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Affiliation(s)
- Jan Przybyszewski
- Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054, Cracow, Poland.
| | - Maciej Malawski
- Sano Centre for Computational Medicine, Czarnowiejska 36, 30-054, Cracow, Poland
| | - Sabina Lichołai
- Division of Molecular Biology and Clinical Genetics, Faculty of Medicine, Jagiellonian University Medical College, Skawińska 8, 31-066, Cracow, Poland
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8
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Li Z, Gao E, Zhou J, Han W, Xu X, Gao X. Applications of deep learning in understanding gene regulation. CELL REPORTS METHODS 2023; 3:100384. [PMID: 36814848 PMCID: PMC9939384 DOI: 10.1016/j.crmeth.2022.100384] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.
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Affiliation(s)
- Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Elva Gao
- The KAUST School, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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9
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Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets. Funct Integr Genomics 2023; 23:33. [PMID: 36625940 PMCID: PMC9838419 DOI: 10.1007/s10142-022-00947-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023]
Abstract
Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.
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10
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Ajila V, Colley L, Ste-Croix DT, Nissan N, Golshani A, Cober ER, Mimee B, Samanfar B, Green JR. P-TarPmiR accurately predicts plant-specific miRNA targets. Sci Rep 2023; 13:332. [PMID: 36609461 PMCID: PMC9822942 DOI: 10.1038/s41598-022-27283-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.
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Affiliation(s)
- Victoria Ajila
- grid.34428.390000 0004 1936 893XDepartment of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 Canada
| | - Laura Colley
- grid.34428.390000 0004 1936 893XDepartment of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 Canada
| | - Dave T. Ste-Croix
- grid.55614.330000 0001 1302 4958Saint-Jean-sur-Richelieu Research and Development Center, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, J3B 7B5 Canada
| | - Nour Nissan
- grid.55614.330000 0001 1302 4958Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6 Canada ,grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, K1S 5B6 Canada
| | - Ashkan Golshani
- grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, K1S 5B6 Canada
| | - Elroy R. Cober
- grid.55614.330000 0001 1302 4958Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6 Canada
| | - Benjamin Mimee
- grid.55614.330000 0001 1302 4958Saint-Jean-sur-Richelieu Research and Development Center, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, J3B 7B5 Canada
| | - Bahram Samanfar
- grid.55614.330000 0001 1302 4958Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6 Canada ,grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, K1S 5B6 Canada
| | - James R. Green
- grid.34428.390000 0004 1936 893XDepartment of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 Canada
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11
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Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling. BIOLOGY 2022; 11:biology11121798. [PMID: 36552307 PMCID: PMC9775672 DOI: 10.3390/biology11121798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 11/27/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.
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12
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinform 2022; 23:6696143. [PMID: 36094095 DOI: 10.1093/bib/bbac397] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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13
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Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.
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14
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Min S, Lee B, Yoon S. TargetNet: functional microRNA target prediction with deep neural networks. Bioinformatics 2022; 38:671-677. [PMID: 34677573 DOI: 10.1093/bioinformatics/btab733] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 09/13/2021] [Accepted: 10/19/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. RESULTS In this article, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (i) relaxed CTS selection criteria accommodating irregularities in the seed region, (ii) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments and (iii) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-the-art algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. AVAILABILITY AND IMPLEMENTATION The codes and pre-trained models are available at https://github.com/mswzeus/TargetNet.
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Affiliation(s)
- Seonwoo Min
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, South Korea.,LG AI Research, Seoul 07796, South Korea
| | - Byunghan Lee
- Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul 01811, South Korea
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, South Korea.,Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
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15
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Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:133-160. [DOI: 10.1007/978-3-031-08356-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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16
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Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:109-131. [DOI: 10.1007/978-3-031-08356-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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17
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Nachtigall PG, Bovolenta LA. Computational Detection of MicroRNA Targets. Methods Mol Biol 2022; 2257:187-209. [PMID: 34432280 DOI: 10.1007/978-1-0716-1170-8_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA-target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA-target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.
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Affiliation(s)
- Pedro Gabriel Nachtigall
- Laboratório Especial de Toxinologia Aplicada, CeTICS, Instituto Butantan, São Paulo, SP, Brazil.
| | - Luiz Augusto Bovolenta
- Department of Morphology, Institute of Biosciences of Botucatu (IBB), São Paulo State University (UNESP), Botucatu, Brazil
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18
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Kaczmarek E, Pyman B, Nanayakkara J, Tuschl T, Tyryshkin K, Renwick N, Mousavi P. Discriminating Neoplastic from Nonneoplastic Tissues Using an miRNA-Based Deep Cancer Classifier. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 192:344-352. [PMID: 34774515 DOI: 10.1016/j.ajpath.2021.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/07/2021] [Accepted: 10/13/2021] [Indexed: 10/19/2022]
Abstract
Next-generation sequencing has enabled the collection of large biological data sets, allowing novel molecular-based classification methods to be developed for increased understanding of disease. miRNAs are small regulatory RNA molecules that can be quantified using next-generation sequencing and are excellent classificatory markers. Herein, we adapt a deep cancer classifier (DCC) to differentiate neoplastic from nonneoplastic samples using comprehensive miRNA expression profiles from 1031 human breast and skin tissue samples. The classifier was fine-tuned and evaluated using 750 neoplastic and 281 nonneoplastic breast and skin tissue samples. Performance of the DCC was compared with two machine-learning classifiers: support vector machine and random forests. In addition, performance of feature extraction through the DCC was also compared with a developed feature selection algorithm, cancer specificity. The DCC had the highest performance of area under the receiver operating curve and high performance in both sensitivity and specificity, unlike machine-learning and feature selection models, which often performed well in one metric compared with the other. In particular, deep learning was shown to have noticeable advantages with highly heterogeneous data sets. In addition, our cancer specificity algorithm identified candidate biomarkers for differentiating neoplastic and nonneoplastic tissue samples (eg, miR-144 and miR-375 in breast cancer and miR-375 and miR-451 in skin cancer).
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Affiliation(s)
- Emily Kaczmarek
- Medical Informatics Laboratory, School of Computing, Queen's University, Kingston, Ontario, Canada.
| | - Blake Pyman
- Medical Informatics Laboratory, School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Jina Nanayakkara
- Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada
| | - Thomas Tuschl
- Laboratory of RNA Molecular Biology, Rockefeller University, New York, New York
| | - Kathrin Tyryshkin
- Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada
| | - Neil Renwick
- Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada.
| | - Parvin Mousavi
- Medical Informatics Laboratory, School of Computing, Queen's University, Kingston, Ontario, Canada
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19
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Lin JL, Kuo WL, Huang YH, Jong TL, Hsu AL, Hsu WH. Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2724-2732. [PMID: 32031946 DOI: 10.1109/tcbb.2020.2971992] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening and genetic screening studies. The effects of various antiaging interventions on the rate of aging in the early stage of the aging process can be determined based on the quantification of physiological age. However, in general, the age of C. elegans is evaluated via human visual inspection of morphological changes based on personal experience and subjective judgment. For example, the rate of motor activity decay has been used to predict lifespan in early- to mid-stage aging. Using image processing, the physiological age of C. elegans can be measured and then classified into periods or classes from childhood to elderhood (e.g., 3 periods comprising days 0-2, 4-6 and 10-12) by using texture entropy (Shamir, L. et al., 2009). Our dataset consists of 913 microscopic images of C. elegans, with approximately 60 images per day from day 1 to day 14 of adulthood. We present quantitative methods to measure the physiological age of C. elegans with convolution neural networks (CNNs), which can measure age with a granularity of days rather than periods. The methods achieved a mean absolute error (MAE) of less than 1 day for the measured age of C. elegans. In our experiments, we found that after training and testing our dataset, 5 popular CNN models, 50-layer residual network (ResNet50), InceptionV3, InceptionResNetV2, 16-layer Visual Geometry Group network (VGG16) and MobileNet, measured the physiological age of C. elegans with an average testing MAE of 1.58 days. Furthermore, based on the results, we propose two models, one model for linear regression analysis and the other model for logistic regression, that combine a CNN model and a new attribute: curved_or_straight. The linear regression analysis model achieved a test MAE of 0.94 days; the logistic regression model achieved an accuracy of 84.78 percent with an error tolerance of 1 day.
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20
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Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021; 19:5762-5790. [PMID: 34765093 PMCID: PMC8566780 DOI: 10.1016/j.csbj.2021.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.
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Affiliation(s)
- Claudia Caudai
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Antonella Galizia
- CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy
| | - Filippo Geraci
- CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy
| | - Loredana Le Pera
- CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Veronica Morea
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Emanuele Salerno
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Allegra Via
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Teresa Colombo
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
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21
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Li L, Yang Y, Zhang Q, Wang J, Jiang J, Neuroimaging Initiative AD. Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment. Behav Neurol 2021; 2021:3359103. [PMID: 34336000 PMCID: PMC8298161 DOI: 10.1155/2021/3359103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 06/11/2021] [Indexed: 11/26/2022] Open
Abstract
OBJECTIVES Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. METHODS In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. RESULTS We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50%, 98.39% ± 2.50%, and 99.44% ± 1.11% using the DLG model, while 71.38% ± 0.63%, 63.13% ± 2.87%, and 85.59% ± 6.66% using traditional GWAS. Similar results were obtained from the other two intergroup classifications. CONCLUSION The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.
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Affiliation(s)
- Lanlan Li
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Yeying Yang
- LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Qi Zhang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Jiao Wang
- School of Life Science, Shanghai University, Shanghai 200444, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
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22
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Ben Or G, Veksler-Lublinsky I. Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species. BMC Bioinformatics 2021; 22:264. [PMID: 34030625 PMCID: PMC8146624 DOI: 10.1186/s12859-021-04164-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 05/04/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA-target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods. RESULTS We examined the evolution of miRNA-target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA-target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species. CONCLUSIONS The transferability of miRNA-targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA-target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to "non-model" organisms for which minimal experimental data are available. AVAILABILITY AND IMPLEMENTATION The code is freely available at https://github.com/gbenor/TPVOD .
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Affiliation(s)
- Gilad Ben Or
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Isana Veksler-Lublinsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
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23
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Pertseva M, Gao B, Neumeier D, Yermanos A, Reddy ST. Applications of Machine and Deep Learning in Adaptive Immunity. Annu Rev Chem Biomol Eng 2021; 12:39-62. [PMID: 33852352 DOI: 10.1146/annurev-chembioeng-101420-125021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.
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Affiliation(s)
- Margarita Pertseva
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Life Science Zurich Graduate School, ETH Zurich and University of Zurich, 8006 Zurich, Switzerland
| | - Beichen Gao
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Daniel Neumeier
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
| | - Alexander Yermanos
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; .,Department of Pathology and Immunology, University of Geneva, 1205 Geneva, Switzerland.,Department of Biology, Institute of Microbiology and Immunology, ETH Zurich, 8093 Zurich, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
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24
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Gu T, Zhao X, Barbazuk WB, Lee JH. miTAR: a hybrid deep learning-based approach for predicting miRNA targets. BMC Bioinformatics 2021; 22:96. [PMID: 33639834 PMCID: PMC7912887 DOI: 10.1186/s12859-021-04026-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 02/14/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. RESULTS We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. CONCLUSIONS We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR . Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.
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Affiliation(s)
- Tongjun Gu
- Bioinformatics, Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA. .,Division of Quantitative Sciences, University of Florida Health Cancer Center, University of Florida, Gainesville, FL, USA.
| | - Xiwu Zhao
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - William Bradley Barbazuk
- Bioinformatics, Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA.,Department of Biology, University of Florida, Gainesville, FL, USA.,Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Ji-Hyun Lee
- Division of Quantitative Sciences, University of Florida Health Cancer Center, University of Florida, Gainesville, FL, USA.,Department of Biostatistics, University of Florida, Gainesville, FL, USA
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25
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Computational biology and chemistry Special section editorial: Computational analyses for miRNA. Comput Biol Chem 2021; 91:107448. [PMID: 33579616 DOI: 10.1016/j.compbiolchem.2021.107448] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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26
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Tan Q, Guo B, Hu J, Dong X, Hu J. Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2020.08.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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27
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Zhiming C, Daming L, Lianbing D. Risk evaluation of urban rainwater system waterlogging based on neural network and dynamic hydraulic model. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-189045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
With the rapid development of urban construction and the further improvement of the degree of urbanization, despite the intensification of the drainage system construction, the problem of urban waterlogging is still showing an increasingly significant trend. In this paper, the authors analyze the risk evaluation of urban rainwater system waterlogging based on neural network and dynamic hydraulic model. This article introduces the concept of risk into the study of urban waterlogging problems, combines advanced computer simulation methods to simulate different conditions of rainwater systems, and conducts urban waterlogging risk assessment. Because the phenomenon of urban waterlogging is vague, it is affected by a variety of factors and requires comprehensive evaluation. Therefore, the fuzzy comprehensive evaluation method is very suitable for solving the risk evaluation problem of urban waterlogging. In order to improve the scientificity of drainage and waterlogging prevention planning, sponge cities should gradually establish rainwater impact assessment and waterlogging risk evaluation systems, comprehensively evaluate the current capacity of urban drainage and waterlogging prevention facilities and waterlogging risks, draw a map of urban rainwater and waterlogging risks, and determine the risk level. At the same time, delineate drainage and waterlogging prevention zones and risk management zones to provide effective technical support for the formulation of drainage and storm waterlogging prevention plans and emergency management.
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Affiliation(s)
- Cai Zhiming
- Institute of Data Science, City University of Macau, China
| | - Li Daming
- Institute of Data Science, City University of Macau, China
- The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co., Ltd, China
| | - Deng Lianbing
- Zhuhai Da Hengqin Science and Technology Development Co., Ltd, Hengqin New Area, China
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28
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Kyrollos DG, Reid B, Dick K, Green JR. RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction. Sci Rep 2020; 10:11770. [PMID: 32678114 PMCID: PMC7366700 DOI: 10.1038/s41598-020-68251-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/15/2020] [Indexed: 12/16/2022] Open
Abstract
MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA–gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved (\documentclass[12pt]{minimal}
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\begin{document}$$p < 0.001$$\end{document}p<0.001) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA–gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA–gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and 10.5683/SP2/LD8JKJ.
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Affiliation(s)
- Daniel G Kyrollos
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
| | - Bradley Reid
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
| | - Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.,Institute of Data Science, Carleton University, Ottawa, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada. .,Institute of Data Science, Carleton University, Ottawa, Canada.
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Cui J, Shu J. Circulating microRNA trafficking and regulation: computational principles and practice. Brief Bioinform 2020; 21:1313-1326. [PMID: 31504144 PMCID: PMC7412956 DOI: 10.1093/bib/bbz079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 01/18/2023] Open
Abstract
Rapid advances in genomics discovery tools and a growing realization of microRNA's implication in intercellular communication have led to a proliferation of studies of circulating microRNA sorting and regulation across cells and different species. Although sometimes, reaching controversial scientific discoveries and conclusions, these studies have yielded new insights in the functional roles of circulating microRNA and a plethora of analytical methods and tools. Here, we consider this body of work in light of key computational principles underpinning discovery of circulating microRNAs in terms of their sorting and targeting, with the goal of providing practical guidance for applications that is focused on the design and analysis of circulating microRNAs and their context-dependent regulation. We survey a broad range of informatics methods and tools that are available to the researcher, discuss their key features, applications and various unsolved problems and close this review with prospects and broader implication of this field.
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Affiliation(s)
- Juan Cui
- Systems Biology and Biomedical Informatics Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Jiang Shu
- Systems Biology and Biomedical Informatics Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
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30
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Jiang H, Wang J, Li M, Lan W, Wu FX, Pan Y. miRTRS: A Recommendation Algorithm for Predicting miRNA Targets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1032-1041. [PMID: 30281478 DOI: 10.1109/tcbb.2018.2873299] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.
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31
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Jiang H, Yang M, Chen X, Li M, Li Y, Wang J. miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm. IEEE J Biomed Health Inform 2020; 24:3630-3641. [PMID: 32287029 DOI: 10.1109/jbhi.2020.2987034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
microRNAs (miRNAs) are small non-coding RNAs which modulate the stability of gene targets and their rates of translation into proteins at transcriptional level and post-transcriptional level. miRNA dysfunctions can lead to human diseases because of dysregulation of their targets. Correct miRNA target prediction will lead to better understanding of the mechanisms of human diseases and provide hints on curing them. In recent years, computational miRNA target prediction methods have been proposed according to the interaction rules between miRNAs and targets. However, these methods suffer from high false positive rates due to the complicated relationship between miRNAs and their targets. The rapidly growing number of experimentally validated miRNA targets enables predicting miRNA targets with high precision via accurate data analysis. Taking advantage of these known miRNA targets, a novel recommendation system model (miRTMC) for miRNA target prediction is established using a new matrix completion algorithm. In miRTMC, a heterogeneous network is constructed by integrating the miRNA similarity network, the gene similarity network, and the miRNA-gene interaction network. Our assumption is that the latent factors determining whether a gene is the target of miRNA or not are highly correlated, i.e., the adjacency matrix of the heterogeneous network is low-rank, which is then completed by using a nuclear norm regularized linear least squares model under non-negative constraints. Alternating direction method of multipliers (ADMM) is adopted to numerically solve the matrix completion problem. Our results show that miRTMC outperforms the competing methods in terms of various evaluation metrics. Our software package is available at https://github.com/hjiangcsu/miRTMC.
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Liu R, Zhang L, Xu Z, Cui Y. [MiR-665 Promotes the Biological Behavior of Small Cell Lung Cancer by Targeting LLGL1]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2020; 23:223-232. [PMID: 32222154 PMCID: PMC7210082 DOI: 10.3779/j.issn.1009-3419.2020.104.03] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
背景与目的 MicroRNAs(miRNAs)是一种广泛存在于真核生物体中的非编码小分子RNA,尽管一些miRNA在肿瘤中作用已被发现,但是miR-665对小细胞肺癌的中的表达及影响还尚不清楚。本研究旨在分析miR-665对肺癌细胞增殖、周期、侵袭和迁移的影响,探讨miR-665在小细胞肺癌中发挥的作用及其工作机制。 方法 qRT-PCR检测miR-665在肺癌组织和癌旁正常组织中的表达水平;TargetScan预测miR-665的潜在靶基因并用双荧光素酶报告基因实验、qRT-PCR和Western blot进行验证;免疫组化、qRT-PCR和Western blot检测LLGL1在肺癌组织和癌旁正常组织中的表达水平;CCK8法、流式细胞法、Transwell和细胞划痕实验检测miR-665和LLGL1对肺癌细胞NCI-H446、NCI-H1688增殖、侵袭、迁移以及S期细胞比值的影响;构建肺癌裸鼠移植瘤模型并观察miR-665对小鼠肿瘤生长的影响。 结果 miR-665在肺癌组织中的表达水平明显高于癌旁正常组织;miR-665能靶向作用于LLGL1的3’-UTR并抑制其表达;相比于癌旁正常组织,LLGL1在肺癌组织中的表达水平明显降低;抑制miR-665的表达可以抑制肺癌NCI-H446细胞的增殖、S期细胞比值、侵袭和迁移能力,而干扰LLGL1能逆转这种抑制效果;上调miR-665则促进肺癌NCI-H1688的增殖、S期细胞比值、侵袭和迁移能力,但这种促进效果同样被LLGL1的过表达逆转;在肺癌裸鼠移植瘤模型中,抑制miR-665能上调LLGL1蛋白的表达并抑制肿瘤的生长,而上调miR-665的表达则可以产生相反的结果。 结论 miR-665表达水平的变化与肺癌密切相关,miR-665可以通过抑制其靶基因LLGL1的表达促进肺癌细胞的生物学行为,在小细胞肺癌中发挥促癌基因的作用。
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Affiliation(s)
- Rongfeng Liu
- Department of Medical Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Lingling Zhang
- Department of Medical Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Zhihong Xu
- Department of Medical Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Yanzhi Cui
- Department of Medical Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
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Xie W, Luo J, Pan C, Liu Y. SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA-gene associations. Brief Bioinform 2020; 22:2032-2042. [PMID: 32181478 DOI: 10.1093/bib/bbaa022] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 02/10/2020] [Accepted: 02/11/2020] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION MircroRNAs (miRNAs) regulate target genes and are responsible for lethal diseases such as cancers. Accurately recognizing and identifying miRNA and gene pairs could be helpful in deciphering the mechanism by which miRNA affects and regulates the development of cancers. Embedding methods and deep learning methods have shown their excellent performance in traditional classification tasks in many scenarios. But not so many attempts have adapted and merged these two methods into miRNA-gene relationship prediction. Hence, we proposed a novel computational framework. We first generated representational features for miRNAs and genes using both sequence and geometrical information and then leveraged a deep learning method for the associations' prediction. RESULTS We used long short-term memory (LSTM) to predict potential relationships and proved that our method outperformed other state-of-the-art methods. Results showed that our framework SG-LSTM got an area under curve of 0.94 and was superior to other methods. In the case study, we predicted the top 10 miRNA-gene relationships and recommended the top 10 potential genes for hsa-miR-335-5p for SG-LSTM-core. We also tested our model using a larger dataset, from which 14 668 698 miRNA-gene pairs were predicted. The top 10 unknown pairs were also listed. AVAILABILITY Our work can be download in https://github.com/Xshelton/SG_LSTM. CONTACT luojiawei@hnu.edu.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Weidun Xie
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Chu Pan
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Ying Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
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miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator. Sci Rep 2020; 10:1466. [PMID: 32001758 PMCID: PMC6992741 DOI: 10.1038/s41598-020-58336-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 01/15/2020] [Indexed: 12/20/2022] Open
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to predict miRNA-target interactions, but variable results among the different prediction tools have caused confusion for users. To solve this problem, we developed miRgo, an application that integrates many of these tools. To train the prediction model, extreme values and median values from four different data combinations, which were obtained via an energy distribution function, were used to find the most representative dataset. Support vector machines were used to integrate 11 prediction tools, and numerous feature types used in these tools were classified into six categories-binding energy, scoring function, evolution evidence, binding type, sequence property, and structure-to simplify feature selection. In addition, a novel evaluation indicator, the Chu-Hsieh-Liang (CHL) index, was developed to improve the prediction power in positive data for feature selection. miRgo achieved better results than all other prediction tools in evaluation by an independent testing set and by its subset of functionally important genes. The tool is available at http://predictor.nchu.edu.tw/miRgo.
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35
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Chaoming L. Prediction and analysis of sphere motion trajectory based on deep learning algorithm optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-179209] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Liang Chaoming
- Guangzhou Institute of Physical Education, Guangzhou, China
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36
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Xiao Q, Luo J, Dai J. Computational Prediction of Human Disease- Associated circRNAs Based on Manifold Regularization Learning Framework. IEEE J Biomed Health Inform 2019; 23:2661-2669. [DOI: 10.1109/jbhi.2019.2891779] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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37
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Wen M, Cong P, Zhang Z, Lu H, Li T. DeepMirTar: a deep-learning approach for predicting human miRNA targets. Bioinformatics 2019; 34:3781-3787. [PMID: 29868708 DOI: 10.1093/bioinformatics/bty424] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 05/28/2018] [Indexed: 12/22/2022] Open
Abstract
Motivation MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. Results In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. Availability and implementation DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ming Wen
- College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China
| | - Peisheng Cong
- School of Chemical Science and Engineering, Tongji University, Shanghai, People's Republic of China
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China
| | - Tonghua Li
- School of Chemical Science and Engineering, Tongji University, Shanghai, People's Republic of China
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38
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Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in the development of miRNA bioinformatics tools. Brief Bioinform 2019; 20:1836-1852. [PMID: 29982332 PMCID: PMC7414524 DOI: 10.1093/bib/bby054] [Citation(s) in RCA: 319] [Impact Index Per Article: 63.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 05/18/2018] [Indexed: 12/13/2022] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.
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Affiliation(s)
- Liang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Liisa Heikkinen
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Changliang Wang
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Yang Yang
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Huiyan Sun
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Garry Wong
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
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39
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Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 2019; 20:389-403. [PMID: 30971806 DOI: 10.1038/s41576-019-0122-6] [Citation(s) in RCA: 507] [Impact Index Per Article: 101.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
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Affiliation(s)
- Gökcen Eraslan
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.,School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Žiga Avsec
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Julien Gagneur
- Department of Informatics, Technical University of Munich, Garching, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany. .,School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany. .,Department of Mathematics, Technical University of Munich, Garching, Germany.
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40
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Wang JY, Cheng H, Zhang HY, Ye YQ, Feng Q, Chen ZM, Zheng YL, Wu ZG, Wang B, Yao J. Suppressing microRNA-29c promotes biliary atresia-related fibrosis by targeting DNMT3A and DNMT3B. Cell Mol Biol Lett 2019; 24:10. [PMID: 30906331 PMCID: PMC6410490 DOI: 10.1186/s11658-018-0134-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 12/18/2018] [Indexed: 12/15/2022] Open
Abstract
This study was designed to investigate the potential role of microRNA-29c (miR-29c) in biliary atresia-related fibrosis. The expression of miR-29c was determined in 15 pairs of peripheral blood samples from infants with biliary atresia (BA) and infants with non-BA neonatal cholestasis using quantitative real-time PCR. EMT was established by induction with TGF-β1 in HIBEpiC cells. MiR-29c was inhibited by lipofectamine transfection. The expressions of proteins related to epithelial-mesenchymal transition (EMT), i.e., E-cadherin, N-cadherin and vimentin, were determined using quantitative real-time PCR and western blotting. Direct interaction between miR-29c and DNMT3A and DNMT3B was identified using a luciferase reporter assay. The expressions of DNMT3A and DNMT3B were suppressed by treatment with SGI-1027. Patients with BA showed significantly lower miR-29c levels in peripheral blood samples than the control subjects. In vitro, TGF-β1-induced EMT significantly decreased the expression of miR-29c. Downregulation of miR-29c had a promotional effect on BA-related fibrosis in HIBEpiC cells, as confirmed by the decrease in E-cadherin and increase in N-cadherin and vimentin levels. MiR-29c was found to target the 3'UTR of DNMT3A and DNMT3B and inhibit their expression. Suppression of DNMT3A and DNMT3B reversed the effects of miR-29c downregulation on BA-related fibrosis in HIBEpiC cells. These data suggest that BA-related fibrosis is closely associated with the occurrence of EMT in HIBEpiC cells. MiR-29c might be a candidate for alleviating BA-related fibrosis by targeting DNMT3A and DNMT3B.
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Affiliation(s)
- Jian-yao Wang
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Hao Cheng
- Graduate School of China Medical University, Shenzhen, 110122 Liaoning Province China
| | - Hong-yan Zhang
- Graduate School of China Medical University, Shenzhen, 110122 Liaoning Province China
| | - Yong-qin Ye
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Qi Feng
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Zi-min Chen
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Yue-lan Zheng
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Zhou-guang Wu
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Bin Wang
- Department of General Surgery, Shenzhen Children’s Hospital, Shenzhen, 518026 Guangdong Province China
| | - Jun Yao
- Department of Gastroenterology, Jinan University of Medical Sciences, Shenzhen Municipal People’s Hospital, Shenzhen, 518020 Guangdong Province China
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Abstract
During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.
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42
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Shu X, Zang X, Liu X, Yang J, Wang J. Predicting MicroRNA Mediated Gene Regulation between Human and Viruses. Cells 2018; 7:cells7080100. [PMID: 30096814 PMCID: PMC6115789 DOI: 10.3390/cells7080100] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/02/2018] [Accepted: 08/06/2018] [Indexed: 01/22/2023] Open
Abstract
MicroRNAs (miRNAs) mediate various biological processes by actively fine-tuning gene expression at the post-transcriptional level. With the identification of numerous human and viral miRNAs, growing evidence has indicated a common role of miRNAs in mediating the interactions between humans and viruses. However, there is only limited information about Cross-Kingdom miRNA target sites from studies. To facilitate an extensive investigation on the interplay among the gene regulatory networks of humans and viruses, we designed a prediction pipeline, mirTarP, that is suitable for miRNA target screening on the genome scale. By applying mirTarP, we constructed the database mirTar, which is a comprehensive miRNA target repository of bidirectional interspecies regulation between viruses and humans. To provide convenient downloading for users from both the molecular biology field and medical field, mirTar classifies viruses according to “ICTV viral category” and the “medical microbiology classification” on the web page. The mirTar database and mirTarP tool are freely available online.
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Affiliation(s)
- Xin Shu
- The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210023, China.
| | - Xinyuan Zang
- The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210023, China.
| | - Xiaoshuang Liu
- The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210023, China.
| | - Jie Yang
- The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210023, China.
| | - Jin Wang
- The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210023, China.
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Liu Y, Luo J, Ding P. Inferring MicroRNA Targets Based on Restricted Boltzmann Machines. IEEE J Biomed Health Inform 2018; 23:427-436. [PMID: 29993787 DOI: 10.1109/jbhi.2018.2814609] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Predicting the miRNA-target interactions (MTIs) is a critical task for elucidating mechanistic roles of miRNAs in pathophysiology. However, most existing techniques have a higher false positive because the precise miRNA target mechanisms are poorly known. Considering that ensemble methods can take advantage of the complementary knowledge in different methods, we propose an alternative optimization framework, Inferring MiRNA Targets based on Restricted Boltzmann Machines (IMTRBM), to enhance the accuracy of previous prediction results. First, the proposed method directly constructs a weighted MTI network though the results predicted by individual methods and each miRNA target pair is weighted based on the frequency appearing in these results. Second, we transform the miRNA-target prediction problem into a complete bipartite graph model, named restricted Boltzmann machine, and utilize a practical learning procedure to train our model and make predictions. Our results show that the algorithm outperforms individual miRNA-target prediction approach in the number of validated miRNA targets at cutoffs of top list. Moreover, our framework can tolerate the decrease and increase of predicted MTIs and even discover new miRNA targets, which have been a challenge to predict for any individual methods. Finally, for the miRNAs that are not appearing in IMTRBM, we design a new method to supplement IMTRBM based on the intuition that similar miRNAs have similar functions, which also achieves a comparable result. The source code of IMTRBM is available at https://github.com/liuying201705/IMTRBM.
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miRNAtools: Advanced Training Using the miRNA Web of Knowledge. Noncoding RNA 2018; 4:ncrna4010005. [PMID: 29657302 PMCID: PMC5890392 DOI: 10.3390/ncrna4010005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 02/13/2018] [Accepted: 02/14/2018] [Indexed: 01/06/2023] Open
Abstract
Micro-RNAs (miRNAs) are small non-coding RNAs that act as negative regulators of the genomic output. Their intrinsic importance within cell biology and human disease is well known. Their mechanism of action based on the base pairing binding to their cognate targets have helped the development not only of many computer applications for the prediction of miRNA target recognition but also of specific applications for functional assessment and analysis. Learning about miRNA function requires practical training in the use of specific computer and web-based applications that are complementary to wet-lab studies. In order to guide the learning process about miRNAs, we have created miRNAtools (http://mirnatools.eu), a web repository of miRNA tools and tutorials. This article compiles tools with which miRNAs and their regulatory action can be analyzed and that function to collect and organize information dispersed on the web. The miRNAtools website contains a collection of tutorials that can be used by students and tutors engaged in advanced training courses. The tutorials engage in analyses of the functions of selected miRNAs, starting with their nomenclature and genomic localization and finishing with their involvement in specific cellular functions.
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Gene Prediction in Metagenomic Fragments with Deep Learning. BIOMED RESEARCH INTERNATIONAL 2017; 2017:4740354. [PMID: 29250541 PMCID: PMC5698827 DOI: 10.1155/2017/4740354] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 10/08/2017] [Indexed: 01/14/2023]
Abstract
Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.
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Jiang X, Zhang H, Duan F, Quan X. Identify Huntington's disease associated genes based on restricted Boltzmann machine with RNA-seq data. BMC Bioinformatics 2017; 18:447. [PMID: 29020921 PMCID: PMC5637347 DOI: 10.1186/s12859-017-1859-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 10/02/2017] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression. RESULTS In this paper, we propose a deep learning approach based on the restricted Boltzmann machine to analyze the RNA-seq data of Huntington's disease, namely stacked restricted Boltzmann machine (SRBM). According to the SRBM, we also design a novel framework to screen the key genes during the Huntington's disease development. In this work, we assume that the effects of regulatory factors can be captured by the hierarchical structure and narrow hidden layers of the SRBM. First, we select disease-associated factors with different time period datasets according to the differentially activated neurons in hidden layers. Then, we select disease-associated genes according to the changes of the gene energy in SRBM at different time periods. CONCLUSIONS The experimental results demonstrate that SRBM can detect the important information for differential analysis of time series gene expression datasets. The identification accuracy of the disease-associated genes is improved to some extent using the novel framework. Moreover, the prediction precision of disease-associated genes for top ranking genes using SRBM is effectively improved compared with that of the state of the art methods.
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Affiliation(s)
- Xue Jiang
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China
| | - Han Zhang
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China
| | - Feng Duan
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China.,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China
| | - Xiongwen Quan
- College of Computer and Control Engineering, Nankai University, Tongyan Road, Tianjin, 300350, China. .,Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tongyan Road, Tianjin, 300350, China.
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A Review of Computational Methods for Finding Non-Coding RNA Genes. Genes (Basel) 2016; 7:genes7120113. [PMID: 27918472 PMCID: PMC5192489 DOI: 10.3390/genes7120113] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 11/04/2016] [Accepted: 11/17/2016] [Indexed: 12/19/2022] Open
Abstract
Finding non-coding RNA (ncRNA) genes has emerged over the past few years as a cutting-edge trend in bioinformatics. There are numerous computational intelligence (CI) challenges in the annotation and interpretation of ncRNAs because it requires a domain-related expert knowledge in CI techniques. Moreover, there are many classes predicted yet not experimentally verified by researchers. Recently, researchers have applied many CI methods to predict the classes of ncRNAs. However, the diverse CI approaches lack a definitive classification framework to take advantage of past studies. A few review papers have attempted to summarize CI approaches, but focused on the particular methodological viewpoints. Accordingly, in this article, we summarize in greater detail than previously available, the CI techniques for finding ncRNAs genes. We differentiate from the existing bodies of research and discuss concisely the technical merits of various techniques. Lastly, we review the limitations of ncRNA gene-finding CI methods with a point-of-view towards the development of new computational tools.
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Pastur-Romay LA, Cedrón F, Pazos A, Porto-Pazos AB. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications. Int J Mol Sci 2016; 17:E1313. [PMID: 27529225 PMCID: PMC5000710 DOI: 10.3390/ijms17081313] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 07/14/2016] [Accepted: 07/25/2016] [Indexed: 12/20/2022] Open
Abstract
Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.
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Affiliation(s)
- Lucas Antón Pastur-Romay
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
| | - Francisco Cedrón
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
| | - Alejandro Pazos
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
- Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain.
| | - Ana Belén Porto-Pazos
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
- Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain.
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