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Zhang L, Song W, Zhu T, Liu Y, Chen W, Cao Y. ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model. Brief Bioinform 2024; 25:bbae133. [PMID: 38561979 PMCID: PMC10985285 DOI: 10.1093/bib/bbae133] [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: 11/14/2023] [Revised: 02/11/2024] [Accepted: 03/02/2024] [Indexed: 04/04/2024] Open
Abstract
Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Tinghao Zhu
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Nuclear Power Institute of China, Chengdu 610213, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, No. 29 Wangjiang Road, Chengdu 610065, China
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, No. 29 Wangjiang Road, Chengdu 610065, China
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2
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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024:10.1007/s12539-024-00603-4. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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3
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Zhang L, Badai J, Wang G, Ru X, Song W, You Y, He J, Huang S, Feng H, Chen R, Zhao Y, Chen Y. Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis. Front Immunol 2023; 14:1123652. [PMID: 36825001 PMCID: PMC9941151 DOI: 10.3389/fimmu.2023.1123652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/11/2023] [Indexed: 02/09/2023] Open
Abstract
Introduction Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities. Methods Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Results Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation. Discussion These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China,Innovation Center of Nursing Research, West China Hospital, Sichuan University, Chengdu, China
| | - Jiayidaer Badai
- College of Computer Science, Sichuan University, Chengdu, China
| | - Guan Wang
- College of Computer Science, Sichuan University, Chengdu, China,Innovation Center of Nursing Research, West China Hospital, Sichuan University, Chengdu, China
| | - Xufang Ru
- Chinese Academy of Sciences (CAS) Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China,Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jiaojiao He
- College of Computer Science, Sichuan University, Chengdu, China
| | - Suna Huang
- Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China
| | - Hua Feng
- Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China
| | - Runsheng Chen
- College of Computer Science, Sichuan University, Chengdu, China,Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Runsheng Chen, ; Yi Zhao, ; Yujie Chen, ;
| | - Yi Zhao
- College of Computer Science, Sichuan University, Chengdu, China,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China,Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,*Correspondence: Runsheng Chen, ; Yi Zhao, ; Yujie Chen, ;
| | - Yujie Chen
- Chinese Academy of Sciences (CAS) Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China,Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Army Medical University, Chongqing, China,*Correspondence: Runsheng Chen, ; Yi Zhao, ; Yujie Chen, ;
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4
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You Y, Zhang L, Tao P, Liu S, Chen L. Spatiotemporal Transformer Neural Network for Time-Series Forecasting. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1651. [PMID: 36421506 PMCID: PMC9689721 DOI: 10.3390/e24111651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/05/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Peng Tao
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Luonan Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China
- West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, Chengdu 610041, China
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Ma F, Xiao M, Zhu L, Jiang W, Jiang J, Zhang PF, Li K, Yue M, Zhang L. An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection. Front Genet 2022; 13:981633. [PMID: 36186430 PMCID: PMC9516312 DOI: 10.3389/fgene.2022.981633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/30/2022] [Indexed: 11/20/2022] Open
Abstract
Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions. Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.
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Affiliation(s)
- Fubo Ma
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Lin Zhu
- China Animal Health and Epidemiology Center, Qingdao, Shandong, China
| | - Wen Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Jizhe Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Peng-Fei Zhang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Min Yue
- Hainan Institute of Zhejiang University, Sanya, China
- *Correspondence: Le Zhang, ; Min Yue,
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Le Zhang, ; Min Yue,
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6
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Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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7
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ASTM: Developing the web service for anthrax related spatiotemporal characteristics and meteorology study. QUANTITATIVE BIOLOGY 2022. [DOI: 10.15302/j-qb-022-0288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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8
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Liu S, You Y, Tong Z, Zhang L. Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research. Front Genet 2021; 12:761629. [PMID: 34764986 PMCID: PMC8576451 DOI: 10.3389/fgene.2021.761629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.
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Affiliation(s)
- Suran Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhaoqi Tong
- College of Software Engineering, Sichuan University, Chengdu, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
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9
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Xiao M, Liu G, Xie J, Dai Z, Wei Z, Ren Z, Yu J, Zhang L. 2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1250-1261. [PMID: 33406042 PMCID: PMC8769043 DOI: 10.1109/tcbb.2021.3049617] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/02/2020] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
Since the COVID-19 epidemic is still expanding around the world and poses a serious threat to human life and health, it is necessary for us to carry out epidemic transmission prediction, whole genome sequence analysis, and public psychological stress assessment for 2019-nCoV. However, transmission prediction models are insufficiently accurate and genome sequence characteristics are not clear, and it is difficult to dynamically assess the public psychological stress state under the 2019-nCoV epidemic. Therefore, this study develops a 2019nCoVAS web service (http://www.combio-lezhang.online/2019ncov/home.html) that not only offers online epidemic transmission prediction and lineage-associated underrepresented permutation (LAUP) analysis services to investigate the spreading trends and genome sequence characteristics, but also provides psychological stress assessments based on such an emotional dictionary that we built for 2019-nCoV. Finally, we discuss the shortcomings and further study of the 2019nCoVAS web service.
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Affiliation(s)
- Ming Xiao
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Guangdi Liu
- College of Computer and Information ScienceSouthwest UniversityChong-Qing400715PR China
| | - Jianghang Xie
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Zichun Dai
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Zihao Wei
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Ziyao Ren
- College of Computer ScienceSichuan UniversityChengdu610065PR China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of Genomics, Chinese Academy of SciencesBeijing100101PR China
| | - Le Zhang
- College of Computer ScienceSichuan UniversityChengdu610065PR China
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10
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Xiao M, Yang X, Yu J, Zhang L. CGIDLA:Developing the Web Server for CpG Island Related Density and LAUPs (Lineage-Associated Underrepresented Permutations) Study. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2148-2154. [PMID: 31443042 DOI: 10.1109/tcbb.2019.2935971] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
It is well known that CpG island plays an important role in gene methylation. Since CpG island is closely related to human genetic characteristics such as TATA-box, tissue expression specificity, and LAUPs (Lineage-associated Underrepresented Permutations), it is important to investigate the sequence specificity of CpG island as well as the potential genetic characteristics related to CpG island to further understand the methylation related regulation mechanism. Therefore, this study develops such an online service website for CpG island related density and LAUPs analysis (CGIDLA, www.combio-lezhang.online/cgidla/index.html), that not only can investigate the relationship among the CpG island density, TATA-box feature, and expression breadth of human genes, but also deposit LAUPs of 32 representative species to help molecular biologists investigate the relationship between CpG island and LUAPs. Moreover, CGIDLA provides the source code download service and the related LAUPs counting functions.
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You Y, Ru X, Lei W, Li T, Xiao M, Zheng H, Chen Y, Zhang L. Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme. BMC Bioinformatics 2020; 21:383. [PMID: 32938364 PMCID: PMC7646399 DOI: 10.1186/s12859-020-03674-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma. CONCLUSIONS We summarize the whole process of the experiment and discuss how to expand our experiment in the future.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xufang Ru
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, P.R. China
| | - Wanjing Lei
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Tingting Li
- College of Mathematics and Statistics, Southwest University, Chongqing, 400715, P.R. China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Huiru Zheng
- School of Computing, Ulster University, Coleraine, Londonderry, Northern Ireland, UK
| | - Yujie Chen
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, P.R. China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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12
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Lei W, Zeng H, Feng H, Ru X, Li Q, Xiao M, Zheng H, Chen Y, Zhang L. Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis. Front Genet 2020; 11:391. [PMID: 32373167 PMCID: PMC7186496 DOI: 10.3389/fgene.2020.00391] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 03/30/2020] [Indexed: 01/15/2023] Open
Abstract
Subarachnoid hemorrhage (SAH) is devastating disease with high mortality, high disability rate, and poor clinical prognosis. It has drawn great attentions in both basic and clinical medicine. Therefore, it is necessary to explore the therapeutic drugs and effective targets for early prediction of SAH. Firstly, we demonstrate that LCN2 can effectively intervene or treat SAH from the perspective of cell signaling pathway. Next, three potential genes that we explored have been validated by manually reviewed experimental evidences. Finally, we turn out that the SAH early ensemble learning predictive model performs better than the classical LR, SVM, and Naïve-Bayes models.
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Affiliation(s)
- Wanjing Lei
- College of Computer Science, Sichuan University, Chengdu, China
| | - Han Zeng
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Hua Feng
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Xufang Ru
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Qiang Li
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Huiru Zheng
- School of Computing, Ulster University, Coleraine, United Kingdom
| | - Yujie Chen
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, China
- State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
- College of Computer and Information Science, Southwest University, Chongqing, China
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Zhao J, Cao Y, Zhang L. Exploring the computational methods for protein-ligand binding site prediction. Comput Struct Biotechnol J 2020; 18:417-426. [PMID: 32140203 PMCID: PMC7049599 DOI: 10.1016/j.csbj.2020.02.008] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/23/2020] [Accepted: 02/11/2020] [Indexed: 12/21/2022] Open
Abstract
Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.
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Affiliation(s)
- Jingtian Zhao
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
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