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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024; 16:243-260. [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] [MESH Headings] [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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Pukhta IR, Rout RK. Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38424698 DOI: 10.1080/10255842.2024.2311322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 01/20/2024] [Indexed: 03/02/2024]
Abstract
Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.
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Affiliation(s)
- Irfan Rashid Pukhta
- Assistant Professor, Department of Computer Science and Engineering National Institute of Technology, Srinagar, Jammu and Kashmir 190006, India
| | - Ranjeet Kumar Rout
- Assistant Professor, Department of Computer Science and Engineering National Institute of Technology, Srinagar, Jammu and Kashmir 190006, India
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3
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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: 3] [Impact Index Per Article: 1.5] [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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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. 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: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, 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, 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.
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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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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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MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction. Acta Pharm Sin B 2021; 11:3092-3104. [PMID: 34729303 PMCID: PMC8546929 DOI: 10.1016/j.apsb.2021.05.032] [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: 01/12/2021] [Revised: 03/12/2021] [Accepted: 05/06/2021] [Indexed: 02/05/2023] Open
Abstract
Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/.
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Key Words
- Data mining
- Database
- GO, Gene Ontology
- IUPAC, International Union of Pure and Applied Chemistry
- InChI Key, International Chemical Identifier hash
- InChI, International Chemical Identifier
- MC, Mitotic Catastrophe
- MCDB, Mitotic Catastrophe Database
- Mitotic catastrophe
- PDB, Protein Data Bank
- PMID, PubMed identifier
- Protein sequence analysis
- PubChem, Public Chemistry
- PubMed, Public Medicine
- SMILES, Simplified Molecular Input Line Entry Specification
- Target prediction
- UniProt, Universal Protein Resource
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Purohit S, Satapathy SC, Sibi Chakkaravarthy S, Zhang YD. Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 48:1-13. [PMID: 34189012 PMCID: PMC8221988 DOI: 10.1007/s13369-021-05811-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 06/02/2021] [Indexed: 11/29/2022]
Abstract
Virus attacks have had devastating effects on mankind. The prominent viruses such as Ebola virus (2012), SARS-CoV or Severe acute respiratory syndrome, Middle East respiratory syndrome-related coronavirus called as the MERS (EMC/2012), Spanish flu (H1N1 virus-1918) and the most recent COVID-19(SARS-CoV-2) are the ones that have created a difficult situation for the survival of the human race. Currently, throughout the world, a global pandemic situation has put economy, livelihood and human existence in a very pathetic situation. Most of the above-mentioned viruses exhibit some similar characteristics and genetic pattern. Analysing such characteristics and genetic pattern can help the researchers to get a deeper insight into the viruses and helps in finding appropriate medicine or cure. To address these issues, this paper proposes an experimental analysis of the above-mentioned viruses data using correlation methods. The virus data considered for the experimental analysis include the distribution of various amino acids, protein sequences, 3D modelling of viruses, pairwise alignment of proteins that comprise the DNA genome of the viruses. Furthermore, this comparative analysis can be used by the researchers and organizations like WHO(World Health Organization), computational biologists, genetic engineers to frame a layout for studying the DNA sequence distribution, percentage of GC (guanine-cytosine) protein which determines the heat stability of viruses. We have used the Biopython to illustrate the gene study of prominent viruses and have derived results and insights in the form of 3D modelling. The experimental results are more promising with an accuracy rate of 96% in overall virus relationship calculation.
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Affiliation(s)
| | | | - S Sibi Chakkaravarthy
- Artificial Intelligence and Robotics (AIR) Research Center and School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, India
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, UK
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