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Kadlof M, Banecki K, Chiliński M, Plewczynski D. Chromatin image-driven modelling. Methods 2024; 226:54-60. [PMID: 38636797 DOI: 10.1016/j.ymeth.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 03/13/2024] [Accepted: 04/05/2024] [Indexed: 04/20/2024] Open
Abstract
The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modelling. Additionally, we introduce the initial structure generator, a tool designed to generate optimal starting structures for the proposed algorithms. The methods are versatile and can be applied to various data types, with minor modifications to accommodate new generation imaging techniques.
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Affiliation(s)
- Michał Kadlof
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
| | - Krzysztof Banecki
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland; Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Mateusz Chiliński
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland; Centre of New Technologies, University of Warsaw, Warsaw, Poland; Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Dariusz Plewczynski
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland; Centre of New Technologies, University of Warsaw, Warsaw, Poland
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2
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Zhu H, Liu T, Wang Z. scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data. Brief Bioinform 2023:7193585. [PMID: 37302805 PMCID: PMC10359091 DOI: 10.1093/bib/bbad223] [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: 01/09/2023] [Revised: 05/10/2023] [Accepted: 05/23/2023] [Indexed: 06/13/2023] Open
Abstract
Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.
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Affiliation(s)
- Hao Zhu
- Department of Computer Science, University of Miami, 330M Ungar Building, 1365 Memorial Drive, Coral Gables, 33124-4245, FL, USA
| | - Tong Liu
- Department of Computer Science, University of Miami, 330M Ungar Building, 1365 Memorial Drive, Coral Gables, 33124-4245, FL, USA
| | - Zheng Wang
- Department of Computer Science, University of Miami, 330M Ungar Building, 1365 Memorial Drive, Coral Gables, 33124-4245, FL, USA
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Zhao C, Liu T, Wang Z. Functional Similarities of Protein-Coding Genes in Topologically Associating Domains and Spatially-Proximate Genomic Regions. Genes (Basel) 2022; 13:genes13030480. [PMID: 35328034 PMCID: PMC8951421 DOI: 10.3390/genes13030480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 02/26/2022] [Accepted: 03/05/2022] [Indexed: 02/01/2023] Open
Abstract
Topologically associating domains (TADs) are the structural and functional units of the genome. However, the functions of protein-coding genes existing in the same or different TADs have not been fully investigated. We compared the functional similarities of protein-coding genes existing in the same TAD and between different TADs, and also in the same gap region (the region between two consecutive TADs) and between different gap regions. We found that the protein-coding genes from the same TAD or gap region are more likely to share similar protein functions, and this trend is more obvious with TADs than the gap regions. We further created two types of gene–gene spatial interaction networks: the first type is based on Hi-C contacts, whereas the second type is based on both Hi-C contacts and the relationship of being in the same TAD. A graph auto-encoder was applied to learn the network topology, reconstruct the two types of networks, and predict the functions of the central genes/nodes based on the functions of the neighboring genes/nodes. It was found that better performance was achieved with the second type of network. Furthermore, we detected long-range spatially-interactive regions based on Hi-C contacts and calculated the functional similarities of the gene pairs from these regions.
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Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data. PLoS Comput Biol 2021; 17:e1009546. [PMID: 34793453 PMCID: PMC8601426 DOI: 10.1371/journal.pcbi.1009546] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/08/2021] [Indexed: 11/19/2022] Open
Abstract
Construction of chromosomes 3D models based on single cell Hi-C data constitute an important challenge. We present a reconstruction approach, DPDchrom, that incorporates basic knowledge whether the reconstructed conformation should be coil-like or globular and spring relaxation at contact sites. In contrast to previously published protocols, DPDchrom can naturally form globular conformation due to the presence of explicit solvent. Benchmarking of this and several other methods on artificial polymer models reveals similar reconstruction accuracy at high contact density and DPDchrom advantage at low contact density. To compare 3D structures insensitively to spatial orientation and scale, we propose the Modified Jaccard Index. We analyzed two sources of the contact dropout: contact radius change and random contact sampling. We found that the reconstruction accuracy exponentially depends on the number of contacts per genomic bin allowing to estimate the reconstruction accuracy in advance. We applied DPDchrom to model chromosome configurations based on single-cell Hi-C data of mouse oocytes and found that these configurations differ significantly from a random one, that is consistent with other studies.
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Zha M, Wang N, Zhang C, Wang Z. Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential. Int J Mol Sci 2021; 22:ijms22115914. [PMID: 34072879 PMCID: PMC8199262 DOI: 10.3390/ijms22115914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/23/2021] [Accepted: 05/28/2021] [Indexed: 11/16/2022] Open
Abstract
Reconstructing three-dimensional (3D) chromosomal structures based on single-cell Hi-C data is a challenging scientific problem due to the extreme sparseness of the single-cell Hi-C data. In this research, we used the Lennard-Jones potential to reconstruct both 500 kb and high-resolution 50 kb chromosomal structures based on single-cell Hi-C data. A chromosome was represented by a string of 500 kb or 50 kb DNA beads and put into a 3D cubic lattice for simulations. A 2D Gaussian function was used to impute the sparse single-cell Hi-C contact matrices. We designed a novel loss function based on the Lennard-Jones potential, in which the ε value, i.e., the well depth, was used to indicate how stable the binding of every pair of beads is. For the bead pairs that have single-cell Hi-C contacts and their neighboring bead pairs, the loss function assigns them stronger binding stability. The Metropolis-Hastings algorithm was used to try different locations for the DNA beads, and simulated annealing was used to optimize the loss function. We proved the correctness and validness of the reconstructed 3D structures by evaluating the models according to multiple criteria and comparing the models with 3D-FISH data.
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Affiliation(s)
- Mengsheng Zha
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Dr, Hattiesburg, MS 39406, USA; (M.Z.); (C.Z.)
| | - Nan Wang
- Department of Computer Science, New Jersey City University, 2039 Kennedy Blvd, Jersey City, NJ 07305, USA;
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Dr, Hattiesburg, MS 39406, USA; (M.Z.); (C.Z.)
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1364 Memorial Drive, Coral Gables, FL 33124, USA
- Correspondence:
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Kundu S, Ray MD, Sharma A. Interplay between genome organization and epigenomic alterations of pericentromeric DNA in cancer. J Genet Genomics 2021; 48:184-197. [PMID: 33840602 DOI: 10.1016/j.jgg.2021.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 02/07/2021] [Accepted: 02/20/2021] [Indexed: 12/16/2022]
Abstract
In eukaryotic genome biology, the genomic organization inside the three-dimensional (3D) nucleus is highly complex, and whether this organization governs gene expression is poorly understood. Nuclear lamina (NL) is a filamentous meshwork of proteins present at the lining of inner nuclear membrane that serves as an anchoring platform for genome organization. Large chromatin domains termed as lamina-associated domains (LADs), play a major role in silencing genes at the nuclear periphery. The interaction of the NL and genome is dynamic and stochastic. Furthermore, many genes change their positions during developmental processes or under disease conditions such as cancer, to activate certain sorts of genes and/or silence others. Pericentromeric heterochromatin (PCH) is mostly in the silenced region within the genome, which localizes at the nuclear periphery. Studies show that several genes located at the PCH are aberrantly expressed in cancer. The interesting question is that despite being localized in the pericentromeric region, how these genes still manage to overcome pericentromeric repression. Although epigenetic mechanisms control the expression of the pericentromeric region, recent studies about genome organization and genome-nuclear lamina interaction have shed light on a new aspect of pericentromeric gene regulation through a complex and coordinated interplay between epigenomic remodeling and genomic organization in cancer.
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Affiliation(s)
- Subhadip Kundu
- Laboratory of Chromatin and Cancer Epigenetics, Department of Biochemistry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - M D Ray
- Department of Surgical Oncology, IRCH, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - Ashok Sharma
- Laboratory of Chromatin and Cancer Epigenetics, Department of Biochemistry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.
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Meluzzi D, Arya G. Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data. Methods 2020; 181-182:24-34. [PMID: 31470090 PMCID: PMC7044057 DOI: 10.1016/j.ymeth.2019.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/24/2019] [Accepted: 08/23/2019] [Indexed: 02/08/2023] Open
Abstract
Chromosome conformation capture (3C) and its variants are powerful experimental techniques for probing intra- and inter-chromosomal interactions within cell nuclei at high resolution and in a high-throughput, quantitative manner. The contact maps derived from such experiments provide an avenue for inferring the 3D spatial organization of the genome. This review provides an overview of the various computational methods developed in the past decade for addressing the very important but challenging problem of deducing the detailed 3D structure or structure population of chromosomal domains, chromosomes, and even entire genomes from 3C contact maps.
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Affiliation(s)
- Dario Meluzzi
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
| | - Gaurav Arya
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, United States.
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Halder AK, Denkiewicz M, Sengupta K, Basu S, Plewczynski D. Aggregated network centrality shows non-random structure of genomic and proteomic networks. Methods 2020; 181-182:5-14. [PMID: 31740366 DOI: 10.1016/j.ymeth.2019.11.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 11/02/2019] [Accepted: 11/08/2019] [Indexed: 11/25/2022] Open
Abstract
Network analysis is a powerful tool for modelling biological systems. We propose a new approach that integrates the genomic interaction data at population level with the proteomic interaction data. In our approach we use chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data from human genome to construct a set of genomic interaction networks, considering the natural partitioning of chromatin into chromatin contact domains (CCD). The genomic networks are then mapped onto proteomic interactions, to create protein-protein interaction (PPI) subnetworks. Furthermore, the network-based topological properties of these proteomic subnetworks are investigated, namely closeness centrality, betweenness centrality and clustering coefficient. We statistically confirm, that networks identified by our method significantly differ from random networks in these network properties. Additionally, we identify one of the regions, namely chr6:32014923-33217929, as having an above-random concentration of the single nucleotide polymorphisms (SNPs) related to autoimmune diseases. Then we present it in the form of a meta-network, which includes multi-omic data: genomic contact sites (anchors), genes, proteins and SNPs. Using this example we demonstrate, that the created networks provide a valid mapping of genes to SNPs, expanding on the raw SNP dataset used.
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Affiliation(s)
- Anup Kumar Halder
- Centre of New Technologies, University of Warsaw, Warsaw, Poland; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
| | - Michał Denkiewicz
- Centre of New Technologies, University of Warsaw, Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Kaustav Sengupta
- Centre of New Technologies, University of Warsaw, Warsaw, Poland; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
| | - Dariusz Plewczynski
- Centre of New Technologies, University of Warsaw, Warsaw, Poland; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland; Computer Science Department, University of California, 2063 Kemper Hall, One Shields Avenue, Davis, CA 95616-8562, United States.
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Abstract
BACKGROUND The genome architecture mapping (GAM) technique can capture genome-wide chromatin interactions. However, besides the known systematic biases in the raw GAM data, we have found a new type of systematic bias. It is necessary to develop and evaluate effective normalization methods to remove all systematic biases in the raw GAM data. RESULTS We have detected a new type of systematic bias, the fragment length bias, in the genome architecture mapping (GAM) data, which is significantly different from the bias of window detection frequency previously mentioned in the paper introducing the GAM method but is similar to the bias of distances between restriction sites existing in raw Hi-C data. We have found that the normalization method (a normalized variant of the linkage disequilibrium) used in the GAM paper is not able to effectively eliminate the new fragment length bias at 1 Mb resolution (slightly better at 30 kb resolution). We have developed an R package named normGAM for eliminating the new fragment length bias together with the other three biases existing in raw GAM data, which are the biases related to window detection frequency, mappability, and GC content. Five normalization methods have been implemented and included in the R package including Knight-Ruiz 2-norm (KR2, newly designed by us), normalized linkage disequilibrium (NLD), vanilla coverage (VC), sequential component normalization (SCN), and iterative correction and eigenvector decomposition (ICE). CONCLUSIONS Based on our evaluations, the five normalization methods can eliminate the four biases existing in raw GAM data, with VC and KR2 performing better than the others. We have observed that the KR2-normalized GAM data have a higher correlation with the KR-normalized Hi-C data on the same cell samples indicating that the KR-related methods are better than the others for keeping the consistency between the GAM and Hi-C experiments. Compared with the raw GAM data, the normalized GAM data are more consistent with the normalized distances from the fluorescence in situ hybridization (FISH) experiments. The source code of normGAM can be freely downloaded from http://dna.cs.miami.edu/normGAM/.
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Affiliation(s)
- Tong Liu
- Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL, 33124, USA
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL, 33124, USA.
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Liu T, Wang Z. HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks. Genes (Basel) 2019; 10:genes10110862. [PMID: 31671634 PMCID: PMC6896157 DOI: 10.3390/genes10110862] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 10/28/2019] [Indexed: 12/17/2022] Open
Abstract
We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix.
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Affiliation(s)
- Tong Liu
- Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL 33124, USA.
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL 33124, USA.
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Liu T, Porter J, Zhao C, Zhu H, Wang N, Sun Z, Mo YY, Wang Z. TADKB: Family classification and a knowledge base of topologically associating domains. BMC Genomics 2019; 20:217. [PMID: 30871473 PMCID: PMC6419456 DOI: 10.1186/s12864-019-5551-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 02/21/2019] [Indexed: 01/01/2023] Open
Abstract
Background Topologically associating domains (TADs) are considered the structural and functional units of the genome. However, there is a lack of an integrated resource for TADs in the literature where researchers can obtain family classifications and detailed information about TADs. Results We built an online knowledge base TADKB integrating knowledge for TADs in eleven cell types of human and mouse. For each TAD, TADKB provides the predicted three-dimensional (3D) structures of chromosomes and TADs, and detailed annotations about the protein-coding genes and long non-coding RNAs (lncRNAs) existent in each TAD. Besides the 3D chromosomal structures inferred by population Hi-C, the single-cell haplotype-resolved chromosomal 3D structures of 17 GM12878 cells are also integrated in TADKB. A user can submit query gene/lncRNA ID/sequence to search for the TAD(s) that contain(s) the query gene or lncRNA. We also classified TADs into families. To achieve that, we used the TM-scores between reconstructed 3D structures of TADs as structural similarities and the Pearson’s correlation coefficients between the fold enrichment of chromatin states as functional similarities. All of the TADs in one cell type were clustered based on structural and functional similarities respectively using the spectral clustering algorithm with various predefined numbers of clusters. We have compared the overlapping TADs from structural and functional clusters and found that most of the TADs in the functional clusters with depleted chromatin states are clustered into one or two structural clusters. This novel finding indicates a connection between the 3D structures of TADs and their DNA functions in terms of chromatin states. Conclusion TADKB is available at http://dna.cs.miami.edu/TADKB/. Electronic supplementary material The online version of this article (10.1186/s12864-019-5551-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tong Liu
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33124-4245, USA
| | - Jacob Porter
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA
| | - Chenguang Zhao
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA
| | - Hao Zhu
- School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA
| | - Nan Wang
- Department of Computer Science, New Jersey City University, 2039 Kennedy Blvd, Jersey City, NJ, 07305, USA
| | - Zheng Sun
- Department of Electrical and Computer Engineering, California Baptist University, 3739 Adams Street, Riverside, CA, 92504, USA
| | - Yin-Yuan Mo
- Department of Pharmacology and Toxicology, University of Mississippi Medical Center, 2500 N State St, Jackson, MS, 39216, USA
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33124-4245, USA.
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Liu X, Xie L, Wu Z, Wang K, Zhao Z, Ruan J, Zhi D. The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications. BMC Bioinformatics 2018; 19:492. [PMID: 30591012 PMCID: PMC6309051 DOI: 10.1186/s12859-018-2460-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10–12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.
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Affiliation(s)
- Xiaoming Liu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Present address: College of Public Health, University of South Florida, Tampa, FL, 33612, USA.
| | - Lei Xie
- Department of Computer Science, Hunter College & The Graduate Center, The City University of New York, New York, NY, 10065, USA
| | - Zhijin Wu
- Department of Biostatistics, Brown University, Providence, RI, 02912, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA
| | - Degui Zhi
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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