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Li C, Xiang Z, Tang J, Luo B, Wang F. RGBT Tracking via Noise-Robust Cross-Modal Ranking. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5019-5031. [PMID: 33861706 DOI: 10.1109/tnnls.2021.3067107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Existing RGBT tracking methods usually localize a target object with a bounding box, in which the trackers are often affected by the inclusion of background clutter. To address this issue, this article presents a novel algorithm, called noise-robust cross-modal ranking, to suppress background effects in target bounding boxes for RGBT tracking. In particular, we handle the noise interference in cross-modal fusion and seed labels from the following two aspects. First, the soft cross-modality consistency is proposed to allow the sparse inconsistency in fusing different modalities, aiming to take both collaboration and heterogeneity of different modalities into account for more effective fusion. Second, the optimal seed learning is designed to handle label noises of ranking seeds caused by some problems, such as irregular object shape and occlusion. In addition, to deploy the complementarity and maintain the structural information of different features within each modality, we perform an individual ranking for each feature and employ a cross-feature consistency to pursue their collaboration. A unified optimization framework with an efficient convergence speed is developed to solve the proposed model. Extensive experiments demonstrate the effectiveness and efficiency of the proposed approach comparing with state-of-the-art tracking methods on GTOT and RGBT234 benchmark data sets.
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Wang F, Wei L. Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images. Bioinformatics 2022; 38:2602-2611. [PMID: 35212728 DOI: 10.1093/bioinformatics/btac123] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/09/2022] [Accepted: 02/24/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction approaches. However, existing methods suffer from intrinsic limitations, such as poor feature representation ability, data imbalanced issue, and multi-label classification problem, greatly impacting the model performance and generalization. RESULTS In this study, we propose MSTLoc, a novel multi-scale end-to-end deep learning model to identify protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. In our MSTLoc, we deploy a deep convolution neural network to extract multi-scale features from the IHC images, aggregate the high-level features and low-level features via feature fusion to sufficiently exploit the dependencies amongst various subcellular locations, and utilize Vision Transformer (ViT) to model the relationship amongst the features and enhance the feature representation ability. We demonstrate that the proposed MSTLoc achieves better performance than current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we demonstrate that as compared with the hand-crafted features, the multi-scale deep features learnt from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Finally, case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved with cancer development. For the convenient use of our method, we establish a user-friendly webserver available at http://server.wei-group.net/ MSTLoc. AVAILABILITY AND IMPLEMENTATION http://server.wei-group.net/ MSTLoc. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fengsheng Wang
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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Ru X, Ye X, Sakurai T, Zou Q. Application of learning to rank in bioinformatics tasks. Brief Bioinform 2021; 22:6102666. [PMID: 33454758 DOI: 10.1093/bib/bbaa394] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 11/09/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
Over the past decades, learning to rank (LTR) algorithms have been gradually applied to bioinformatics. Such methods have shown significant advantages in multiple research tasks in this field. Therefore, it is necessary to summarize and discuss the application of these algorithms so that these algorithms are convenient and contribute to bioinformatics. In this paper, the characteristics of LTR algorithms and their strengths over other types of algorithms are analyzed based on the application of multiple perspectives in bioinformatics. Finally, the paper further discusses the shortcomings of the LTR algorithms, the methods and means to better use the algorithms and some open problems that currently exist.
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Affiliation(s)
| | - Xiucai Ye
- Department of Computer Science and Center for Artificial Intelligence Research (C-AIR), University of Tsukuba
| | | | - Quan Zou
- University of Electronic Science and Technology of China
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Pavlopoulos GA, Kontou PI, Pavlopoulou A, Bouyioukos C, Markou E, Bagos PG. Bipartite graphs in systems biology and medicine: a survey of methods and applications. Gigascience 2018; 7:1-31. [PMID: 29648623 PMCID: PMC6333914 DOI: 10.1093/gigascience/giy014] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Revised: 01/15/2018] [Accepted: 02/13/2018] [Indexed: 11/14/2022] Open
Abstract
The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.
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Affiliation(s)
- Georgios A Pavlopoulos
- Lawrence Berkeley Labs, DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA
| | - Panagiota I Kontou
- University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2–4, Lamia, 35100, Greece
| | - Athanasia Pavlopoulou
- Izmir International Biomedicine and Genome Institute (iBG-Izmir), Dokuz Eylül University, 35340, Turkey
| | - Costas Bouyioukos
- Université Paris Diderot, Sorbonne Paris Cité, Epigenetics and Cell Fate, UMR7216, CNRS, France
| | - Evripides Markou
- University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2–4, Lamia, 35100, Greece
| | - Pantelis G Bagos
- University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2–4, Lamia, 35100, Greece
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When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-3102-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Xia S, Chen P, Zhang J, Li X, Wang B. Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.087] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Manifold regularization in structured output space for semi-supervised structured output prediction. Neural Comput Appl 2016. [DOI: 10.1007/s00521-015-2029-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Poirion OB, Zhu X, Ching T, Garmire L. Single-Cell Transcriptomics Bioinformatics and Computational Challenges. Front Genet 2016; 7:163. [PMID: 27708664 PMCID: PMC5030210 DOI: 10.3389/fgene.2016.00163] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 09/02/2016] [Indexed: 12/21/2022] Open
Abstract
The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.
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Affiliation(s)
- Olivier B Poirion
- Epidemiology Program, University of Hawaii Cancer Center Honolulu, HI, USA
| | - Xun Zhu
- Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI, USA; Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at ManoaHonolulu, HI, USA
| | - Travers Ching
- Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI, USA; Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at ManoaHonolulu, HI, USA
| | - Lana Garmire
- Epidemiology Program, University of Hawaii Cancer Center Honolulu, HI, USA
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Cui X, Lu Z, Wang S, Jing-Yan Wang J, Gao X. CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction. Bioinformatics 2016; 32:i332-i340. [PMID: 27307635 PMCID: PMC4908355 DOI: 10.1093/bioinformatics/btw271] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
MOTIVATION Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. METHOD We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence-structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. RESULTS We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM-HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION Our program is freely available for download from http://sfb.kaust.edu.sa/Pages/Software.aspx CONTACT : xin.gao@kaust.edu.sa SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xuefeng Cui
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
| | - Zhiwu Lu
- Beijing Key Laboratory of Big Data Management and Analysis Methods, School of Information, Renmin University of China, Beijing 100872, China
| | - Sheng Wang
- Toyota Technological Institute at Chicago, 6045 Kenwood Avenue, Chicago, IL 60637, USA Department of Human Genetics, University of Chicago, E. 58th St, Chicago, IL 60637, USA
| | - Jim Jing-Yan Wang
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
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Liu X, Wang J, Yin M, Edwards B, Xu P. Supervised learning of sparse context reconstruction coefficients for data representation and classification. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2042-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Yang J, Ding Z, Guo F, Wang H, Hughes N. A novel multivariate performance optimization method based on sparse coding and hyper-predictor learning. Neural Netw 2015; 71:45-54. [PMID: 26291045 DOI: 10.1016/j.neunet.2015.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Revised: 06/05/2015] [Accepted: 07/23/2015] [Indexed: 10/23/2022]
Abstract
In this paper, we investigate the problem of optimization of multivariate performance measures, and propose a novel algorithm for it. Different from traditional machine learning methods which optimize simple loss functions to learn prediction function, the problem studied in this paper is how to learn effective hyper-predictor for a tuple of data points, so that a complex loss function corresponding to a multivariate performance measure can be minimized. We propose to present the tuple of data points to a tuple of sparse codes via a dictionary, and then apply a linear function to compare a sparse code against a given candidate class label. To learn the dictionary, sparse codes, and parameter of the linear function, we propose a joint optimization problem. In this problem, the both the reconstruction error and sparsity of sparse code, and the upper bound of the complex loss function are minimized. Moreover, the upper bound of the loss function is approximated by the sparse codes and the linear function parameter. To optimize this problem, we develop an iterative algorithm based on descent gradient methods to learn the sparse codes and hyper-predictor parameter alternately. Experiment results on some benchmark data sets show the advantage of the proposed methods over other state-of-the-art algorithms.
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Affiliation(s)
- Jiachen Yang
- School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
| | - Zhiyong Ding
- School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
| | - Fei Guo
- School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China.
| | - Huogen Wang
- School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
| | - Nick Hughes
- College of Information Science and Technology, University of Nebraska Omaha, Omaha, NE 68182, United States.
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Yang X, Yang F. Completing tags by local learning: a novel image tag completion method based on neighborhood tag vector predictor. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-1983-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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