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Yan TC, Yue ZX, Xu HQ, Liu YH, Hong YF, Chen GX, Tao L, Xie T. A systematic review of state-of-the-art strategies for machine learning-based protein function prediction. Comput Biol Med 2023; 154:106446. [PMID: 36680931 DOI: 10.1016/j.compbiomed.2022.106446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.
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Affiliation(s)
- Tian-Ci Yan
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zi-Xuan Yue
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Hong-Quan Xu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yu-Hong Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yan-Feng Hong
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Gong-Xing Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
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2
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Gliozzo J, Mesiti M, Notaro M, Petrini A, Patak A, Puertas-Gallardo A, Paccanaro A, Valentini G, Casiraghi E. Heterogeneous data integration methods for patient similarity networks. Brief Bioinform 2022; 23:6604996. [PMID: 35679533 PMCID: PMC9294435 DOI: 10.1093/bib/bbac207] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/14/2022] [Accepted: 05/04/2022] [Indexed: 12/29/2022] Open
Abstract
Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.
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Affiliation(s)
- Jessica Gliozzo
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,European Commission, Joint Research Centre (JRC), Ispra (VA), Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Marco Mesiti
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Marco Notaro
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Alessandro Petrini
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Alex Patak
- European Commission, Joint Research Centre (JRC), Ispra (VA), Italy
| | | | - Alberto Paccanaro
- Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX UK.,School of Applied Mathematics (EMAp), Fundação Getúlio Vargas, Rio de Janeiro Brazil
| | - Giorgio Valentini
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy.,DSRC UNIMI, Data Science Research Center, Milano, 20135, Italy.,ELLIS, European Laboratory for Learning and Intelligent Systems, Berlin, Germany
| | - Elena Casiraghi
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
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Rydén M, Englund M, Ali N. ProteoMill: Efficient network-based functional analysis portal for proteomics data. Bioinformatics 2021; 37:3491-3493. [PMID: 33978717 PMCID: PMC8545334 DOI: 10.1093/bioinformatics/btab373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/25/2021] [Accepted: 05/11/2021] [Indexed: 11/26/2022] Open
Abstract
Summary Functional analysis has become a common approach to incorporate biological knowledge into the analysis of omics data, and to explore molecular events that govern a disease state. It is though only one step in a wider analytical pipeline that typically requires use of multiple individual analysis software. There is currently a need for a well-integrated omics analysis tool that performs all the steps. The ProteoMill portal is developed as an R Shiny application and integrates all necessary steps from data-upload, converting identifiers, to quality control, differential expression and network-based functional analysis into a single fast, interactive easy to use workflow. Further, it maintains annotation data sources up to date, overcoming a common problem with use of outdated information and seamlessly integrates multiple R-packages for an improved user-experience. The functionality provided in this software can benefit researchers by facilitating the exploratory analysis of proteomics data. Availability and implementation ProteoMill is available at https://proteomill.com.
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Affiliation(s)
- Martin Rydén
- Faculty of Medicine, Department of Clinical Sciences Lund, Orthopaedics, Clinical Epidemiology Unit, Lund University, Lund, Sweden
| | - Martin Englund
- Faculty of Medicine, Department of Clinical Sciences Lund, Orthopaedics, Clinical Epidemiology Unit, Lund University, Lund, Sweden
| | - Neserin Ali
- Faculty of Medicine, Department of Clinical Sciences Lund, Rheumatology and Molecular Skeletal Biology, Lund University, Lund, Sweden
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Perlasca P, Frasca M, Ba CT, Gliozzo J, Notaro M, Pennacchioni M, Valentini G, Mesiti M. Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools. PLoS One 2020; 15:e0244241. [PMID: 33351828 PMCID: PMC7755227 DOI: 10.1371/journal.pone.0244241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 12/04/2020] [Indexed: 11/19/2022] Open
Abstract
The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.
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Affiliation(s)
- Paolo Perlasca
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Marco Frasca
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Cheick Tidiane Ba
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Jessica Gliozzo
- Neuroradiology Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Marco Notaro
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Mario Pennacchioni
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Giorgio Valentini
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
- CINI National Laboratory in Artificial Intelligence and Intelligent Systems—AIIS, Rome, Italy
| | - Marco Mesiti
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
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Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction. Sci Rep 2020; 10:3612. [PMID: 32107391 PMCID: PMC7046773 DOI: 10.1038/s41598-020-60235-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 11/05/2019] [Indexed: 12/15/2022] Open
Abstract
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification.
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