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Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [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: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
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Stainthorp AK, Lin CC, Wang D, Medhi R, Ahmed Z, Suen KM, Miska EA, Whitehouse A, Ladbury JE. Regulation of microRNA expression by the adaptor protein GRB2. Sci Rep 2023; 13:9784. [PMID: 37328606 PMCID: PMC10276003 DOI: 10.1038/s41598-023-36996-3] [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: 03/21/2023] [Accepted: 06/14/2023] [Indexed: 06/18/2023] Open
Abstract
Protein interactions with the microRNA (miRNA)-mediated gene silencing protein Argonaute 2 (AGO2) control miRNA expression. miRNA biogenesis starts with the production of precursor transcripts and culminates with the loading of mature miRNA onto AGO2 by DICER1. Here we reveal an additional component to the regulatory mechanism for miRNA biogenesis involving the adaptor protein, growth factor receptor-bound protein 2 (GRB2). The N-terminal SH3 domain of GRB2 is recruited to the PAZ domain of AGO2 forming a ternary complex containing GRB2, AGO2 and DICER1. Using small-RNA sequencing we identified two groups of miRNAs which are regulated by the binding of GRB2. First, mature and precursor transcripts of mir-17~92 and mir-221 miRNAs are enhanced. Second, mature, but not precursor, let-7 family miRNAs are diminished suggesting that GRB2 directly affects loading of these miRNAs. Notably, the resulting loss of let-7 augments expression of oncogenic targets such as RAS. Thus, a new role for GRB2 is established with implications for cancer pathogenesis through regulation of miRNA biogenesis and oncogene expression.
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Affiliation(s)
- Amy K Stainthorp
- School of Molecular and Cellular Biology and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK
| | - Chi-Chuan Lin
- School of Molecular and Cellular Biology and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK
| | - Dapeng Wang
- LeedsOmics, University of Leeds, Leeds, LS2 9JT, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Ragini Medhi
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK
| | - Zamal Ahmed
- Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kin Man Suen
- School of Molecular and Cellular Biology and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK
| | - Eric A Miska
- Wellcome Trust Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK
| | - Adrian Whitehouse
- School of Molecular and Cellular Biology and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK
| | - John E Ladbury
- School of Molecular and Cellular Biology and Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK.
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Chen S, Liu Y, Zhang Y, Wierbowski SD, Lipkin SM, Wei X, Yu H. A full-proteome, interaction-specific characterization of mutational hotspots across human cancers. Genome Res 2022; 32:135-149. [PMID: 34963661 PMCID: PMC8744679 DOI: 10.1101/gr.275437.121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 11/22/2021] [Indexed: 11/24/2022]
Abstract
Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.
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Affiliation(s)
- Siwei Chen
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Yuan Liu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Yingying Zhang
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Shayne D Wierbowski
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Steven M Lipkin
- Department of Medicine, Weill Cornell Medicine, New York, New York 10021, USA
| | - Xiaomu Wei
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York 10021, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
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Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles. PLoS One 2020; 15:e0242780. [PMID: 33232371 PMCID: PMC7685479 DOI: 10.1371/journal.pone.0242780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 11/10/2020] [Indexed: 11/19/2022] Open
Abstract
As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.
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Washah HN, Salifu EY, Soremekun O, Elrashedy AA, Munsamy G, Olotu FA, Soliman ME. Integrating Bioinformatics Strategies in Cancer Immunotherapy: Current and Future Perspectives. Comb Chem High Throughput Screen 2020; 23:687-698. [DOI: 10.2174/1386207323666200427113734] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 12/21/2019] [Accepted: 02/26/2020] [Indexed: 02/08/2023]
Abstract
For the past few decades, the mechanisms of immune responses to cancer have been
exploited extensively and significant attention has been given into utilizing the therapeutic
potential of the immune system. Cancer immunotherapy has been established as a promising
innovative treatment for many forms of cancer. Immunotherapy has gained its prominence through
various strategies, including cancer vaccines, monoclonal antibodies (mAbs), adoptive T cell cancer
therapy, and immune checkpoint therapy. However, the full potential of cancer immunotherapy is yet
to be attained. Recent studies have identified the use of bioinformatics tools as a viable option to help
transform the treatment paradigm of several tumors by providing a therapeutically efficient method of
cataloging, predicting and selecting immunotherapeutic targets, which are known bottlenecks in the
application of immunotherapy. Herein, we gave an insightful overview of the types of
immunotherapy techniques used currently, their mechanisms of action, and discussed some
bioinformatics tools and databases applied in the immunotherapy of cancer. This review also provides
some future perspectives in the use of bioinformatics tools for immunotherapy.
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Affiliation(s)
- Houda N. Washah
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Elliasu Y. Salifu
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Opeyemi Soremekun
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Ahmed A. Elrashedy
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Geraldene Munsamy
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Fisayo A. Olotu
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Mahmoud E.S. Soliman
- Molecular Bio-computation and Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
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Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders. Comput Struct Biotechnol J 2020; 18:2535-2546. [PMID: 33033576 PMCID: PMC7522539 DOI: 10.1016/j.csbj.2020.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/06/2020] [Accepted: 09/07/2020] [Indexed: 12/30/2022] Open
Abstract
Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.
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Si Z, Hu K. Identification of osteosarcoma driver genes using a network method. Oncol Lett 2020; 19:1215-1222. [PMID: 31966051 PMCID: PMC6956419 DOI: 10.3892/ol.2019.11212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 11/07/2019] [Indexed: 02/05/2023] Open
Abstract
Osteosarcoma (OS) is a severe disease that is generally caused by genetic alterations. Systematic identification of driver genes may be used to increase the understanding of the mechanisms underlying the disease. The present study identified a framework to predict driver genes, with the hypothesis that driver genes operate through a number of connected functional genes. OS-related genes were extracted from the Catalogue Of Somatic Mutations In Cancer and subsequently ranked by virtue of their effect on a set of functional genes using a network-based algorithm. This revealed the driver genes associated with dysregulated networks. In addition, compared with the Mutations For Functional Impact on Network Neighbors algorithm, the results obtained using the aforementioned network-based algorithm revealed that the proposed method is effective. Gene functional analysis demonstrated that the potential OS driver genes were involved in OS-associated pathways. Through the validation of the 15 candidate OS driver genes, the classifier constructed in the present study revealed that the identified driver genes were able to distinguish 184 cancer samples from controls. Therefore, the present study provided insights into the identification of driver genes from a vast amount of sequencing data.
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Affiliation(s)
- Zebing Si
- Department of Orthopedics, The Affiliated Yuebei People's Hospital of Shantou University Medical College, Wujiang, Shaoguan 512026, P.R. China
| | - Konghe Hu
- Department of Orthopedics, The Affiliated Yuebei People's Hospital of Shantou University Medical College, Wujiang, Shaoguan 512026, P.R. China
- Correspondence to: Dr Konghe Hu, Department of Orthopedics, The Affiliated Yuebei People's Hospital of Shantou University Medical College, 133 Shaoguan Huimin South Avenue, Wujiang, Shaoguan 512026, P.R. China, E-mail:
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Hui Y, Wei PJ, Xia J, Wang YT, Zheng CH. MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks. BMC Med Genomics 2019; 12:140. [PMID: 31888623 PMCID: PMC6936061 DOI: 10.1186/s12920-019-0582-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 09/10/2019] [Indexed: 01/09/2023] Open
Abstract
Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.
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Affiliation(s)
- Ying Hui
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Pi-Jing Wei
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China
| | - Junfeng Xia
- Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Yu-Tian Wang
- School of Software Engineering, Qufu Normal University, Qufu, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China.
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Cava C, Bertoli G, Colaprico A, Olsen C, Bontempi G, Castiglioni I. Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis. BMC Genomics 2018; 19:25. [PMID: 29304754 PMCID: PMC5756345 DOI: 10.1186/s12864-017-4423-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 12/27/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. RESULTS We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. CONCLUSIONS Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.
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Affiliation(s)
- Claudia Cava
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Milan, Segrate-Milan Italy
| | - Gloria Bertoli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Milan, Segrate-Milan Italy
| | - Antonio Colaprico
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, 1050 Brussels, Belgium
- Machine Learning Group (MLG), Department d’Informatique, Universite libre de Bruxelles (ULB), 1050 Brussels, Belgium
| | - Catharina Olsen
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, 1050 Brussels, Belgium
- Machine Learning Group (MLG), Department d’Informatique, Universite libre de Bruxelles (ULB), 1050 Brussels, Belgium
| | - Gianluca Bontempi
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, 1050 Brussels, Belgium
- Machine Learning Group (MLG), Department d’Informatique, Universite libre de Bruxelles (ULB), 1050 Brussels, Belgium
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Milan, Segrate-Milan Italy
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