@article{Erdős2026,

title = {Pathogenic variations illuminate functional constraints in intrinsically disordered proteins},

author = {Norbert Deutsch; Gábor Erdős and Zsuzsanna Dosztányi },

doi = {10.1101/2025.05.01.651640},

year  = {2026},

date = {2026-04-17},

urldate = {2026-04-17},

journal = {iScience},

volume = {49},

issue = {4},

abstract = {Intrinsically disordered regions (IDRs) play key roles in cellular signaling and regulation, yet their contribution to human disease remains poorly understood. Here, we analyzed nearly one million ClinVar missense variants, focusing on those located within IDRs defined by curated and predicted annotations. Pathogenic variants were significantly enriched in short linear motifs (SLiMs) and disordered binding regions, consistent with their central functional importance. To extend these insights beyond existing annotations, we applied AlphaMissense and uncovered localized “island-like” patterns of elevated pathogenicity within IDRs. Leveraging these signals, we developed a classifier to prioritize predicted ELM motifs (PEMs), revealing thousands of candidate functional sites linked to major disease classes, including neurological, cardiovascular, and cancer-associated genes. Case studies, including POLK and FOXP2, demonstrate how this approach connects genetic variation to molecular mechanisms. This framework provides a scalable strategy to interpret variants of uncertain significance and defines the functional constraints governing the disordered proteome.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{etal.al.2026,

title = {Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences},

author = {Gavin Farrell et al., including Alexander Miguel Monzon and Silvio C. E. Tosatto.},

doi = {10.1038/s41592-026-03037-6},

year  = {2026},

date = {2026-03-20},

urldate = {2026-03-20},

journal = {Nature Methods},

abstract = {Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tosatto2026,

title = {Missense Constraint in Intrinsically Disordered Proteins Enhances Missense Variant Interpretation in Neurodevelopmental Disorders},

author = {Nazareth D. J. Robles; Silvio C. E. Tosatto and Maria Cristina Aspromonte.},

doi = {10.3390/genes17020219},

year  = {2026},

date = {2026-02-10},

urldate = {2026-02-10},

journal = {Genes},

volume = {17},

issue = {2},

abstract = {Interpreting missense variants in intrinsically disordered proteins (IDPs) remains a major challenge, as these proteins lack stable structure and are under-represented in experimental and clinical annotations. Variants occurring in IDPs are disproportionately classified as variants of uncertain significance (VUS), reflecting the absence of appropriate predictive tools rather than true biological neutrality. Here, we address this challenge using a curated dataset of neurodevelopmental disorder (NDD)-associated proteins. Methods: We integrated curated and predicted disorder annotations from DisProt and MobiDB to characterize the structural landscape of 339 NDD-associated proteins. To quantify a regional genetic constraint, we recalculated the Missense Tolerance Ratio (MTR) using a published framework adapted to the recent gnomAD release (v4.1.0). Integration with 33,124 ClinVar-reported missense variants revealed that, while mean constraint levels differ only modestly across structural states, ordered and structural transition regions show the strongest depletion of missense variation. Results: MTR identifies localized low-tolerance subregions within IDRs, indicating that these regions are not uniformly permissive and can harbor functionally essential elements. Conclusions: Overall, our results demonstrate that missense constraint in NDD proteins is highly localized and context-dependent, and that integrating high-quality disorder annotations with updated MTR profiles can improve the prioritization and interpretation of missense variants in IDRs and IDPs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tosatto2025b,

title = {Modeling intrinsically disordered regions from AlphaFold2 to AlphaFold3},

author = {Mahta Mehdiabadi; Silvio C. E. Tosatto and Damiano Piovesan},

doi = {10.1002/pro.70426},

year  = {2025},

date = {2025-12-27},

urldate = {2025-12-27},

journal = {Protein Science},

volume = {35},

issue = {1},

abstract = {AlphaFold2 has demonstrated a remarkable success in predicting the structures of globular proteins and folded domains with near-experimental accuracy. However, it typically represents intrinsically disordered regions (IDRs), protein segments that lack a stable 3D structure under physiological conditions, as long extended loops that appear to float around the structured core. While AlphaFold2's static prediction cannot capture the conformational heterogeneity and the dynamic nature of IDRs, it performs well in predicting IDRs from sequence. AlphaFold3 introduces significant architectural and training modifications over its predecessor, including the use of cross-distillation aimed at reducing structural hallucinations in disordered regions. In this study, we look into how these models differ in representing IDRs. We evaluate the performance of AlphaFold3 and AlphaFold2 on disorder prediction, using the CAID3 benchmark. Our analysis shows that AlphaFold3 does not outperform AlphaFold2 in this benchmark. We observe that solvent accessibility remains a robust and consistent proxy for predicting intrinsic disorder across both models. However, changes in the predicted secondary structure content and pLDDT scores lead to different interpretations of disorder. Overall, our findings suggest that AlphaFold2 remains the preferred choice for identifying intrinsically disordered regions, as it avoids structural hallucinations while providing predictions comparable to those of AlphaFold3.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {IDPEnsembleTools: An open-source library for analysis of conformational ensembles of disordered proteins},

author = {Hamidreza Ghafouri; Giacomo Janson; Silvio C. E. Tosatto and Alexander Miguel Monzon.},

doi = {10.1002/pro.70427},

year  = {2025},

date = {2025-12-23},

urldate = {2025-12-23},

journal = {Protein Science},

volume = {35},

issue = {1},

abstract = {Intrinsically disordered proteins (IDPs) lack stable tertiary structure and instead exist as dynamic ensembles of conformations, playing essential roles in cellular regulation, signaling, and disease. As structural ensembles of IDPs become increasingly available through databases such as the Protein Ensemble Database (PED) and various computational generation methods, the need for systematic tools to analyze and compare these ensembles has grown. Here, we present IDPET (Intrinsically Disordered Protein Ensemble Tools), an open-source Python library designed to facilitate comprehensive analysis of IDP conformational ensembles. IDPET enables users to load and process ensembles from various sources and formats in parallel, compute global and local structural features, perform dimensionality reduction and clustering, and compare ensembles quantitatively using metrics based on Jensen–Shannon divergence (JSD). To demonstrate the package's functionalities, we analyze three ensembles of the unfolded drkN SH3 domain deposited in PED. This example illustrates how IDPET can extract structural descriptors, visualize conformational diversity, assess global and local features, and quantify differences between ensembles generated using distinct experimental and computational methods. By providing a reproducible and extensible framework, IDPET supports systematic exploration of ensemble features in IDPs. It is compatible with atomistic and coarse-grained models and can be easily integrated with community resources.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ghafouri2025,

title = {Advances in the determination of disordered protein ensemble},

author = {Hamidreza Ghafouri; Silvio C. E. Tosatto and Alexander Miguel Monzon.},

doi = {10.1016/j.sbi.2025.103198},

year  = {2025},

date = {2025-12-11},

urldate = {2025-12-11},

journal = {Current Opinion in Structural Biology},

volume = {96},

abstract = {Intrinsically disordered proteins (IDPs) play essential roles in regulation, signaling, and phase separation, yet their structural complexity cannot be captured by a single conformation. Instead, they populate dynamic ensembles that encode a context-dependent function. Recent advances in experimental techniques coupled with physics-based simulations, coarse-grained models, and machine learning, have transformed our ability to generate and interpret IDP ensembles. Integrative frameworks now combine complementary data with computational approaches to refine ensembles at both local and global levels. Nevertheless, challenges remain in benchmarking, error estimation, and modeling assemblies involving protein–protein and protein–nucleic acid interactions. We highlight recent progress and outline the emerging directions that will shape the next generation of ensemble determination methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {DisProt in 2026: enhancing intrinsically disordered proteins accessibility, deposition, and annotation},

author = {Maria Victoria Nugnes; Kamel Eddine Adel Bouhraoua; Mehdi Zoubiri; Rita Pancsa; Erzsébet Fichó; DisProt Consortium; Peter Tompa; Damiano Piovesan; Silvio C.E. Tosatto and Maria Cristina Aspromonte},

doi = {10.1093/nar/gkaf1175},

year  = {2025},

date = {2025-11-17},

urldate = {2025-11-17},

journal = {Nucleic Acids Research},

volume = {54},

issue = {D1},

abstract = {DisProt (https://disprot.org/) is an open database integrating experimental evidence on intrinsically disordered proteins (IDPs), intrinsically disordered regions (IDRs), and their functions. Over the past two years, the database has grown over 20%, now comprising 3201 IDPs and 13 347 pieces of evidence, including over 1500 new structural state annotations and >1300 new function annotations. DisProt has systematically adopted the Minimum Information About Disorder Experiments (MIADE) guidelines, more than doubling annotations with experimental details and improving the interpretability of disorder-related experiments. The website has evolved into a hybrid knowledgebase and deposition system, introducing a Deposition Page that allows direct submissions by external users. Through BLAST-based homology propagation in MobiDB, DisProt disorder regions and linear interacting peptides have been extended from hundreds to hundreds of thousands of proteins across >11 000 organisms. This new release marks a paradigm shift by integrating computational predictions as valid evidence and introducing major updates and restructuring of the IDP Ontology, enhancing accuracy, interoperability, and semantic clarity. DisProt continues to support community engagement through training resources together with DisTriage, an AI-based literature triage tool, providing curators with regularly updated lists of prioritized publications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tosatto2025,

title = {Integrative Multi-Omics Characterization and Structural Insights into the Poorly Annotated Integrin ITGA6 X1X2 Isoform in Mammals},

author = {Ximena Aixa Castro Naser;Alessandro Cestaro ; Silvio C. E. Tosatto and Emanuela Leonardi.},

doi = {10.3390/genes16101134},

year  = {2025},

date = {2025-09-25},

urldate = {2025-09-25},

journal = {Genes},

volume = {16},

issue = {10},

abstract = {Background: Accurate annotation of gene isoforms remains one of the major obstacles in translating genomic data into meaningful biological insight. Laminin-binding integrins, particularly integrin α6 (ITGA6), exemplify this challenge through their complex splicing patterns. The rare ITGA6 X1X2 isoform, generated by the alternative inclusion of exons X1 and X2 within the β-propeller domain, has remained poorly characterized despite decades of integrin research. Methods: We combined comparative genomics across primates with targeted re-alignment to assess exon conservation and annotation fidelity; analyzed RNA-seq for exon-level usage; applied splice-site prediction to evaluate inclusion potential; surveyed cancer mutation resources for exon-specific variants; and used structural/disorder modeling to infer effects on the β-propeller. Results: Exon X2 is conserved at the genomic level but inconsistently annotated, reflecting the limitations of current annotation pipelines rather than genuine evolutionary loss. RNA-seq analyses reveal low but detectable expression of X2, consistent with weak splice site predictions that suggest strict regulatory control and condition-specific expression. Despite its rarity, recurrent mutations in exon X2 are reported in cancer datasets, implying possible roles in disease. Structural modeling further indicates that X2 contributes to a flexible, disordered region within the β-propeller domain, potentially influencing laminin binding or β-subunit dimerization. Conclusions: Altogether, our results suggest that ITGA6 X1X2 could be a rare, tightly regulated isoform with potential functional and pathological relevance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mehdiabadi2025b,

title = {Critical Assessment of Protein Intrinsic Disorder Round 3 ‐ Predicting Disorder in the Era of Protein Language Models},

author = {Mahta Mehdiabadi and Alessio Del Conte and Maria Victoria Nugnes and Maria Cristina Aspromonte and Silvio C. E. Tosatto and Damiano Piovesan},

doi = {10.1002/prot.70045},

issn = {1097-0134},

year  = {2025},

date = {2025-08-26},

journal = {Proteins},

publisher = {Wiley},

abstract = {ABSTRACTIntrinsic disorder (ID) in proteins is a complex phenomenon, encompassing a continuum from entirely disordered regions to structured domains with flexible segments. The absence of a ground truth for all forms of disorder, combined with the possibility of structural transitions between ordered and disordered states under specific conditions, makes accurate prediction of ID especially challenging. The Critical Assessment of Protein Intrinsic Disorder (CAID) evaluates ID prediction methods using diverse benchmarks derived from DisProt, a manually curated database of experimentally validated annotations. This paper presents findings from the third round (CAID3), in which 24 new methods were assessed along with the predictors from previous rounds. Compared to CAID2, the top‐performing methods in CAID3 demonstrated significant gains in average precision: over 31% improvement in predicting linker regions, and 15% in disorder prediction. This round introduces a new binding sub‐challenge focused on identifying binding regions within known IDR boundaries. The results indicate that this task remains challenging, highlighting the potential for improvement. The top‐performing methods in CAID3 are mostly new and commonly used embeddings from protein language models (pLMs), underscoring the growing impact of pLMs in tackling the complexities of disordered proteins and advancing ID prediction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Erdős2025,

title = {AIUPred – Binding: Energy Embedding to Identify Disordered Binding Regions},

author = {Gábor Erdős and Norbert Deutsch and Zsuzsanna Dosztányi},

doi = {10.1016/j.jmb.2025.169071},

issn = {0022-2836},

year  = {2025},

date = {2025-08-01},

journal = {Journal of Molecular Biology},

volume = {437},

number = {15},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chemes2025,

title = {Sensitivity to chirality correlates in a continuum with protein disorder},

author = {Lucía Álvarez; Lucía Beatriz Chemes},

doi = {10.1016/j.tibs.2025.04.003},

issn = { 0968-0004},

year  = {2025},

date = {2025-07-03},

urldate = {2025-07-03},

journal = {Trends in Biochemical Sciences},

volume = {50},

issue = {7},

abstract = {Intrinsically disordered proteins

(IDPs) exist as dynamic conformational ensembles the behavior of

which challenges the tenets of the

protein structure–function paradigm. In a new study, Newcombe,

Due et al. reveal a striking continuum in sensitivity to chirality: while

folded complexes are under strong

chiral constraints, progressively

disordered complexes show decreased sensitivity to chirality. 

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Erdős2025b,

title = {AIUPred – Binding: Energy Embedding to Identify Disordered Binding Regions},

author = {Gábor Erdős; Norbert Deutsch and Zsuzsanna Dosztányi},

doi = {10.1016/j.jmb.2025.169071},

year  = {2025},

date = {2025-06-04},

urldate = {2025-06-04},

journal = {Journal of Molecular Biology},

volume = {437},

issue = {15},

abstract = {Intrinsically disordered regions (IDRs) play critical roles in various cellular processes, often mediating interactions through disordered binding regions that transition to ordered states. Experimental characterization of these functional regions is highly challenging, underscoring the need for fast and accurate computational tools. Despite their importance, predicting disordered binding regions remains a significant challenge due to limitations in existing datasets and methodologies. In this study, we introduce AIUPred-binding, a novel prediction tool leveraging a high dimensional mathematical representation of structural energies – we call energy embedding – and pathogenicity scores from AlphaMissense. By employing a transfer learning approach, AIUPred-binding demonstrates improved accuracy in identifying functional sites within IDRs. Our results highlight the tool’s ability to discern subtle features within disordered regions, addressing biases and other challenges associated with manually curated datasets. We present AIUPred-binding integrated into the AIUPred web framework as a versatile and efficient resource for understanding the functional roles of IDRs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mehdiabadi2025,

title = {MobiDB-lite 4.0: faster prediction of intrinsic protein disorder and structural compactness},

author = {Mahta Mehdiabadi and Matthias Blum and Giulio Tesei and Sören von Bülow and Kresten Lindorff-Larsen and Silvio C E Tosatto and Damiano Piovesan},

editor = {Jianlin Cheng},

doi = {10.1093/bioinformatics/btaf297},

issn = {1367-4811},

year  = {2025},

date = {2025-05-06},

volume = {41},

number = {5},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               

                  Motivation

                  In recent years, many disorder predictors have been developed to identify intrinsically disordered regions (IDRs) in proteins, achieving high accuracy. However, it may be difficult to interpret differences in predictions across methods. Consensus methods offer a simple solution, highlighting reliable predictions while filtering out uncertain positions. Here, we present a new version of MobiDB-lite, a consensus method designed to predict long IDRs and classify them based on compositional biases and conformational properties.

               

               

                  Results

                  MobiDB-lite 4.0 pipeline was optimized to be ten times faster than the previous version. It now provides compactness annotations based on predicted apparent scaling exponent. The newly added features and disorder subclassifications allow the users to get a comprehensive insight into the protein’s function and characteristics. MobiDB-lite 4.0 is integrated into the MobiDB and DisProt databases. A version without the compactness predictor is integrated into InterProScan, propagating MobiDB-lite annotations to UniProtKB.

               

               

                  Availability and implementation

                  The MobiDB-lite 4.0 source code and a Docker container are available from the GitHub repository: https://github.com/BioComputingUP/MobiDB-lite.

               },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Piovesan2024,

title = {MOBIDB in 2025: integrating ensemble properties and function annotations for intrinsically disordered proteins},

author = {Damiano Piovesan and Alessio Del Conte and Mahta Mehdiabadi and Maria Cristina Aspromonte and Matthias Blum and Giulio Tesei and Sören von Bülow and Kresten Lindorff-Larsen and Silvio C E Tosatto},

doi = {10.1093/nar/gkae969},

issn = {1362-4962},

year  = {2025},

date = {2025-01-06},

volume = {53},

number = {D1},

pages = {D495--D503},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               The MobiDB database (URL: https://mobidb.org/) aims to provide structural and functional information about intrinsic protein disorder, aggregating annotations from the literature, experimental data, and predictions for all known protein sequences. Here, we describe the improvements made to our resource to capture more information, simplify access to the aggregated data, and increase documentation of all MobiDB features. Compared to the previous release, all underlying pipeline modules were updated. The prediction module is ten times faster and can detect if a predicted disordered region is structurally extended or compact. The PDB component is now able to process large cryo-EM structures extending the number of processed entries. The entry page has been restyled to highlight functional aspects of disorder and all graphical modules have been completely reimplemented for better flexibility and faster rendering. The server has been improved to optimise bulk downloads. Annotation provenance has been standardised by adopting ECO terms. Finally, we propagated disorder function (IDPO and GO terms) from the DisProt database exploiting sequence similarity and protein embeddings. These improvements, along with the addition of comprehensive training material, offer a more intuitive interface and novel functional knowledge about intrinsic disorder.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DelConte2024,

title = {DRMAAtic: dramatically improve your cluster potential},

author = {Alessio Del Conte and Hamidreza Ghafouri and Damiano Clementel and Ivan Mičetić and Damiano Piovesan and Silvio C E Tosatto and Alexander Miguel Monzon},

editor = {Toma Tebaldi},

doi = {10.1093/bioadv/vbaf112},

issn = {2635-0041},

year  = {2024},

date = {2024-12-26},

volume = {5},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               

                  Motivation

                  The accessibility and usability of high-performance computing (HPC) resources remain significant challenges in bioinformatics, particularly for researchers lacking extensive technical expertise. While Distributed Resource Managers (DRMs) optimize resource utilization, the complexities of interfacing with these systems often hinder broader adoption. DRMAAtic addresses these challenges by integrating the Distributed Resource Management Application API (DRMAA) with a user-friendly RESTful interface, simplifying job management across diverse HPC environments. This framework empowers researchers to submit, monitor, and retrieve computational jobs securely and efficiently, without requiring deep knowledge of underlying cluster configurations.

               

               

                  Results

                  We present DRMAAtic, a flexible and scalable tool that bridges the gap between web interfaces and HPC infrastructures. Built on the Django REST Framework, DRMAAtic supports seamless job submission and management via HTTP calls. Its modular architecture enables integration with any DRM supporting DRMAA APIs and offers robust features such as role-based access control, throttling mechanisms, and dependency management. Successful applications of DRMAAtic include the RING web server for protein structure analysis, the CAID Prediction Portal for disorder and binding predictions, and the Protein Ensemble Database deposition server. These deployments demonstrate DRMAAtic’s potential to enhance computational workflows, improve resource efficiency, and facilitate open science in life sciences.

               

               

                  Availability and implementation

                  https://github.com/BioComputingUP/DRMAAtic, https://drmaatic.biocomputingup.it/.

               },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aspromonte2024,

title = {Searching and Using MobiDB Resource 6 to Explore Predictions and Annotations for Intrinsically Disordered Proteins},

author = {Maria Cristina Aspromonte and Federica Quaglia and Alexander Miguel Monzon and Damiano Clementel and Alessio Del Conte and Damiano Piovesan and Silvio C. E. Tosatto},

doi = {10.1002/cpz1.70077},

issn = {2691-1299},

year  = {2024},

date = {2024-12-24},

journal = {Current Protocols},

volume = {4},

number = {12},

publisher = {Wiley},

abstract = {<jats:title>Abstract</jats:title><jats:p>Intrinsically disordered proteins (IDPs) make up around 30% of eukaryotic proteomes and play a crucial role in cellular processes and in pathological conditions such as neurodegenerative disorders and cancers. However, IDPs exhibit dynamic conformational ensembles and are often involved in the formation of biomolecular condensates. Understanding the function of IDPs is critical to research in many areas of science. MobiDB is a unique resource that serves as a comprehensive knowledgebase of IDPs and intrinsically disordered regions (IDRs), combining disorder annotations from experimental evidence and predictions for a broad range of protein sequences. Over the past decade, MobiDB has evolved with a focus on expanding annotation coverage, standardizing annotation provenance, and enhancing database accessibility. The latest MobiDB, version 6, released in July 2024, includes significant improvements, such as the integration of AlphaFoldDB predictions and a new homology transfer pipeline that has substantially increased the number of entries with high‐quality annotations. The user interface has also been updated, highlighting annotation features, clarifying the entry page, and providing an immediate overview of disorder, binding, and disorder functions information in the protein sequence. This protocol guides the user through applications of the MobiDB, including disorder prediction, curated data analysis, and exploration of interaction data. This guide covers how to perform a search in MobiDB annotations using the web interface and the MobiDB REST API for programmatic access. The protocols use a step‐by‐step walkthrough using the human growth hormone receptor to demonstrate MobiDB's functions for visualization and interpretation of protein disorder data. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.</jats:p><jats:p><jats:bold>Basic Protocol 1</jats:bold>: Searching MobiDB query formats</jats:p><jats:p><jats:bold>Basic Protocol 2</jats:bold>: Searching MobiDB selected datasets and selected proteomes</jats:p><jats:p><jats:bold>Basic Protocol 3</jats:bold>: Performing a search on the Statistics page in MobiDB</jats:p><jats:p><jats:bold>Support Protocol</jats:bold>: Programmatic access with MobiDB REST API</jats:p><jats:p><jats:bold>Basic Protocol 4</jats:bold>: Visualizing and interpreting a MobiDB Entry: The GHR use case</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}