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Abstract Background Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. Methods By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies. [ABSTRACT FROM AUTHOR]

Background: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. Methods: By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results: A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions: The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases. [ABSTRACT FROM AUTHOR]

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower.Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer.Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets.Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease‐related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open‐access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps. [ABSTRACT FROM AUTHOR]

We use persistent cohomology and circular coordinates to investigate three datasets related to infectious diseases. We show that all three datasets exhibit circular coordinates that carry information about the data itself. For one of the datasets we are able to recover time post infection from the circular coordinate itself - for the other datasets, this information was not available, but in one we were able to relate the circular coordinate to red blood cell counts and weight changes in the subjects. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, the news reports on it have been accompanied by maps. Similarly to weather maps, they became part of our daily news feed. The newspapers' maps reporting on the COVID-19 pandemic often use the same or similar data to depict the current state of the spread within a national territory. However, the designs of the maps vary between different news outlets. These differences were analysed using the documentary method introduced by sociologist Ralf Bohnsack, using the case study of six COVID-19 maps published in Mexican national newspapers. The analysis resulted in a typology of maps, which proposed different readings of the infographic itself when it comes to the aspects of (1) the cognitive process it supposedly triggers, (2) the kind of information it contains, (3) the usefulness for the viewer/reader, and (4) whether the author or the reader has the authority over the reading/learning process. [ABSTRACT FROM AUTHOR]

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation. [ABSTRACT FROM AUTHOR]

This scoping review investigates the characteristics and reporting of disease maps of zoonoses published in the scientific literature from 2017 to 2018. Two reviewers conducted duplicate screening of titles and abstracts identified from a search in Medline and additional databases. Studies were included if they had a disease map figure describing a zoonotic disease. Map characteristics were extracted and summarized from full-text articles meeting inclusion criteria. The search identified 1666 records. A total of 302 articles meeting eligibility criteria were included, comprising 505 disease maps. While most studies (66%) used maps for descriptive exploratory purposes of identifying and representing spatial patterns visually, disease maps were also used analytically to display the results of geospatial and spatial statistical analyses in studies (34%). Most published disease maps identified in this review were reported without information that could be important for geospatial interpretations and their reproducibility. Specifically, 92% of maps in this review did not report the map projection. The findings from this scoping review support the development of a reporting guideline for thematic disease maps. [ABSTRACT FROM AUTHOR]

Background: Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms' models and examined the performance of different machine approaches for that task. Results: We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models' differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems' performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions: Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale. [ABSTRACT FROM AUTHOR]

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions. [ABSTRACT FROM AUTHOR]

Objectives: Deficits related to inadequate clinical communication can result in incorrect diagnoses, inappropriate surgery, incorrect disease stratification, pathologic reporting, and/or interpretation. There are currently no validated or defined solutions to disease-specific communication with regard to thyroid care.Methods: We propose a solution that could ameliorate problems arising from inadequate disease-specific communications between physicians through the development of a thyroid disease-specific database, the Thyroid Care Collaborative.Results: To improve the quality of thyroid nodule and cancer care, we have developed an imaging module for enhanced reporting of ultrasound, cytologic, surgical, and pathologic details that are obtained during the workup and treatment of a patient.Conclusion: The main advantages of this disease-specific, dynamic, three-dimensional, anatomic disease map are: 1) portability across institutions and disciplines, 2) disease specificity to thyroid nodule and cancer care, and 3) ability to trigger more detailed evaluation or reconciliation of any change in a patient's status regarding the nature or the extent of a patient's disease. The first and second advantages above have been identified as areas representing opportunities for quality improvement in health informatics research. Laryngoscope, 129:269-274, 2019. [ABSTRACT FROM AUTHOR]

Australia and Brazil are among the most important sugarcane producers in the world. Even under favorable climatic conditions, the yields in these regions are often limited by several factors, including fungal diseases. Sugarcane orange rust (SOR), caused by Puccinia kuehnii, is one of the most severe fungal diseases and can cause extensive losses in susceptible cultivars. Considering the significant influence climatic conditions can exert on SOR, the present study had as its aims, firstly, evaluation of weather data during SOR epidemics in the state of Queensland, Australia, in 1999/2000, and the state of São Paulo, Brazil, in 2009/2010; and secondly, development of a predictive model for SOR occurrence and severity, for identifying agro-climatic favorability zones for this disease in these regions. The results showed that SOR climatic favorability was highest on east of Queensland and in the center-east region of São Paulo, where air temperature and humidity were favorable for the epidemics. This information can help growers to avoid the use of susceptible cultivars in those zones where SOR poses the greatest risk, and to optimize strategies for chemical control of the disease. [ABSTRACT FROM AUTHOR]

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Background: Good maps of malaria risk have long been recognized as an important tool for malaria control. The production of such maps relies on modelling to predict the risk for most of the map, with actual observations of malaria prevalence usually only known at a limited number of specific locations. Estimation is complicated by the fact that there is often local variation of risk that cannot be accounted for by the known covariates and because data points of measured malaria prevalence are not evenly or randomly spread across the area to be mapped.Method: We describe, by way of an example, a simple two-stage procedure for producing maps of predicted risk: we use logistic regression modelling to determine approximate risk on a larger scale and we employ geo-statistical ('kriging') approaches to improve prediction at a local level. Malaria prevalence in children under 10 was modelled using climatic, population and topographic variables as potential predictors. After the regression analysis, spatial dependence of the model residuals was investigated. Kriging on the residuals was used to model local variation in malaria risk over and above that which is predicted by the regression model.Results: The method is illustrated by a map showing the improvement of risk prediction brought about by the second stage. The advantages and shortcomings of this approach are discussed in the context of the need for further development of methodology and software. [ABSTRACT FROM AUTHOR]

Since movement between countries has become easier for people, preventing the spread of various infectious diseases occurring around the world has become an international public health challenge. The use of "disease maps" in infectious disease prevention can encourage a range of studies on patterns of occurrence and modes of transmission to create thematic visual guides charting the geographical spread of diseases, and infectious disease research agencies around the world are conducting epidemiological surveillance for tracking and analyzing the occurrence of infectious diseases. In the case of Japan, influenza is a recurring and widespread infectious disease for which surveillance programs involving mapping are ongoing. With the development of Information-Communication Technology (ICT), surveillance information is easily accessible on the Web to both medical professions and ordinary citizens, as are online Geographical Information Systems (GIS) utilizing these data. Adapting to these transformations in the environment of information delivery requires that disease maps reflect an accurate grasp of local trends and real-time information delivery. Most studies of disease mapping have focused on the technical dimensions of the utilization of disease maps and WebGIS, and very little research has evaluated the use of disease maps and the quality of surveillance information on a regional scale. Accordingly, the aim of this study is to examine the current status of utilization of disease maps in Japan and issues for health crisis management, with a focus on local influenza surveillance. We conducted a survey of the websites of specialized agencies and local governments in Japan and we engaged in semi-structured interviews with officials from nine agencies that deliver epidemiological information using disease maps. The website survey assessed the quality of information provided by 82 public health institutes, 552 public health centers, 1,042 medical associations, and 1,977 local governments based on an index of 15 items measuring spatial scale along with map forms and usage. Interview items encompassed the "history of construction and management of the local surveillance system," "users and utilization of local surveillance," "impact of introduction and relationship with other surveillance measures," "new developments and enhancements." From the results of website survey, we found that only 332 agencies and local governments delivered original information on infectious disease jurisdictional districts, namely 116 public health centers, 108 medical associations, and 51 local governments. The spatial scale of surveillance generally corresponded to the jurisdictions of agencies and local governments; however, medical associations are provided at various levels, such as the county and city medical association level, municipal district level, public school district level, chome and aza (block) level, school facility level, and hospital and clinic levels. Among a total of 56 agencies and local governments conducting visualization, only few three were found to be using WebGIS. The semi-structured interviews revealed that the construction of local surveillance systems was necessary due to limitation of existing nationwide surveillance in rapid information delivery. Specialized agencies and local governments operating regional surveillance pointed out three problems of current nationwide infectious disease surveillance. First, the number of patients reported is limited because a few sentinel medical institutions report this kind of information. Second, use of the FAX information delivery system results in a time lag of two weeks for data aggregation and delivery. Third, because the minimum spatial unit is the public health center level, detailed information at the level under jurisdiction is not available. Such challenges arose amidst the crisis caused by the influenza A/H1N1pdm09 pandemic in 2009. In the aftermath of the failures of the 2009 epidemiological response, agencies and local governments cooperated with local government departments and the board of education to establish a system to collect information from all medical institutions and school facilities in the jurisdiction. Through visualizing such data as a disease map and sharing progress online, local surveillance has been effective in supporting medical consultations among local doctors, infection prevention at school facilities, and emergency response for night-time medical treatment, among other interventions. Local surveillance is informed by data in the jurisdiction of each specialized agency and local government; however, disease maps utilizing these information are maintained by 56 agencies and local governments. To construct the information delivery system on the local scale, we recommend the promotion of cooperation between agencies and local government departments and the strengthening of interactions between medical professionals, and mapping and GIS experts. In addition, it is evident that disease maps are effective for medical consultation support and infection control measures in familiar areas. From this perspective, local-scale disease maps have potential to be utilized as a communication tool for the sharing of risk management information between medical profession and residents. [ABSTRACT FROM AUTHOR]