scholarly journals Computer-assisted initial diagnosis of rare diseases

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2211 ◽  
Author(s):  
Rui Alves ◽  
Marc Piñol ◽  
Jordi Vilaplana ◽  
Ivan Teixidó ◽  
Joaquim Cruz ◽  
...  
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Large Scale ◽  
Initial Diagnosis ◽  
Computer Assisted ◽  
Web Interface ◽  
Genetic Origin ◽  
Data Set ◽  
User Friendly ◽  
Current Database

Introduction.Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype.Methods.Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis.Results.We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms.Discussion.The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed athttp://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded fromhttps://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.

scholarly journals The Microbe Directory: An annotated, searchable inventory of microbes’ characteristics

2018 ◽  
Vol 2 ◽  
pp. 3 ◽  
Author(s):  
Heba Shaaban ◽  
David A. Westfall ◽  
Rawhi Mohammad ◽  
David Danko ◽  
Daniela Bezdan ◽  
...  
Keyword(s):  
Biofilm Formation ◽  
Large Scale ◽  
Research Effort ◽  
Gram Stain ◽  
Web Interface ◽  
Ongoing Effort ◽  
Student Researchers ◽  
User Friendly ◽  
Optimal Ph ◽  
Online Web

The Microbe Directory is a collective research effort to profile and annotate more than 7,500 unique microbial species from the MetaPhlAn2 database that includes bacteria, archaea, viruses, fungi, and protozoa. By collecting and summarizing data on various microbes’ characteristics, the project comprises a database that can be used downstream of large-scale metagenomic taxonomic analyses, allowing one to interpret and explore their taxonomic classifications to have a deeper understanding of the microbial ecosystem they are studying. Such characteristics include, but are not limited to: optimal pH, optimal temperature, Gram stain, biofilm-formation, spore-formation, antimicrobial resistance, and COGEM class risk rating. The database has been manually curated by trained student-researchers from Weill Cornell Medicine and CUNY—Hunter College, and its analysis remains an ongoing effort with open-source capabilities so others can contribute. Available in SQL, JSON, and CSV (i.e. Excel) formats, the Microbe Directory can be queried for the aforementioned parameters by a microorganism’s taxonomy. In addition to the raw database, The Microbe Directory has an online counterpart (https://microbe.directory/) that provides a user-friendly interface for storage, retrieval, and analysis into which other microbial database projects could be incorporated. The Microbe Directory was primarily designed to serve as a resource for researchers conducting metagenomic analyses, but its online web interface should also prove useful to any individual who wishes to learn more about any particular microbe.

scholarly journals Comprehensive comparison of large-scale tissue expression datasets

2015 ◽  
Author(s):  
Alberto Santos ◽  
Kalliopi Tsafou ◽  
Christian Stolte ◽  
Sune Pletscher-Frankild ◽  
Seán I O’Donoghue ◽  
...  
Keyword(s):  
Large Scale ◽  
Comprehensive Evaluation ◽  
Tissue Expression ◽  
Expression Data ◽  
Web Interface ◽  
Comprehensive Comparison ◽  
Single User ◽  
The Right ◽  
User Friendly ◽  
Literature Curation

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource ( http://tissues.jensenlab.org ), which makes all the scored and integrated data available through a single user-friendly web interface.

scholarly journals The use of machine learning in rare diseases: a scoping review

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Julia Schaefer ◽  
Moritz Lehne ◽  
Josef Schepers ◽  
Fabian Prasser ◽  
Sylvia Thun
Keyword(s):  
Machine Learning ◽  
Rare Disease ◽  
Rare Diseases ◽  
Scoping Review ◽  
Input Data ◽  
Skin Diseases ◽  
Demographic Data ◽  
Support Vector ◽  
Research Activity ◽  
Data Set

Abstract Background Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. Methods Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010–2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. Results Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and “omics” data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). Conclusion Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

scholarly journals A Large-Scale Semantic Analysis of Verbal Fluency Across the Aging Spectrum: Data From the Canadian Longitudinal Study on Aging

2019 ◽  
Vol 75 (9) ◽  
pp. e221-e230 ◽  
Author(s):  
Vanessa Taler ◽  
Brendan T Johns ◽  
Michael N Jones
Keyword(s):  
Longitudinal Study ◽  
Verbal Fluency ◽  
Large Scale ◽  
Computational Models ◽  
Semantic Analysis ◽  
Computer Assisted ◽  
Data Set ◽  
Standard Assessment ◽  
Using Data ◽  
Fluency Task

Abstract Objectives The present study aimed to characterize changes in verbal fluency performance across the lifespan using data from the Canadian Longitudinal Study on Aging (CLSA). Methods We examined verbal fluency performance in a large sample of adults aged 45–85 (n = 12,686). Data are from the Tracking cohort of the CLSA. Participants completed a computer-assisted telephone interview that included an animal fluency task, in which they were asked to name as many animals as they could in 1 min. We employed a computational modeling approach to examine the factors driving performance on this task. Results We found that the sequence of items produced was best predicted by their semantic neighborhood, and that pairwise similarity accounted for most of the variance in participant analyses. Moreover, the total number of items produced declined slightly with age, and older participants produced items of higher frequency and denser semantic neighborhood than younger adults. Discussion These findings indicate subtle changes in the way people perform this task as they age. The use of computational models allowed for a large increase in the amount of variance accounted for in this data set over standard assessment types, providing important theoretical insights into the aging process.

scholarly journals How to design a registry for undiagnosed patients in the framework of rare disease diagnosis – suggestions on software, data set and coding system

2021 ◽  
Author(s):  
Alexandra Berger ◽  
Anne-Kathrin Rustemeier ◽  
Jens Göbel ◽  
Dennis Kadioglu ◽  
Vanessa Britz ◽  
...  
Keyword(s):  
Open Source ◽  
Rare Disease ◽  
Rare Diseases ◽  
Action Plan ◽  
Correct Diagnosis ◽  
Disease Patient ◽  
Disease Diagnosis ◽  
Clinical Findings ◽  
Snomed Ct ◽  
Data Set

Abstract Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software OSSE (Open-Source Registry System for Rare Diseases) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some patients.

scholarly journals Comprehensive comparison of large-scale tissue expression datasets

2015 ◽  
Author(s):  
Alberto Santos ◽  
Kalliopi Tsafou ◽  
Christian Stolte ◽  
Sune Pletscher-Frankild ◽  
Seán I O’Donoghue ◽  
...  
Keyword(s):  
Large Scale ◽  
Comprehensive Evaluation ◽  
Tissue Expression ◽  
Expression Data ◽  
Web Interface ◽  
Comprehensive Comparison ◽  
Single User ◽  
The Right ◽  
User Friendly ◽  
Literature Curation

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource ( http://tissues.jensenlab.org ), which makes all the scored and integrated data available through a single user-friendly web interface.

scholarly journals A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research

2014 ◽  
Vol 22 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Rémy Choquet ◽  
Meriem Maaroufi ◽  
Albane de Carrara ◽  
Claude Messiaen ◽  
Emmanuel Luigi ◽  
...  
Keyword(s):  
Data Collection ◽  
Rare Disease ◽  
Rare Diseases ◽  
Care Coordination ◽  
Minimum Data Set ◽  
National Plan ◽  
Data Set ◽  
Common Data Elements ◽  
Minimum Data ◽  
Data Elements

Abstract Background Although rare disease patients make up approximately 6–8% of all patients in Europe, it is often difficult to find the necessary expertise for diagnosis and care and the patient numbers needed for rare disease research. The second French National Plan for Rare Diseases highlighted the necessity for better care coordination and epidemiology for rare diseases. A clinical data standard for normalization and exchange of rare disease patient data was proposed. The original methodology used to build the French national minimum data set (F-MDS-RD) common to the 131 expert rare disease centers is presented. Methods To encourage consensus at a national level for homogeneous data collection at the point of care for rare disease patients, we first identified four national expert groups. We reviewed the scientific literature for rare disease common data elements (CDEs) in order to build the first version of the F-MDS-RD. The French rare disease expert centers validated the data elements (DEs). The resulting F-MDS-RD was reviewed and approved by the National Plan Strategic Committee. It was then represented in an HL7 electronic format to maximize interoperability with electronic health records. Results The F-MDS-RD is composed of 58 DEs in six categories: patient, family history, encounter, condition, medication, and questionnaire. It is HL7 compatible and can use various ontologies for diagnosis or sign encoding. The F-MDS-RD was aligned with other CDE initiatives for rare diseases, thus facilitating potential interconnections between rare disease registries. Conclusions The French F-MDS-RD was defined through national consensus. It can foster better care coordination and facilitate determining rare disease patients’ eligibility for research studies, trials, or cohorts. Since other countries will need to develop their own standards for rare disease data collection, they might benefit from the methods presented here.

Behind the Scene of “The Holy Family with St. Anne and the Young St. John” by Bernardino Luini: A Computer-Assisted Method to Unveil the Underdrawings

2020 ◽  
pp. 000370282094992
Author(s):  
Michele Caccia ◽  
Letizia Bonizzoni ◽  
Marco Martini ◽  
Raffaella Fontana ◽  
Valeria Villa ◽  
...  
Keyword(s):  
Image Data ◽  
Principal Component ◽  
Computer Assisted ◽  
Data Set ◽  
Widespread Application ◽  
Technological Advances ◽  
Straightforward Method ◽  
Handling Methods ◽  
User Friendly ◽  
Infrared Reflectography

Uncovering the underdrawings (UDs), the preliminary sketch made by the painter on the grounded preparatory support, is a keystone for understanding the painting's history including the original project of the artist, the pentimenti (an underlying image in a painting providing evidence of revision by the artist) or the possible presence of co-workers’ contributions. The application of infrared reflectography (IRR) has made the dream of discovering the UDs come true: since its introduction, there has been a growing interest in the technology, which therefore has evolved leading to advanced instruments. Most of the literature either report on the technological advances in IRR devices or present case studies, but a straightforward method to improve the visibility of the UDs has not been presented yet. Most of the data handling methods are devoted to a specific painting or they are not user-friendly enough to be applied by non-specialized users, hampering, thus, their widespread application in areas other than the scientific one, e.g., in the art history field. We developed a computer-assisted method, based on principal component analysis (PCA) and image processing, to enhance the visibility of UDs and to support the art-historians and curators’ work. Based on ImageJ/Fiji, one of the most widespread image analysis software, the algorithm is very easy to use and, in principle, can be applied to any multi- or hyper-spectral image data set. In the present paper, after describing the method, we accurately present the extraction of the UD for the panel “The Holy Family with St. Anne and the Young St. John” and for other four paintings by Luini and his workshop paying particular attention to the painting known as “The Child with the Lamb”.

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