Gro matchmaking
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The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously-identified matches and generate several new leads currently being validated. The API is available at https: Rare genetic disorders collectively affect around million people worldwide, but the number of people affected by any one of these disorders can be extremely small.
Government Matchmaking - How to Catch Their Eye
Siloing of data severely impedes the discovery of genetic causes of these disorders, while directly copying such data across various resources is impossible due to a number of legal and privacy concerns. Developing efforts such as the Global Alliance for Genomics and Health GA4GH APIs are designed to facilitate the exchange of genetic data between such databases, however these are currently targeting genetic data and hypothesis-driven queries. The initial version of this API follows a query-by-example philosophy, in which the request is simply a description of the individual to be matched and the response is a list of the descriptions of similar individuals.
Because the API is built around the description of an individual rather than a complex query language, it is easy to understand, straightforward to implement, and provides the various databases the flexibility of experimenting with matching algorithms and regulating the amount of data that is disclosed. Further, because the case is used as the query, more specific and complete case records will return more relevant matches, thus encouraging users to submit the most complete and specific case information possible.
The sharing and automated analysis of genetic and phenotypic data has necessitated standardization using a number of ontologies and controlled terminologies.
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The HPO has over 11, terms corresponding to phenotypic abnormalities, which are structured from general e. Many MME partners perform some form of internal matchmaking to identify similar patients within their database, but each organization has a different focus, collects different types of data, and stores their data in different formats.
The MME API provides a standardized language for exchanging patient profiles in order to enable matchmaking between patient databases. Here we present a description of the MME API, the method used to authenticate endpoints of this API within the MME, and a test dataset available to verify that endpoints are behaving as expected and assist in the development of novel matching algorithms. An overview of the match request and response process is shown in Figure 1.
The user starts by contributing a case to one of the Matchmaker Exchange services Philippakis et al. These other services use the structured patient data in the query to identify and return descriptions of similar cases within their respective databases. They are not permitted to store request data for uses other than analytics and diagnostics i. Similar cases found through the API are then reported to the users for evaluation. The users can then follow up with each other on any promising matches using contact information provided with the query and response.
It is currently up to each MME service to define the process for alerting their respective users of the match i. Overview of the matchmaking process, in which 1 Alice deposits case P1 into Matchmaker A; 2 sometime later, Bob deposits a similar case P2 into Matchmaker B; 3a Matchmaker B then sends a match request with a description of P2 to Matchmaker A and 3b receives a match response with a description of similar patients including P1 from Matchmaker A; 4 Matchmaker A informs Alice and Matchmaker B informs Bob of the P1-P2 match; and 5 Alice and Bob communicate if the match warrants further investigation.
The API defines a set of data types, each with a corresponding set of properties e. An object is a particular example instantiation of a type an example Disorder object in JSON format is: A match request see Figure 2B contains a single case in this format, used as the query, and the match response contains a scored list of the most similar cases in the remote system, also in this format. There are few required fields, making it easy to implement regardless of the data stored by the matchmaker service, and many optional fields, enabling additional information to be conveyed to improve the accuracy of matchmaking and help users interpret the matches.
An example match request and response, based on a patient description in Hood et al. The Accept header specifies that the response should conform to version 1. The X-Auth-Token header is set to the secret token that b. B An example request body, describing a particular patient with Floating-Harbor Syndrome additional features omitted for brevity.
The Content-Type header specifies that the response conforms to version 1. D An example response body, containing a list of matching cases and corresponding match scores patient details and additional matches omitted for brevity. Example values from a patient description in Hood et al. Standardized identifiers and ontologies are used wherever possible.
Introduction
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