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// INFINITY INFOSOFT

Medical research software

Infinity InfoSoft executed a multifaceted medical research software upgrade for an ontology provider, incorporating AI-driven search, custom data dashboards, and ontology integration into a chemical research company’s infrastructure.

Client

Our client specializes in the development of technologies that extract information from structured and unstructured data, transforming it into knowledge for research, discoveries, and decision-making. Their expertise spans across chemistry, biology, and related scientific fields. They own a vast ontology system, a structured framework of interconnected scientific terms and concepts.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Enhancing the search system in medical research software

Our team focused on developing and enhancing a specialized AI-powered search system – a key subsystem within a larger framework, designed for web and mobile interfaces. This task involved multiple technical and functional improvements:

  • Advanced document search: We enabled the system to conduct in-depth searches across various document formats from a vast document repository. The system allowed users to locate documents, view internal and external sources, and identify key scientific concepts and annotations highlighted within these documents.
  • Annotation and categorization: A critical feature was the ability for users to select specific words or tickets within documents and assign them to relevant domains for annotation. We integrated GPT-based AI features to guide users in correctly annotating and categorizing each term or entity.
  • Submission and review process: Upon making edits or adding new information to a document, the system facilitated a review process. Users could submit these changes, which would then be sent to an administrator for status assignment and approval of the new annotations, comments, or categorizations.
  • Query and analysis features: Users now are able to select documents from a large database and add them to a collective basket. They can then query these documents using the search bar in the Analyzer, asking specific questions or requesting summaries and analyses based on GPT technology.
  • Custom filter development: Our developer created sophisticated filters for document search, tailored to various source types.
  • Document viewer challenges: One of the complex tasks was developing a document viewer capable of displaying marked annotations on PDF documents. This required intricate back-end coordination to overlay annotations correctly.
  • Legacy code and architecture overhaul: We addressed the challenges presented by legacy code and lack of architectural structure, ensuring the system was built on solid, modern technological foundations.
  • Integration of multiple GPT versions: Our team enhanced the system with multiple versions of GPT (3.5, 4, Davinci), enabling more versatile document analysis.
  • LLM integration: We focused on custom LLM development that allows users to enter queries in natural language. Once the queries have been converted into back-end requests, they can be sent to the server.

Data science dashboard automation

Our data science team focused on automating data visualization through dashboards, a crucial component for the client’s research in identifying molecular targets for new pharmaceutical treatments. The primary diseases under study included obesity and muscle diseases.

  • Dashboard creation: The team’s objective was to create dashboards for visualizing pharmaceutical data. This involved processing large datasets, which are a vast number of annotated medical articles with unique ID and metadata, to form sizable GBQ tables. 

  • Data visualization: Using Looker Studio, we transformed these large data tables into smaller, more manageable formats for dashboard creation. This visualization step was necessary for experts to better review and filter data.

  • Dashboard automation: Post-approval by medical experts, we automated the dashboard creation using data engineering techniques. This involved using repositories containing SQL scripts to fetch required information. These scripts were scheduled to run at specific intervals, ensuring the dashboards remained up-to-date with the latest research findings.

  • Continuous updates and integration: Our solution allowed for the continuous integration of new relevant publications into the dashboards. This dynamic updating process was facilitated by Google Cloud Functions. It kept dashboards updated with the most recent data.

  • Query management: We handled queries through large tables, pulling out specific information based on search queries. The team then visualized these statistics in the dashboards and identified any issues in the search queries.

Ontology integration in chemical research

Our project focused on integrating our client’s ontologies into an established lab management software at a chemical research company. This task involved several key steps to modernize and automate their outdated system:

  • System analysis and research: We began with a thorough analysis and research of the client’s legacy system. This lab management software, primarily used for storing reports and research findings, was based on older Java versions and JSP technologies.
  • Developing new interface and back end: Our approach included developing a new interface and back-end system to automate the process of document analysis and database updating, which was previously done manually.
  • User types and functionalities: We designed the system to cater to two distinct user types:
  • Document uploader: Researchers who add documents to the system. After a document is annotated, it appears on a special page where the uploader can review the results, make edits, and confirm submission to the database.
  • Administrator: Responsible for reviewing and confirming document additions. This role involves a comprehensive review of documents, with the ability to edit, approve, or make changes before final database entry.
  • Back-end development and legacy code overhaul: Our developer undertook the task of overhauling the existing legacy code. This involved writing JSP pages according to client specifications and developing back-end functionalities (requests, responses, data processing, and database entry).
  • Admin interface development: We also developed an admin part of the system where the admin (typically the head of the research department) receives a notification with a link to the interface displaying information from the database.
  • Integration of ontology API: The core of our solution was integrating the Ontology API into the client’s lab management software. This API served as a point for sending document-related queries and receiving responses, which were then processed and displayed through the frontend before being sent to the client’s database.
  • Handling documents and data: In this system, documents uploaded to the ontological system were processed, and the resulting data was saved in the company’s chemical research database. This allowed for automatic analysis of documents and retrieval of important chemical compound information.
  • Full-stack development: Our developer worked as a full-stack engineer, dealing with both front-end and back-end aspects and ensuring seamless integration of all system components.

Technologies

Programming languages

JavaScript, TypeScript, Java

Front-end

React, react-pdf, Redux, Redux-thunk, React-redux, Primereact, SASS, Lodash, Axios, FileSaver, GPT-Tokenizer

Back-end

Spring boot, Java with Lucene Libraries, Stardog

Data Science and analytics

Python (Pandas, Numpy, Plotly, Matplotlib), GCP (Google Big Query, Google Cloud Storage, Cloud Run), Looker, Data Studio, Apache Solr, custom tools for data processing and visualization

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  • ✅ Innovation-Driven Culture: Constantly evolving to keep you ahead of the curve.

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