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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Updated: Dec 20, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Developing a Search Engine for Precision Medicine.

Samuel J Shenoi¹, Vi Ly², Sarvesh Soni³

¹Baylor University, Waco, Texas.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 2, 2020

Summary

This summary is machine-generated.

Precision medicine aims to personalize cancer treatments. The PRIMROSE search engine helps oncologists find relevant research and clinical trials by analyzing patient data, improving information retrieval for better patient care.

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Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

Area of Science:

Oncology
Bioinformatics
Medical Informatics

Background:

Precision medicine advances cancer treatment by tailoring therapies to individual genetic, environmental, and lifestyle factors.
Oncologists face challenges in accessing specific patient information due to the vast amount of medical data.
Efficient retrieval of relevant scientific articles and clinical trials is crucial for informed treatment decisions.

Purpose of the Study:

To develop the PRecIsion Medicine Robust Oncology Search Engine (PRIMROSE) for cancer patients.
To enhance information retrieval for oncologists by integrating patient-specific data.
To improve the accessibility and usability of precision oncology resources.

Main Methods:

Utilized Elasticsearch for efficient data storage and retrieval of scientific articles and clinical trials.
Developed a knowledge graph to expand search queries and improve information recall.
Experimented with machine learning and learning-to-rank models to optimize search relevance.
Created a ReactJS front-facing website and a REST API for user accessibility.

Main Results:

The PRIMROSE search engine effectively retrieves scientific literature and clinical trials based on detailed patient profiles.
Knowledge graph integration enhanced search recall, providing more comprehensive results.
Comparison of machine learning and learning-to-rank approaches informed further search engine optimization.
A user-friendly interface was developed for seamless access by healthcare providers.

Conclusions:

PRIMROSE offers a robust solution for navigating the complexities of precision oncology information.
The system facilitates better access to relevant research, supporting evidence-based clinical decision-making.
Integrating advanced search technologies improves the application of precision medicine in cancer care.