Automated Identification of Novel Therapeutic Targets for Pancreatic Cancer via CRISPR-Enhanced Transcriptomic Profiling and Machine Learning Integration
Year: 2025
Type: article
Abstract: **Abstract:** This research presents a novel framework for identifying effective therapeutic targets for pancreatic cancer by combining CRISPR-based gene knockout screening with high-throughput transcriptomic analysis and a cascade of machine learning algorithms. The process automates target identification, circumventing traditional bottlenecks in drug discovery and offering a significantly accelerated route from genetic screening to clinical candidate validation. This approach promises to deliver more effective therapeutics and address the dismal survival rates associated with pancreatic cancer. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*
Source: Figshare
Authors: KYUNGJUN, LIM, Researcher, Freederia AI
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6902376458
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