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Updated: Aug 18, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Emati: a recommender system for biomedical literature based on supervised learning.

Özge Kart^1,2, Alexandre Mestiashvili¹, Kurt Lachmann¹

¹Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, Tatzberg 47-49, Dresden 01307, Germany.

Database : the Journal of Biological Databases and Curation

|December 9, 2022

Summary

The Emati system offers personalized scientific article recommendations using machine learning, addressing the challenge of rapidly growing research literature. It provides weekly updates via email and a personalized search function for services like PubMed and arXiv.

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Last Updated: Aug 18, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computer Science
Bioinformatics
Information Science

Background:

The exponential growth of scientific literature presents a significant challenge for researchers seeking to stay current.
Effective methods are needed to filter and recommend relevant new publications.

Purpose of the Study:

To develop Emati, a web-based recommender system for scientific articles.
To provide personalized, up-to-date literature recommendations to users.

Main Methods:

A content-based approach was adopted, independent of user numbers.
Two supervised machine learning models were implemented: Naïve Bayes with TF-IDF and fine-tuned BERT.
The system generates weekly recommendations sorted by probability scores.

Main Results:

The study successfully developed and implemented the Emati system.
Both TF-IDF and BERT models were utilized for article classification.
Emati offers personalized search capabilities across platforms like PubMed and arXiv.

Conclusions:

Emati provides a viable solution for navigating the expanding scientific literature.
The system enhances user experience by delivering tailored article recommendations.
Personalized search and email updates improve access to relevant research.