Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Aug 14, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Discovering research articles containing evolutionary timetrees by machine learning.

Marija Stanojevic¹, Jovan Andjelkovic¹, Adrienne Kasprowicz²

¹Center for Data Analytics and Biomedical Informatics, Computer and Information Science Department, Temple University, Philadelphia, PA 19121, USA.

Bioinformatics (Oxford, England)

|January 17, 2023

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Tiny Subsamples and Upsampling Tame Big Data Evolutionary Analysis in Phylogenomics.

bioRxiv : the preprint server for biology·2026

Same author

Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.

Healthcare (Basel, Switzerland)·2026

Same author

GeoFusion-3D: Multi-Scale Geomorphic Feature Fusion for Landslide Scar Detection Using UAV-Mounted LiDAR.

Sensors (Basel, Switzerland)·2026

Same author

The genetic foundations of convergent traits.

Nature reviews. Genetics·2026

Same author

An Evolutionary Metric for Estimating PhyloAges from Bulk Sequencing of Hematopoietic Stem Cells Reveals the Tempo of Blood Aging in Cancer and Longevity.

Journal of molecular evolution·2025

Same author

Completing a molecular timetree of Afrotheria.

Frontiers in bioinformatics·2025

Same journal

Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Bioinformatics (Oxford, England)·2026

Same journal

Quantifying domain-specific relevance of computational biology Wikipedia articles using TF-IDF and cosine similarity.

Bioinformatics (Oxford, England)·2026

Same journal

GATSBI: improving context-aware protein embeddings through biologically motivated data splits.

Bioinformatics (Oxford, England)·2026

Same journal

BiMba: using Vision Mamba to predict protein sites that bind other proteins.

Bioinformatics (Oxford, England)·2026

Same journal

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

Bioinformatics (Oxford, England)·2026

Same journal

Is a Win-Win possible? Achieving pareto-optimal privacy-utility balance in fine-tuned genome language model embeddings against embedding reconstruction attacks.

Bioinformatics (Oxford, England)·2026

See all related articles

This summary is machine-generated.

We developed an automated system using machine learning to find evolutionary timetrees in research articles, significantly improving discovery efficiency. This tool automates the curation of the TimeTree of Life database, doubling accessible knowledge.

Area of Science:

Evolutionary biology
Bioinformatics
Computational phylogenetics

Background:

Timetrees illustrate species evolutionary relationships and divergence times.
Manual curation of timetrees from scientific literature is labor-intensive and costly.
The TimeTree project has curated timetrees for two decades via timetree.org.

Purpose of the Study:

To develop and optimize automated text-mining methods for discovering research articles containing evolutionary timetrees.
To enhance the efficiency and scalability of the TimeTree of Life database curation process.

Main Methods:

Implemented an optimized machine learning system, including BERT classification, for timetree identification.
Fine-tuned models on whole-text articles and subsequently on figure-related excerpts.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

Related Experiment Videos

Last Updated: Aug 14, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

Developed the TimeTreeFinder (TTF) tool for automated processing of millions of articles.

Main Results:

Achieved an F1 score of 0.88 by text-mining article excerpts, an improvement over whole-text BERT classification (F1 score 0.67).
The TimeTreeFinder (TTF) tool is estimated to double the number of discovered timetree-containing articles compared to manual methods.
Demonstrated 87% precision on recently published, out-of-distribution articles.

Conclusions:

Automated text-mining significantly accelerates the discovery and curation of evolutionary timetrees.
The TTF tool reduces human and time costs, potentially doubling the knowledge accessible in the TimeTree database.
This automation facilitates faster updates and broader accessibility of evolutionary data.