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

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by

Same author

Multimodal feature fusion for molecular property classification.

Journal of cheminformatics·2026

Same author

Accurate identification of cytochrome P450 proteins using multimodal integration of protein language models.

International journal of biological macromolecules·2026

Same author

Danthron as a novel IL-6R agonist promotes thrombopoiesis via the SRC/RAS/MAPK pathway.

Frontiers in immunology·2026

Same author

Epigallocatechin promotes megakaryocyte differentiation via ERα-dependent SRC/PI3K/AKT signaling for thrombocytopenia treatment.

Journal of ethnopharmacology·2026

Same author

Development of a machine learning-based nomogram for predicting atherosclerosis risk in patients with systemic lupus erythematosus.

Clinical and experimental rheumatology·2026

Same author

Prediction of cyclin-dependent proteins using pre-trained protein language models and effective neural network architectures.

Analytical biochemistry·2026

Same journal

Topological skeleton analysis for network-based shape representation in biology and beyond.

iScience·2026

Same journal

Condition-specific neural signatures of reactivation during post-retrieval rest: An EEG study.

iScience·2026

Same journal

Multi-chaotic signal identification employing a causal cross-correlation neural network.

iScience·2026

Same journal

Repeated insertions at positions 261-280 in KPC-2 highlight a ceftazidime-avibactam resistance hotspot.

iScience·2026

Same journal

ROS inhibits microtubule dynamics and cell growth heterogeneity during Arabidopsis sepal morphogenesis.

iScience·2026

Same journal

Type 1 diabetes alters early macrophage-<i>Mycobacterium tuberculosis</i> transcriptional coordination during infection.

iScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 20, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using

Runyu Jing¹, Li Xue², Menglong Li³

¹School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, Sichuan, China.

|November 25, 2022

Summary

This summary is machine-generated.

Interpreting deep learning models remains challenging. The new layerUMAP tool visualizes hidden layer outputs, offering insights to improve biological data analysis and model development.

Keywords:

Artificial intelligence applications Bioinformatics Genomics Systems biology Transcriptomics

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Aug 20, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Computational Biology
Machine Learning
Bioinformatics

Background:

Deep learning models excel at classification and prediction but lack interpretability.
Explaining the predictions of complex deep learning models is a significant challenge in various scientific domains.

Purpose of the Study:

To introduce layerUMAP, a user-friendly command-line tool for visualizing and analyzing deep learning representations.
To address the challenge of interpreting high-level feature representations learned by deep learning models in biological contexts.

Main Methods:

Integration of autoBioSeqpy software with the UMAP (Uniform Manifold Approximation and Projection) library.
Development of an interactive command-line interface for visualizing outputs from hidden layers within deep learning models.

Main Results:

Demonstrated the capability of layerUMAP to provide insightful visual feedback on model behavior.
Illustrated the tool's utility with two distinct biological observation datasets.
Showcased how layerUMAP can guide the development of more effective deep learning models.

Conclusions:

layerUMAP offers an accessible method for understanding deep learning model internals.
The tool facilitates the analysis of learned representations, aiding in the development of superior biological models.
Interactive visualization of hidden layer outputs enhances model interpretability and development.