Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Aggregates Classification01:29

Aggregates Classification

321
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
321
Classification of Systems-II01:31

Classification of Systems-II

146
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
146
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

186
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
186
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K

You might also read

Related Articles

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

Sort by
Same author

An interpretable radiomics-machine learning model for early risk stratification of invasive fungal infections in community-acquired pneumonia: a dual-center study.

Scientific reports·2026
Same author

Post-pandemic shocks and non-suicidal self-injury risk in working-age adults: differential pathways via psychological distress and social support.

BMC public health·2026
Same author

Comparative effectiveness of non-pharmacological interventions for social anxiety disorder in adults: a systematic review and meta-analysis.

Frontiers in psychology·2026
Same author

Comparative effects of non-pharmacological interventions on anxiety, depression and quality of life in individuals with substance use disorders: A systematic review and network meta-analysis.

Journal of affective disorders·2026
Same author

Dual induction of tissue-resident T and B cell immunity for broad influenza protection with a nanofiber-VLP vaccine.

Journal of nanobiotechnology·2026
Same author

Key Methodologies in Characterizing the Multi-Scale Structures of Gluten Proteins in Dough: A Comparative Review.

Biomolecules·2026
Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026
Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026
Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026
Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026
Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026
Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026
See all related articles

Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

1.0K

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.

Xiaohui Yao1,2, Xiaohan Jiang1, Haoran Luo1,2

  • 1Qingdao Innovation and Development Center, Harbin Engineering University, 1777 Sansha Rd, Qingdao, 266000, Shandong, China.

Biodata Mining
|March 5, 2024
PubMed
Summary
This summary is machine-generated.

We developed a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT) for complex disease analysis. MOCAT significantly improves disease classification and biomarker discovery by leveraging multi-layer biological data.

Keywords:
Attention mechanismAutoencoderAuxiliary classifierDisease predictionMulti-omics integrationTrustworthy learning

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.4K

Related Experiment Videos

Last Updated: Jul 1, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

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

Published on: December 15, 2023

1.0K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.4K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Integrating multi-omics data is crucial for understanding complex diseases.
  • High-dimensional and heterogeneous datasets require innovative computational methods.

Purpose of the Study:

  • To propose a novel computational framework for comprehensive multi-omics data integration.
  • To enhance disease classification and biomarker discovery using advanced deep learning techniques.

Main Methods:

  • Developed a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT).
  • Incorporated attention mechanisms with confidence learning for improved feature representation and trustworthy prediction.

Main Results:

  • MOCAT demonstrated superior performance on four benchmark datasets (BRCA, ROSMAP, LGG, KIPAN).
  • The model consistently surpassed state-of-the-art methods in evaluation measurements.
  • Ablation studies confirmed the effectiveness of auxiliary classifiers and attention mechanisms.

Conclusions:

  • The MOCAT framework is a robust tool for analyzing multi-layer biological data, excelling in disease classification and biomarker discovery.
  • This study underscores the importance of sophisticated deep learning methodologies for dissecting complex disease phenotypes and improving predictive accuracy.