Jove
Visualize
Contact Us

Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

107
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
107
RNA-seq03:21

RNA-seq

10.0K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.0K
DNA Microarrays02:34

DNA Microarrays

17.5K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
17.5K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.9K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.9K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

78
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
78
Next-generation Sequencing03:00

Next-generation Sequencing

89.0K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
89.0K

You might also read

Related Articles

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

Sort by
Same author

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.

Nature communications·2025
Same author

Privacy-preserving AUC computation in distributed machine learning with PHT-meDIC.

PLOS digital health·2025
Same author

Target leakage and the use of diagnostic variables in diabetes prediction models.

Nutrition & diabetes·2025
Same author

Privacy-preserving federated unsupervised domain adaptation with application to age prediction from DNA methylation data.

Bioinformatics (Oxford, England)·2025
Same author

Long-term impact of the SARS-CoV-2 pandemic on respiratory viruses in Germany.

BMC public health·2025
Same author

Correction: ENABLE-App-Based Digital Capture and Intervention of Patient-Reported Quality of Life, Adverse Events, and Treatment Satisfaction in Breast Cancer: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2025
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 Experiment Video

Updated: Jul 13, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

4.0K

Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data.

Jonas C Ditz1, Bernhard Reuter2, Nico Pfeifer3

  • 1Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, Tübingen, 72076, Germany. jonas.ditz@uni-tuebingen.de.

Scientific Reports
|October 11, 2023
PubMed
Summary

Convolutional Motif Kernel Networks offer interpretable artificial intelligence for healthcare. This approach provides biologically meaningful explanations for predictions, enhancing trust and integration in clinical settings.

More Related Videos

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

10.4K
Immunostaining for DNA Modifications: Computational Analysis of Confocal Images
09:42

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Published on: September 7, 2017

9.7K

Related Experiment Videos

Last Updated: Jul 13, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

4.0K
Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

10.4K
Immunostaining for DNA Modifications: Computational Analysis of Confocal Images
09:42

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Published on: September 7, 2017

9.7K

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • Artificial neural networks (ANNs) excel at identifying data correlations but often function as "black boxes," limiting scientific understanding and trust.
  • Interpretability is crucial for high-stakes fields like healthcare, enabling domain experts to validate and integrate AI predictions into clinical practice.

Purpose of the Study:

  • To introduce Convolutional Motif Kernel Networks (CMKNs), a novel neural network architecture designed for interpretable predictions.
  • To enable direct interpretation of prediction outcomes with biologically and medically meaningful explanations, eliminating the need for post-hoc analyses.

Main Methods:

  • Developed a neural network architecture learning feature representations in a reproducing kernel Hilbert space using a position-aware motif kernel function.
  • Employed an end-to-end learning scheme for direct extraction of biologically meaningful concepts from data.

Main Results:

  • Demonstrated robust learning capabilities on small datasets.
  • Achieved state-of-the-art performance on healthcare prediction tasks using DNA and protein sequences.
  • Showcased the model's ability to learn biologically meaningful concepts directly from data.

Conclusions:

  • CMKNs provide an interpretable alternative to traditional ANNs in healthcare and bioinformatics.
  • The model's direct interpretability and strong performance facilitate trust and adoption in clinical and research settings.
  • Applicable to biological sequence data (DNA, protein) for advancing medical and biological insights.