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

Next-generation Sequencing

Next-generation Sequencing

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....

Multi-species Conserved Sequences

Multi-species Conserved Sequences

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...

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Step-Growth Polymerization: Overview

Step-Growth Polymerization: Overview

Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...

Multi-pass Transmembrane Proteins and β-barrels

Multi-pass Transmembrane Proteins and β-barrels

In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...

You might also read

Related Articles

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

Sort by

Same author

Rhs NADase effectors and their immunity proteins are exchangeable mediators of inter-bacterial competition in Serratia.

Nature communications·2023

Same author

Deep Learning for FAST Quality Assessment.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2022

Same author

Magnetic resonance imaging response in patients treated with definitive radiation therapy for medically inoperable endometrial cancer-Does it predict treatment response?

Brachytherapy·2019

Same author

Hysterectomy Pathway as the Global Engine of Practice Change: Implications for Value in Care.

Central Asian journal of global health·2017

Same author

Image-guided tandem and cylinder brachytherapy as monotherapy for definitive treatment of inoperable endometrial carcinoma.

Gynecologic oncology·2017

Same author

Advances in Neural Networks and Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering Applications.

Computational intelligence and neuroscience·2016

Search research articles

Related Experiment Video

Updated: May 24, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Adaptive Multistep Prediction With Sequence-to-Sequence (Seq2Seq) Models.

Joseph Kelley, Martin Hagan

IEEE Transactions on Neural Networks and Learning Systems

|March 3, 2025

Summary

The sequence-to-sequence (Seq2Seq) model acts as a fixed-weight adaptive predictor, adjusting to changing data without weight updates. Its embedded learning algorithm within the recurrent neural network decoder enables this adaptation.

More Related Videos

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

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

Related Experiment Videos

Last Updated: May 24, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

Area of Science:

Machine Learning
Artificial Intelligence
Time Series Analysis

Background:

Traditional predictive models often require frequent retraining to adapt to evolving data patterns.
Adaptive prediction is crucial for accurately forecasting systems with time-varying dynamics.

Purpose of the Study:

To demonstrate that sequence-to-sequence (Seq2Seq) models function as adaptive multistep predictors.
To investigate the mechanism enabling Seq2Seq models to adapt to time-varying behaviors without weight updates.

Main Methods:

Utilizing a fixed-weight adaptive approach where learning is embedded within the recurrent neural network (RNN) decoder.
Testing the Seq2Seq model's adaptive capabilities using both simulated and experimental datasets.

Main Results:

The Seq2Seq model exhibits adaptive multistep prediction capabilities.
The study identifies a specific internal mechanism responsible for the model's adaptation to time-varying behaviors.

Conclusions:

Seq2Seq models offer a novel approach to adaptive prediction, reducing the need for constant weight and bias adjustments.
The embedded learning algorithm in the RNN decoder is key to the model's ability to handle dynamic systems.