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

Antiarrhythmic Drugs: Class II Agents as β-Adrenergic Blockers01:24

Antiarrhythmic Drugs: Class II Agents as β-Adrenergic Blockers

2.0K
Adrenergic stimulation generally impacts cardiac rate and rhythm. Specifically, stimulation of the β-adrenoceptors triggers an increase in intracellular calcium ion influx and pacemaker currents, which may cause arrhythmias. Catecholamines like adrenaline also demonstrate β2-adrenoceptor-mediated hypokalemia, impacting cardiac action potential and disrupting the normal cardiac rhythm. Class II antiarrhythmic drugs are β-adrenoceptor antagonists or β-blockers, which...
2.0K
Convolution Properties II01:17

Convolution Properties II

600
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
600
Convolution Properties I01:20

Convolution Properties I

627
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
627
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.3K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.3K
Drug Classes and Categories01:25

Drug Classes and Categories

3.1K
Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Untargeted Metabolomics of Dairy Cows as Influenced by the Combinations of Essential Oil Blends and Fumaric Acid as Natural Feed Additives Using RUSITEC.

Metabolites·2025
Same author

Graph Neural Network-Based Approaches for Protein Function Prediction.

Methods in molecular biology (Clifton, N.J.)·2025
Same author

Multitask Learning-Based Approaches for Protein Function Prediction.

Methods in molecular biology (Clifton, N.J.)·2025
Same author

A Survey of Pretrained Protein Language Models.

Methods in molecular biology (Clifton, N.J.)·2025
Same author

Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein-Protein Interaction Analysis.

Methods in molecular biology (Clifton, N.J.)·2025
Same author

Large Language Model (LLM)-Based Advances in Prediction of Post-translational Modification Sites in Proteins.

Methods in molecular biology (Clifton, N.J.)·2025

Related Experiment Video

Updated: Feb 16, 2026

The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions
08:06

The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions

Published on: February 1, 2018

9.6K

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

Clarence White1, Hamid D Ismail1, Hiroto Saigo2

  • 1Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, 27411, USA.

BMC Bioinformatics
|January 4, 2018
PubMed
Summary

A new deep learning tool, CNN-BLPred, accurately classifies beta-lactamase (BL) enzymes, improving bacterial antibiotic resistance prediction. This computational approach enhances classification accuracy for key BL enzyme classes.

Keywords:
Beta lactamase protein classificationConvolutional neural networkDeep learningFeature selection

More Related Videos

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

892
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Related Experiment Videos

Last Updated: Feb 16, 2026

The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions
08:06

The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions

Published on: February 1, 2018

9.6K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

892
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Enzymology

Background:

  • Beta-lactamase (BL) enzymes are crucial in bacterial antibiotic resistance.
  • The increasing number of identified BL enzymes necessitates advanced classification tools.
  • Existing computational methods for BL enzyme classification are inadequate, particularly for functional classification.

Purpose of the Study:

  • To develop a novel computational tool for classifying beta-lactamase (BL) enzymes.
  • To address the limitations of existing methods in BL enzyme classification.
  • To improve the accuracy and efficiency of BL enzyme classification.

Main Methods:

  • Implemented a Deep Learning approach using Convolutional Neural Network (CNN).
  • Developed CNN-BLPred, incorporating Gradient Boosted Feature Selection (GBFS) for optimal feature identification.
  • Utilized feature selection to reduce a large feature set by approximately 95%.

Main Results:

  • CNN-BLPred demonstrated superior performance compared to existing algorithms in rigorous benchmarking.
  • A simple CNN architecture with one convolutional layer yielded the best results.
  • Achieved a 7% increase in overall BL prediction accuracy and an average 25.64% increase for Classes A, B, C, and D during cross-validation.

Conclusions:

  • CNN-BLPred, a deep learning classifier, significantly enhances beta-lactamase (BL) classification.
  • The approach combines CNN with effective feature selection and data balancing techniques (ROS, RUS, SMOTE).
  • CNN-BLPred offers a more accurate and reliable method for classifying BL enzymes compared to current algorithms.