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

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.1K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

565
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
565
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.0K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

108
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
108
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
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

TerminatorNet: comprehensive identification of intrinsic transcription terminators in bacteria.

Bioinformatics (Oxford, England)·2026
Same author

Improving prediction of bacterial sRNA regulatory targets with expression data.

NAR genomics and bioinformatics·2025
Same author

Popcorn: prediction of short coding and noncoding genomic sequences in prokaryotes.

Bioinformatics (Oxford, England)·2025
Same author

Molecular mechanisms underlying the evolution of a color polyphenism by genetic accommodation in the tobacco hornworm, <i>Manduca sexta</i>.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning.

Genome biology·2023
Same author

<i>Escherichia coli</i> transcriptome assembly from a compendium of RNA-seq data sets.

RNA biology·2023
Same journal

Epidemiological characteristics of amebiasis in Japan from 2001 to 2022.

PloS one·2026
Same journal

Longitudinal associations of academic stress with eating related patterns, nutrition, somatic indicators, and depressive symptoms in university students: A study protocol.

PloS one·2026
Same journal

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning.

PloS one·2026
Same journal

Insulation failure mapping on power transformer bushing using FRA and electrostatic simulation.

PloS one·2026
Same journal

Enhancing medical Q&A systems with multimodal knowledge graphs and dual-layer attention mechanisms.

PloS one·2026
Same journal

UAMP: Consistent video object segmentation with uncertainty-aware memory propagation.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

594

MLpronto: A tool for democratizing machine learning.

Jacob Tjaden1, Brian Tjaden2

  • 1Computer Science Department, Colby College, Waterville, ME, United States of America.

Plos One
|November 30, 2023
PubMed
Summary
This summary is machine-generated.

MLpronto is a user-friendly system making machine learning analysis accessible to everyone. It requires no programming, offering a no-code solution for data analysis and education.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

767

Related Experiment Videos

Last Updated: Jul 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

594
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

767

Area of Science:

  • Computer Science
  • Data Science

Background:

  • The democratization of machine learning (ML) is a growing trend.
  • Accessible data analysis tools are crucial given the abundance of public data.

Purpose of the Study:

  • Introduce MLpronto, a system designed for easy-to-use machine learning analysis.
  • Facilitate broader engagement with ML algorithms.

Main Methods:

  • MLpronto features a web interface requiring no programming or ML background.
  • It analyzes input data using common supervised ML algorithms.
  • Results and corresponding code are generated rapidly.

Main Results:

  • MLpronto provides a no-code solution for citizen data scientists.
  • It serves as an educational tool for ML learners.
  • It acts as a rapid development tool for those preferring code.

Conclusions:

  • MLpronto democratizes machine learning analysis.
  • The system supports users with varying technical expertise.
  • MLpronto is freely available at https://mlpronto.org/.