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

Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Introduction to Biological Bases of Psychology01:30

Introduction to Biological Bases of Psychology

Biopsychology serves as a vital bridge connecting the intricate domains of biology and psychology, shedding light on how biological systems influence psychological phenomena. This field scrutinizes the biological substrates of behavior and mental processes, emphasizing the nervous system along with the roles of neurotransmitters, hormones, and genetics. It also incorporates evolutionary perspectives to explain the adaptive nature of mental functions.
The nervous system, the cornerstone of...

You might also read

Related Articles

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

Sort by
Same author

Extrachromosomal DNA Gives Cancer a New Evolutionary Pathway.

Research square·2026
Same author

Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics.

iScience·2026
Same author

Amyloid precursor protein interacts with the mitochondrial phosphatase PGAM5 and regulates mitochondrial respiration.

bioRxiv : the preprint server for biology·2026
Same author

sAPPα Inhibits Neurite Outgrowth in Primary Mouse Neurons via GABA B Receptor Subunit 1a.

eNeuro·2026
Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

AI-Driven Variant Annotation for Precision Oncology in Breast Cancer.

Clinical and translational science·2025

Related Experiment Video

Updated: Jul 10, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Python-Based Interactive Notebooks for Teaching AI and Data Science to Undergraduate Students in the Life Sciences.

Kriti Shukla1, Elizabeth Brunk2,3,4,5

  • 1Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a cloud-based framework using Python and Jupyter notebooks to teach computational skills in undergraduate biochemistry. The accessible platform enhances students

Keywords:
Artificial intelligenceBiochemistryData scienceEducationInformaticsJupyterMachine learningOmicsPython

Related Experiment Videos

Last Updated: Jul 10, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Biochemistry
  • Computational Biology
  • Educational Technology

Background:

  • Biochemistry is increasingly data-intensive due to technological advancements.
  • Undergraduate life science curricula often lack formal training in computational skills.
  • This gap hinders students' ability to analyze and interpret modern biochemical data.

Purpose of the Study:

  • To develop and implement a reproducible, cloud-based framework for integrating Python-driven data analysis into undergraduate biochemistry.
  • To provide students with hands-on experience in coding, data visualization, and omics data interpretation within the core curriculum.
  • To create a scalable and equitable model for enhancing computational literacy in life sciences.

Main Methods:

  • Developed interactive Jupyter notebooks covering coding, protein structure visualization, metabolic modeling, and multi-omics analysis.
  • Delivered the workflow via the CyVerse Discovery Environment for browser-based access and consistent execution.
  • Employed a scaffolded learn-apply-reflect design linking biochemical concepts with coding tasks.

Main Results:

  • The framework successfully integrated computational tools into the biochemistry curriculum for over 1000 students.
  • Students demonstrated substantial gains in confidence with coding and interpreting omics data, as evidenced by pre/post surveys.
  • The cloud-based infrastructure eliminated local installation issues and supported a large user base.

Conclusions:

  • The developed framework provides a scalable and equitable solution for incorporating computational literacy into biochemistry education.
  • This model can be readily adapted for other life science courses.
  • The approach effectively equips students with essential data analysis skills for a data-intensive scientific landscape.