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

Leaky Scanning02:28

Leaky Scanning

5.2K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Source Transformation01:15

Source Transformation

6.6K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
6.6K
Translation01:31

Translation

15.0K
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
15.0K
Initiation of Translation02:33

Initiation of Translation

34.1K
Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
34.1K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.7K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues.

GigaScience·2025
Same author

A supervised machine learning model for imputing missing boarding stops in smart card data.

Public transport (Heidelberg, Germany)·2024
Same author

Co-Membership-based Generic Anomalous Communities Detection.

Neural processing letters·2023
Same author

Scientometric trends for coronaviruses and other emerging viral infections.

GigaScience·2020
Same author

Over-optimization of academic publishing metrics: observing Goodhart's Law in action.

GigaScience·2019
Same author

Ethical considerations when employing fake identities in online social networks for research.

Science and engineering ethics·2013
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jul 20, 2025

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
06:18

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

Published on: April 26, 2019

6.0K

Malicious source code detection using a translation model.

Chen Tsfaty1, Michael Fire1

  • 1Department of Software and Information Systems Engineering, Ben-Gurion University, Beer-Sheva 8410501, Israel.

Patterns (New York, N.Y.)
|July 31, 2023
PubMed
Summary
This summary is machine-generated.

A new deep-learning algorithm, Malicious Source code Detection using a Translation model (MSDT), effectively identifies malicious code injections in open-source software. This approach enhances software supply chain security by detecting hidden threats within shared codebases.

Keywords:
PyPideep learningmalware analysisopen sourcesoftware supply chain attackstatic analysis

More Related Videos

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
08:23

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

3.6K
Analysis of Translation in the Developing Mouse Brain using Polysome Profiling
08:38

Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

Published on: May 22, 2021

5.2K

Related Experiment Videos

Last Updated: Jul 20, 2025

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
06:18

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

Published on: April 26, 2019

6.0K
De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
08:23

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

3.6K
Analysis of Translation in the Developing Mouse Brain using Polysome Profiling
08:38

Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

Published on: May 22, 2021

5.2K

Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Open-source software development facilitates code reuse but introduces supply chain vulnerabilities.
  • Increasingly sophisticated "supply chain attacks" exploit open-source practices to compromise numerous products.
  • Detecting malicious code injections in shared software packages is a critical cybersecurity challenge.

Purpose of the Study:

  • To introduce a novel deep-learning-based algorithm for detecting malicious code injections in source code packages.
  • To develop an effective method for identifying compromised open-source software components.
  • To enhance the security of the software supply chain against malicious code.

Main Methods:

  • Developed the Malicious Source code Detection using a Translation model (MSDT) algorithm.
  • Utilized deep learning for source code analysis and anomaly detection.
  • Employed a dataset of over 600,000 functions, embedding them and applying clustering to identify outlier (malicious) functions.

Main Results:

  • MSDT demonstrated high efficacy in detecting real-world code injections.
  • The algorithm achieved precision@k values up to 0.909 in experimental evaluations.
  • Outlier detection via clustering of embedding vectors successfully identified malicious functions.

Conclusions:

  • MSDT is a powerful tool for identifying malicious code within open-source software.
  • The deep-learning approach offers a promising solution for securing the software supply chain.
  • Further development and application of MSDT can significantly improve software security against supply chain attacks.