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

11.8K
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...
11.8K
Ribosome Profiling02:24

Ribosome Profiling

3.6K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.6K
Language Development01:22

Language Development

444
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
444
Scaling01:26

Scaling

314
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
314
Language and Cognition01:27

Language and Cognition

438
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
438
Aggregates Classification01:29

Aggregates Classification

380
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
380

You might also read

Related Articles

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

Sort by
Same author

Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.

medRxiv : the preprint server for health sciences·2026
Same author

Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.

NPJ digital medicine·2026
Same author

TimeX: Phenotype Onset Extraction from Clinical Narratives.

npj health systems·2026
Same author

Completeness of Common Data Elements for Breast Cancer Clinical Trials in Observational Databases.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Wrong-side imaging orders: automated detection using electronic health record data - a retrospective cohort study.

BMJ open quality·2026
Same author

Harnessing Patient-Generated Data for Rare Disease Knowledge Enrichment: A Pilot Study.

Studies in health technology and informatics·2026
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
See all related articles

Related Experiment Video

Updated: Sep 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

681

Scalable Scientific Interest Profiling Using Large Language Models.

Yilun Liang1,2, Gongbo Zhang1, Edward Sun3

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Arxiv
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Large Language Models (LLMs) can automate scientific interest profiling. Profiles generated using Medical Subject Headings (MeSH) terms showed better readability and were preferred over abstract-based profiles, despite differences from human-written summaries.

Keywords:
Kullback-Leibler DivergenceLarge Language ModelsNatural Language GenerationResearcher Profiling

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K

Related Experiment Videos

Last Updated: Sep 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

681
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K

Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Research
  • Scientific Communication

Background:

  • Scientist research profiles are crucial for talent discovery and collaboration but are often outdated.
  • Automated and scalable methods are needed to maintain current research profiles.

Purpose of the Study:

  • To design and evaluate Large Language Models (LLMs)-based methods for generating scientific interest profiles.
  • To compare machine-generated profiles (from PubMed abstracts and MeSH terms) with researchers' self-summarized interests.

Main Methods:

  • Utilized GPT-4o-mini to summarize research interests for 595 faculty members based on their PubMed abstracts and MeSH terms.
  • Collected publication data (titles, MeSH terms, abstracts) from CUIMC faculty.
  • Conducted manual and automated evaluations to compare machine-generated and self-written profiles.

Main Results:

  • Lexical overlap was low between machine-generated and self-written profiles (low ROUGE-L, BLEU, METEOR scores).
  • Moderate semantic similarity was found using BERTScore (F1: 0.542 MeSH-based, 0.555 abstract-based).
  • Manual reviews favored MeSH-based profiles (67.86%) for readability (93.44%) and overall impression (77.78% good/excellent).

Conclusions:

  • LLMs offer a scalable solution for automating scientific interest profiling.
  • MeSH-term-derived profiles demonstrate superior readability and user preference compared to abstract-derived profiles.
  • Machine-generated profiles differ in concept choice from human-written ones, highlighting potential for novel idea generation in manual profiles.