Evidence map›Paper›PMID 37925783›Full record

ReviewMolecular aspects of medicine2023

Computational methods in glaucoma research: Current status and future outlook.

Minjae J Kim, Cole A Martin, Jinhwa Kim, Monica M Jablonski

Open access · greenAbstract readReview
In one paragraph

Review in Molecular aspects of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Evaluation of Pregabalin bioadhesive multilayered microemulsion IOP-lowering eye drops.Journal of controlled release : official journal of the Controlled Release Society · 2024
    Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Minjae J KimDepartment of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA. Electronic address: mkim64@uthsc.edu.
Cole A MartinDepartment of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA. Electronic address: cmart143@uthsc.edu.
Jinhwa KimGraduate School of Artificial Intelligence, Graduate School of Metaverse, Department of Management Information Systems, Sogang University, 1 Shinsoo-Dong, Mapo-Gu, Seoul, South Korea. Electronic address: jinhwakim@sogang.ac.kr.
Monica M JablonskiDepartment of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA. Electronic address: mjablon1@uthsc.edu.
University of Tennessee Health Science Center · USSogang University · KR

Funding

Genetic Modulators of GlaucomaR01EY021200 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI JABLONSKI, MONICA M · 2011 to 2022
$3.2M
NEI NIH HHS R01 EY021200
6 · The paper itself

Abstract

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Indexed as

Artificial IntelligenceGlaucomaDrug DiscoveryGenomicsHumansDeep learning artificial intelligence (AI)GlaucomaIn-silico methodsMachine learning artificial intelligence (AI)Molecular dynamics (MD) simulationSystems genetics

Identifiers

PMID37925783
PMCPMC10842846
OpenAlexW4388324535

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.