Evidence map›Paper›PMID 39319265›Full record

ArticleiScience2024

Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery.

Xuan Luo, Yanyun Ding, Yi Cao, Zhen Liu, Wenchong Zhang, Shangzhi Zeng, Shuk Han Cheng, Honglin Li, Stephen J Haggarty, Xin Wang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025
    Review
  4. Review
  5. 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

12 authors.

Xuan LuoDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.
Yanyun DingNational Center for Applied Mathematics Shenzhen, Shenzhen 518000, China.
Yi CaoDepartment of Biomedical Sciences, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.
Zhen LiuDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.
Wenchong ZhangDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.
Shangzhi ZengNational Center for Applied Mathematics Shenzhen, Shenzhen 518000, China.
Shuk Han ChengDepartment of Biomedical Sciences, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.
Honglin LiInnovation Center for AI and Drug Discovery, East China Normal University, Shanghai 200062, China.
Stephen J HaggartyChemical Neurobiology Laboratory, Precision Therapeutics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA.
Xin WangDepartment of Surgery, Chinese University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.
Jin ZhangNational Center for Applied Mathematics Shenzhen, Shenzhen 518000, China.
Peng ShiDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (

Indexed as

Biological sciencesNatural sciencesNeurosciencePharmacologySystems neuroscience

Identifiers

PMID39319265
PMCPMC11419810

What OpenQuestion holds

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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.