Evidence map›Paper›PMID 32024055›Full record

ReviewInternational journal of molecular sciences2020

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches.

Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 4 pooled it
12.4field-weighted citation impact, top 1% 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

65 citing papers in PubMed, 4 syntheses or guidelines pooled it, 153 citations in OpenAlex.

  1. Pooled it
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  16. Article
  17. Pharmaco-Multiomics: A New Frontier in Precision Psychiatry.International journal of molecular sciences · 2025
    Review
  18. Review
  19. The Future of the Psychiatrist.Psychiatric research and clinical practice · 2025
    Article
  20. Review

5 more citing papers are in PubMed but not listed here.

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

3 authors at 3 institutions in 2 countries.

Eugene LinDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Chieh-Hsin LinGraduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan.
Hsien-Yuan LaneGraduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan.ORCID 0000-0003-2162-8174
Asia University · TWChang Gung University · TWChina Medical University · TW

Funding

China Medical University Hospital, Taiwan DMR-108-BC-2Ministry of Science and Technology, Taiwan MOST 108-2312-B-039-002Ministry of Science and Technology, Taiwan MOST 108-2314-B-039-002Ministry of Science and Technology, Taiwan MOST 108-2622-B-039-001-CC2National Health Research Institutes NHRI-EX108-10731NI
6 · The paper itself

Abstract

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

Indexed as

Artificial IntelligenceMachine LearningPharmacogeneticsPrecision MedicineAlgorithmsHumansInterdisciplinary StudiesMental DisordersNeural Networks, Computerartificial intelligencebiomarkerdeep learningmachine learningmulti-omicsneural networksneuroimagingpharmacogenomicsprecision medicineprecision psychiatry

Identifiers

PMID32024055
PMCPMC7037937
OpenAlexW3004379845

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.