AI-assisted literature exploration of innovative Chinese medicine formulas

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Abstract

Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives.

Methods: We present a novel iterative keyword extraction approach for discerning diverse herbs in historical TCM texts from the Pu-Ji Fang copies. Utilizing association rules, we uncover previously unexplored herb pairs. To bridge classical TCM herbal pairs with modern genetic relationships, we conduct gene-herb searches in PubMed and statistically validate this genetic literature as supporting evidence. We have expanded on the present work by developing a generative language model for suggesting innovative TCM formulations based on textual herb combinations.

Results: We collected associations with 7,664 PubMed cross-search entries for gene-herb and 934 for Shenqifuzheng Injection as a positive control. We analyzed 16,384 keyword combinations from Pu-Ji Fang’s 426 volumes, employing statistical methods to probe gene-herb associations, focusing on examining differences among the target genes and Pu-Ji Fang herbs.

Conclusion: Analyzing Pu-Ji Fang reveals a historical focus on flavor over medicinal aspects in TCM. We extend our work on developing a generative model from classical textual keywords to rapidly produces novel herbal compositions or TCM formulations. This integrated approach enhances our comprehension of TCM by merging ancient text analysis, modern genetic research, and generative modeling.

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Long Short-Term Memory

Jürgen Schmidhuber, Jürgen Schmidhuber (2002)

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

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KEGG for taxonomy-based analysis of pathways and genomes

Minoru Kanehisa, Miho Furumichi, Yoko Sato … (2022)

KEGG ( https://www.kegg.jp ) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups.

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The chemistry and biological activities of N-acetylcysteine.

Yuval Samuni, Sara Goldstein, Olivia Dean … (2013)

N-acetylcysteine (NAC) has been in clinical practice for several decades. It has been used as a mucolytic agent and for the treatment of numerous disorders including paracetamol intoxication, doxorubicin cardiotoxicity, ischemia-reperfusion cardiac injury, acute respiratory distress syndrome, bronchitis, chemotherapy-induced toxicity, HIV/AIDS, heavy metal toxicity and psychiatric disorders. The mechanisms underlying the therapeutic and clinical applications of NAC are complex and still unclear. The present review is focused on the chemistry of NAC and its interactions and functions at the organ, tissue and cellular levels in an attempt to bridge the gap between its recognized biological activities and chemistry. The antioxidative activity of NAC as of other thiols can be attributed to its fast reactions with OH, NO2, CO3(-) and thiyl radicals as well as to restitution of impaired targets in vital cellular components. NAC reacts relatively slowly with superoxide, hydrogen-peroxide and peroxynitrite, which cast some doubt on the importance of these reactions under physiological conditions. The uniqueness of NAC is most probably due to efficient reduction of disulfide bonds in proteins thus altering their structures and disrupting their ligand bonding, competition with larger reducing molecules in sterically less accessible spaces, and serving as a precursor of cysteine for GSH synthesis. The outlined reactions only partially explain the diverse biological effects of NAC, and further studies are required for determining its ability to cross the cell membrane and the blood-brain barrier as well as elucidating its reactions with components of cell signaling pathways. Copyright © 2013 Elsevier B.V. All rights reserved.

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Author and article information

Contributors

Meng-Chi Chung: URI : https://loop.frontiersin.org/people/2593062/overviewRole: Role: Role: Role: Role:

Li-Jen Su: Role:

Chien-Lin Chen: Role:

Li-Ching Wu: URI : https://loop.frontiersin.org/people/2075667/overviewRole:

Journal

Journal ID (nlm-ta): Front Pharmacol

Journal ID (iso-abbrev): Front Pharmacol

Journal ID (publisher-id): Front. Pharmacol.

Title: Frontiers in Pharmacology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1663-9812

Publication date (Electronic): 22 March 2024

Publication date Collection: 2024

Volume: 15

Electronic Location Identifier: 1347882

Affiliations

[1] ¹ Department of Biomedical Science and Engineering , National Central University (NCU) , Jhong-Li City, Taiwan

[2] ² Education and Research Center for Technology Assisted Substance Abuse Prevention and Management , National Central University (NCU) , Taoyuan, Taiwan

[3] ³ Core Facilities for High Throughput Experimental Analysis , Department of Biomedical Sciences and Engineering , National Central University (NCU) , Taoyuan, Taiwan

[4] ⁴ IIHMED Reproductive Center , Taipei, Taiwan

[5] ⁵ Tian Medicine Phamaceutical Company Ltd. , Taipei, Taiwan

[6] ⁶ School of Post-Baccalaureate Chinese Medicine , Tzu Chi University , Hualien, Taiwan

[7] ⁷ Department of Health Promotion and Health Education , National Taiwan Normal University , Taipei, Taiwan

Author notes

Edited by: Aiping Lyu, Hong Kong Baptist University, Hong Kong SAR, China

Reviewed by: Tushar Dhanani, Florida Agricultural and Mechanical University, United States

Daogang Guan, Southern Medical University, China

*Correspondence: Li-Ching Wu, nculcwu@ 123456gmail.com

Article

Publisher ID: 1347882

DOI: 10.3389/fphar.2024.1347882

PMC ID: 10995307

SO-VID: b8e6f72b-8d02-41fb-bf64-c70970735c13

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 01 December 2023

Date accepted : 21 February 2024

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The study has been supported by a National Science and Technology Council (NSTC) research grant (112-2221-E-008-079) in Taiwan. The URL of the funder’s website is ( https://www.nstc.gov.tw/). The funders’ involvement was limited to financial support, with no contribution to the formulation of study methodology, the gathering and interpretation of data, the determination of publication, or the composition of the manuscript.

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section-at-acceptance Ethnopharmacology

ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

Keywords: text annotation tool,tcm,text mining,extraction,tcm lstm generative model

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ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

Keywords: text annotation tool, tcm, text mining, extraction, tcm lstm generative model

AI-assisted literature exploration of innovative Chinese medicine formulas

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Herbal Medicines Journal (Herb Med J)

Most cited references 89

Long Short-Term Memory

KEGG for taxonomy-based analysis of pathways and genomes

The chemistry and biological activities of N-acetylcysteine.

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