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2,692 results for “Medicine”
Examples: Bridging Communication Gaps: The Role of Voice-Enabled AI in Medicine
<p><strong>Illustrative examples of potential application cases of advanced voice mode in Clinical Practice. </strong></p>
Local Ecological Knowledge and folk medicine in historical Esthonia, Livonia, Courland and Galicia, 1805-1905
<p>Background: Historical ethnobotanical data can provide valuable information about past human-nature relationships as well as serve as a basis for diachronic analysis. This thesis aims to document medicinal plant uses in the 19th century mentioned in German-language sources in the historical regions of Esthonia, Livonia, Courland and Galicia to analyse the gathered data in regard to plant families and medicinal use categories and finally to qualitatively compare the results with various studies from the study area and surrounding regions with recently acquired data as well as historical data.</p> <p>Methods: Data was mainly obtained by systematic manual search in various relevant historical German-language works focused on the medicinal use of plants. Data about plant and non-plant constituents, their usage, the mode of administration, used plant parts and their German and local names was extracted and collected into a database in the form of Use Reports.</p>
S57 | GREEKPHARMA | Suspect Pharmaceuticals from the National Organization of Medicine, Greece
<p>This is the dataset associated with list S57 GREEKPHARMA on the NORMAN Suspect List Exchange:</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p>
Comparative Study of Entomotoxicity of Three Medicinal Plant Extracts against Sitophilus oryzae
<p>The Sitophilus oryzae is the most widespread and destructive primary stored cereals and grain pest in the world. The major effect of Sitophilus oryzae on an infestation by the feeding activity of grubs and adults and increasing the secondary growth of pests by making conditions optimum for optimum and further infestation. Plant extracts Azadirachta indica, Osmium Sanctum, and Mentha piperita were evaluated for Entomotoxicity such as repellency, adulticidal and larvicidal effect against Sitophilus oryzae. The Entomotoxicity of plant extracts expressed in percentage and Repellency were also expressed in class repellency with class 1, class 2, Class 3, Class 4, and class 5.TheRepellency with 80% of Class 4, Adulticidaland larvicidal percentage with 100 % of Azadirachta Indica and Adulticidal highest Entomotoxicity effect than Osmium sanctum and Mentha piperita</p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
S104 | UKVETMED | UK Veterinary Medicines Directorate's List
<p>This is the collection associated with list S104 UKVETMED UK Veterinary Medicines Directorate's List on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>The UK Veterinary Medicines Directorate's list includes all veterinary medicines including active ingredients and excipients able to be used in the UK as of September 2022. This list of chemicals was compiled by Samuel Fletcher, Veterinary Medicines Directorate (UK) and provided by Kerry Sims, Environment Agency (UK). The list was compiled as follow-up to the <a href="https://www.envchemgroup.com/eb-35-chemical-of-concern.html">Prioritisation and Early Warning System (PEWS)</a> for chemicals of emerging concern in England, in which a number of these Veterinary Medicines have been considered.</p>
eJIM: eJournal of Indian Medicine
<p>The files of the eJIM journal to 2007-2022 (when the article stopped accepting new submissions). Collected with "wget -m -k". ISSN: 1877-8321. An open-access journal of research on Ayurveda and the history of Indian medicine. Launched by G. Jan Meulenbeld and colleagues and published by Roelf Barkhuis in collaboration with the University of Groningen.</p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Rare Diseases
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "Alternative innovation models of pharmaceutical development for rare disease drugs: how (and) do they work?: A qualitative study". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 10/11 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p> <p>Details about the research project can be found at: <a href="https://www.graduateinstitute.ch/NBM">https://www.graduateinstitute.ch/NBM</a></p>
Medicinal Plants of the Guianas: Medical Guiana
Robert A. DeFilipps, Shirley L. Maina and Juliette Crepin. 2004. Medicinal Plants of the Guianas (Guyana, Surinam, French Guiana). Available online: <p></p>http://botany.si.edu/bdg/medicinal/index.html<p></p>Robert A. DeFilipps, Shirley L. Maina and Juliette Crepin. 2004. Medicinal Plants of the Guianas (Guyana, Surinam, French Guiana). Available online: <p></p>http://botany.si.edu/bdg/medicinal/index.html
CT-EBM-SP - Corpus of Clinical Trials for Evidence-Based-Medicine in Spanish (version 2)
<p>A collection of <strong>1200 texts</strong> (292173 tokens) about<strong> clinical trials studies</strong> and <strong>clinical trials announcements</strong> in <strong>Spanish</strong>:</p> <p>- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO).<br>- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos.</p> <p>Texts were annotated with the following entities types:</p> <p>- <strong>Semantic groups from the Unified Medical Language System</strong>: <br> • ANAT: anatomy<br> • CHEM: pharmacological and chemical substances<br> • DEVI: medical devices<br> • DISO: pathologic conditions <br> • LIVB: living beings, included the human being<br> • PHYS: physiological processes<br> • PROC: lab tests, diagnostic or therapeutic procedures<br>- <strong>Medical drug information</strong>:<br> • Contraindicated: a contraindicated drug or treatment<br> • Dose: dose or strength<br> • Form: dosage form<br> • Route: administration route or mode<br>- <strong>Temporal expressions</strong> <br> • Age<br> • Date<br> • Duration<br> • Frequency<br> • Time<br>- <strong>Miscellaneous medical entities</strong>: <br> • Concept: abstract concepts, statistical tests or measurement scales<br> • Food: foods or drinks<br> • Observation: medical observations or clinical findings<br> • Quantifier_or_Qualifier: quantifier or qualifier adjective<br> • Result_or_Value: result or value of a measurement, laboratory analysis or procedure<br>- <strong>Negation/Speculation</strong>: <br> • Neg_cue: negation cue<br> • Negated: negated event<br> • Spec_cue: speculation cue<br> • Speculated: speculated or uncertain event<br>- <strong>Attributes</strong>: <br> • Temporality:<br> ◦ History_of: past event<br> ◦ Future: future event<br> • Experiencer:<br> ◦ Patient: patient or participant on a clinical trial<br> ◦ Family_member<br> ◦ Other: other person different from the patient or the family member</p> <p>86 389 entities and 16 590 attributes were annotated. 10% of the corpus was doubly annotated, and high inter-annotator agreement (IAA) values were achieved: F1-score = 0.84% for entities; and F1-score = 0.88% for attributes (both in strict match). </p> <p>The dataset includes the <strong>texts and annotations used for the human evaluation</strong> of the medical named entity tool:</p> <p>- 100 clinical trial announcements from EudraCT not used for system development: we provide files of the version revised by medical professionals (Reference folder)<br>- 100 clinical cases with Creative Commons license: we provide files with the files revised by medical professionals (Reference folder). These data come from:</p> <p> • Urgencias Bidasoa (https://urgenciasbidasoa.wordpress.com/casos-clinicos-3/)<br> • Hipocampo.org (https://www.hipocampo.org/)<br> • Cases published by Sociedad Andaluza de Medicina Familiar y Comunitaria (SAMFyC): we are greatly thankful for giving us permission to use these cases and we acknowledge that the copyright belongs to the authors' contents. Clinical cases were extracted from books published from 2016 to 2022 (https://www.samfyc.es/tipos-publicacion/publicaciones/).<br> <br>If you use these data, please, acknowledge the copyright and intellectual property rights to the authors' contents.</p> <p>The dataset is freely distributed for research and educational purposes under a Creative Commons Non-Commercial Attribution (CC-BY-NC-A) License.</p> <p>If you use the CT-EBM-SP vs. 2 dataset, please, cite as follows:</p> <p>Campillos-Llanos, L., A. Valverde-Mateos & A. Capllonch-Carrion (2024) Hybrid natural language processing tool for semantic annotation of medical texts in Spanish. BMC Bioinformatics. BioMed Central.</p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Case Study of the Oral Cholera Vaccine Development
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "The 30-year evolution of oral cholera vaccines: A case study of a collaborative network alternative innovation model". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 8/16 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p>
Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"
<p><strong>Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia</strong></p> <p><em>Cancer Discovery</em>, <strong>DOI:</strong> 10.1158/2159-8290.CD-21-0410</p> <p> </p> <p>Data Types:</p> <p>1. Clinical summary</p> <p>2. Drug response data</p> <p>3. Exome-sequencing data</p> <p>4. RNA-sequencing data</p> <p> </p> <p><strong>Updates:</strong></p> <p>- <strong>FILE</strong>: File_3.2. <strong>DATE</strong>: 28.11.2022.</p> <p> </p> <p><strong>1. Clinical summary</strong></p> <p><strong>File_0: </strong>Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.</p> <p><strong>File_1.1: </strong>Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.</p> <p><strong>File_1.2</strong>: Description of the clinical variables in File_1.1.</p> <p> </p> <p><strong>2. Drug response data for 164 AML patient samples and 17 healthy samples</strong></p> <p><strong>File_2: </strong>Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.</p> <p><strong>File_3.1.: </strong>Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.</p> <p><strong>File_3.2.: </strong>Drug response data including drug sensitivity scores (DSS) and selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The data is identical to the Supplementary Table 7 in the manuscript.</p> <p><em>Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS). </em></p> <p><em>Note: If the value is missing, </em><em>the drug was not tested for </em><em>that</em><em> given sample</em><em>.</em></p> <p><strong>File_4: </strong>Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.</p> <p><em>Note: Column E is </em><em>the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.</em></p> <p> </p> <p><strong>3. Exome-sequencing data for 225 AML patient samples</strong></p> <p><em>Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.</em></p> <p>Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.</p> <p><strong>File_5: </strong>VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.</p> <p><strong>File_6:</strong> Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.</p> <p> </p> <p><strong>4. RNA-sequencing data for 163 AML patient samples and 4 healthy</strong></p> <p>CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.</p> <p><strong>File_7:</strong> Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p><strong>File_8: </strong>Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.</p> <p><strong>File_9: </strong>RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p> </p> <p><strong>References</strong></p> <p>1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R<em>, et al.</em> Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports <strong>2014</strong>;4:5193.</p> <p>2. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A<em>, et al.</em> Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med <strong>2013</strong>;368(22):2059-74.</p> <p>3. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A<em>, et al.</em> IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods <strong>2013</strong>;10(11):1081-2.</p> <p>4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND<em>, et al.</em> Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine <strong>2016</strong>;374(23):2209-21.</p> <p>5. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N<em>, et al.</em> COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research <strong>2019</strong>;47(D1):D941-D7.</p> <p> </p>
Dataset for the genome of medicinal plant Sophora flavescens has undergone significant expansion of both transposons and genes
<p><em>Sophora flavescens</em> is a medicinal plant in the genus Sophora of the Fabaceae family. The root of <em>S. flavescens</em> is known in China as Kushen and has a long history of wide use in multiple formulations of Traditional Chinese Medicine (TCM). However, there is little genomic information available for <em>S. flavescens</em>, which has greatly hindered the breeding of <em>S. flavescens</em> and characterisation of bioactive compounds. Therefore, in this study, we used third-generation Nanopore long-read sequencing technology combined with Hi-C scaffolding technology to <em>de novo</em> assemble the <em>S. flavescens</em> genome. We obtained a chromosomal level high-quality <em>S. flavescens</em> draft genome. The draft genome size is approximately 2.08 Gb, with more than 80% annotated as Transposable Elements (TEs). We also annotated 60,485 genes and examined their expression profiles in leaf, stem and root tissues. We also characterised the genes and pathways involved in the biosynthesis of major bioactive compounds, including alkaloids, flavonoids and isoflavonoids. The assembled genome provides valuable resources for conservation, genetic research and breeding of <em>S. flavescens</em>.</p>
Fig. 1 in Streptomyces aquilus sp. nov., a novel actinomycete isolated from a Chinese medicinal plant
Fig. 1. Optical micrograph (a) and scanning electron micrograph (b) of GGCR-6T grown on Gause's synthetic medium at 28 °C after incubation for 14 days.
Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine
<p>Geographical distribution of co-authors of Nobel laureates 1994-2018 in Physics, Chemistry and Physiology or Medicine. Appendix to the article «Quantitative analysis of the co-publications of Ukrainian scientists with the Nobel laureates 1994-2018 in Science».</p>
Figure 5 in A gall mite, Aceria rhodiolae (Acari: Eriophyidae), altering the phytochemistry of a medicinal plant, Rhodiola rosea (Crassulaceae), in the Canadian Arctic
Figure 5. Coxigenital region of Aceria rhodiolae females from (A) Russia, and (B,C,E) Nunavik, Canada. (A,B) Differential interference contrast light microscopy, (C,E) scanning electron micrograph, (D) line drawing. Scale on (B) also applies to (A). Notations on (D) indicate palp, leg and idiosomal setae, and coxal apodemes (ap1, ap2, ap; pra, prosternal apodeme). Other arrows elsewhere indicate characteristic ridges on coxal plates (a,b,c); genital flange (fl), and underlying postgenital plate (pp), which bears setae 3a and extends anterolaterally into lateral flaps (f) that flank the genital coverflap; and ventral ridges on femur, genu, and coxal fields (E).
Figure 8 in A gall mite, Aceria rhodiolae (Acari: Eriophyidae), altering the phytochemistry of a medicinal plant, Rhodiola rosea (Crassulaceae), in the Canadian Arctic
Figure 8. (A) Healthy infructescence of a Rhodiola rosea plant from Nunavik (Canada) versus (B) a mite-infested inflorescence (mostly pale green or yellowish) that partly (centrally) developed into fruits (yellow to red). (C) Dried inflorescences from Labrador (Canada) with a few (upper right) to most (lower left) flowers galled, and a galled leaf (isolated, in the middle). (D) Dried inflorescences from western Russia that were preserved in an herbarium for over 100 years. (E,F) Enlargement of a galled flower and galled leaf from Labrador (same scale). Arrows point at some of the galled flowers (B‒D) or leaves (C). The scale on (C) also applies to (D), and is approximate for (A,B).
Figure 1 in A gall mite, Aceria rhodiolae (Acari: Eriophyidae), altering the phytochemistry of a medicinal plant, Rhodiola rosea (Crassulaceae), in the Canadian Arctic
Figure 1. (A) Map of Canada, showing the area surveyed for Rhodiola rosea in Nunavik, Québec (in white). The small arrow indicates a site where additional samples were taken in Labrador, Newfoundland. (B) Region along the coast of Ungava Bay where populations of R. rosea were surveyed (geographic extremes of study sites: northwest 61.078°N, 69.632°W; northeast 60.422°N, 64.839°W; south 58.023°N). Open circles indicate sites with at least a few galled plants, whereas solid circles indicate sites with no galled plants.
Harvard Citation - Faculty of Medicine UNS
<p>Harvard Citation, Faculty of Medicine UNS version, is a dataset format for Mendeley referencing, which can be used for student's thesis.</p>
Traditional Chinese Medicine Multidimensional Knowledge Graph
<h3>Overview of the Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</h3> <p>The <strong>Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</strong> is a comprehensive, open-source data platform developed by Jingqi Zeng in November 2024. This platform aims to integrate and standardize a vast array of data from multiple sources, encompassing both traditional Chinese medicine (TCM) and modern biomedical sciences. By organizing and linking this diverse information, TCM-MKG acts as a bridge that connects the ancient wisdom of TCM with contemporary medical research and applications.</p> <h3>Key Features and Objectives:</h3> <ul> <li> <p><strong>Multi-source Data Integration</strong>: TCM-MKG consolidates data from over 30 authoritative resources, covering a broad spectrum of topics, including TCM terminology, Chinese patent medicines (CPM), Chinese herbal pieces (CHP), natural products (NP), chemical components, disease targets, and more. These data sources are carefully curated and interlinked, ensuring a rich, multi-dimensional view of TCM in relation to modern biomedical research. The platform incorporates data from reputable databases such as DrugBank, BioGRID, DisGeNET, STRING, and many others, ensuring that the TCM knowledge is not only expansive but also scientifically robust and cross-referenced with global biomedical standards.</p> </li> <li> <p><strong>Standardized Design for Global Interoperability</strong>: TCM-MKG adheres to international data standards and integrates with widely-used global medical classification systems such as ICD-11, UMLS, MeSH, and DOID. This ensures that the platform’s data is globally comparable and facilitates easy integration with international research efforts, promoting collaboration and knowledge exchange across the fields of TCM and modern medicine.</p> </li> <li> <p><strong>Open Source and Collaborative</strong>: In line with its mission to enhance transparency and accessibility, TCM-MKG is open-sourced in a structured tabular format. This allows researchers worldwide to freely access, contribute to, and expand upon the data, fostering interdisciplinary collaboration and accelerating innovation in both TCM research and modern medicine.</p> </li> <li> <p><strong>Advanced Analytical Capabilities</strong>: By leveraging the power of knowledge graph technology and graph-based intelligence algorithms, TCM-MKG supports deep data mining and relational reasoning. Researchers can uncover hidden associations between TCM components, diseases, and targets, providing insights into the mechanisms of herbal interactions and offering new pathways for drug discovery and therapeutic research.</p> </li> </ul> <h3>Personal Research Application:</h3> <p>Using the TCM-MKG platform, I conducted a study titled <strong>"Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine."</strong> This research applied advanced graph intelligence algorithms to quantitatively assess the compatibility mechanisms of Chinese herbal formulas (CHF). The study provides fresh insights into the underlying principles of TCM herbal combinations.</p> <p>This research has been published:</p> <p><strong>Zeng, J., & Jia, X. (2025). Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks. <em>Journal of Pharmaceutical Analysis</em>, 101342. <a href="https://doi.org/10.1016/j.jpha.2025.101342">https://doi.org/10.1016/j.jpha.2025.101342</a></strong></p> <p>The code and methodology for this research have been open-sourced and are available on <a href="https://github.com/ZENGJingqi/GraphAI-for-TCM" target="_new" rel="noopener">GitHub</a>.</p> <h3>Acknowledgments:</h3> <p>This work benefited from the integration of data from numerous open-access and authoritative databases. We acknowledge the valuable contributions of resources such as DrugBank, BindingDB, BioGRID, DisGeNET, and many others. These datasets provided essential insights into TCM, modern drug chemistry, genetics, diseases, and related fields, forming the foundation for the traditional Chinese medicine multi-dimensional knowledge graph (TCM-MKG) used in this study. Furthermore, we utilized the PSICHIC model (https://github. com/huankoh/PSICHIC) to analyze the binding interactions between components and targets. Full citations for these resources are included.</p> <h3>Contact Information:</h3> <p>For further inquiries or more detailed information, please feel free to contact:<br><strong>Email</strong>: <a rel="noopener">zjingqi@163.com</a></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.