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The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants' mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70-30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Bibak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Transcriptome resources can facilitate to increase yield and quality of walnuts. Finding the best transcriptome assembly has not been the subject of walnuts research as yet. This research generated 240,179,782 reads from 11 walnut leaves according to cDNA libraries. The reads provided a complete de novo transcriptome assembly. Fifteen different transcriptome assemblies were constructed from five different well-known assemblers used in scientific literature with different k-mer lengths (Bridger, BinPacker, SOAPdenovo-Trans, Trinity and SPAdes) as well as two merging approaches (EvidentialGene and Transfuse). Based on the four quality metrics of assembly, the results indicated an efficiency in the process of merging the assemblies after being generated by de novo assemblers. Finally, EvidentialGene was recognized as the best assembler for the de novo assembly of the leaf transcriptome in walnut. Among a total number of 183,191 transcripts which were generated by EvidentialGene, there were 109,413 transcripts capable of protein potential (59.72%) and 104,926 were recognized as ORFs (57.27%). In addition, 79,185 transcripts were predicted to exist with at least one hit to the Pfam database. A number of 3,931 transcription factors were identified by BLAST searching against PlnTFDB. Furthermore, 6,591 of the predicted peptide sequences contained signaling peptides, while 92,704 contained transmembrane domains. Comparison of the assembled transcripts with transcripts of the walnut and published genome assembly for the 'Chandler' cultivar using the BLAST algorithm led to identify a total number of 27,304 and 19,178 homologue transcripts, respectively. De novo transcriptomes in walnut leaves can be developed for the future studies in functional genomics and genetic studies of walnuts., Competing Interests: We have no conflicts of interest to disclose.