A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis.

BACKGROUND: Clinical diagnosis based on machine learning usually uses case samples as training samples, and uses machine learning to construct disease prediction models characterized by descriptive texts of clinical manifestations. However, the problem of sample imbalance often exists in the medical field, which leads to a decrease in classification performance of the machine learning. METHODS: To solve the problem of sample imbalance in medical dataset, we propose a hybrid sampling algorithm combining synthetic minority over-sampling technique (SMOTE) and edited nearest neighbor (ENN). Firstly, the SMOTE is used to over-sampling missed abortion and diabetes datasets, so that the number of samples of the two classes is balanced. Then, ENN is used to under-sampling the over-sampled dataset to delete the "noisy sample" in the majority. Finally, Random forest is used to model and predict the sampled missed abortion and diabetes datasets to achieve an accurate clinical diagnosis. RESULTS: Experimental results show that Random forest has the best classification performance on missed abortion and diabetes datasets after SMOTE-ENN sampled, and the MCC index is 95.6% and 90.0%, respectively. In addition, the results of pairwise comparison and multiple comparisons show that the SMOTE-ENN is significantly better than other sampling algorithms. CONCLUSION: Random forest has significantly improved all indexes on the missed abortion dataset after SMOTE-ENN sampled.

Tandem gene duplications drive divergent evolution of caffeine and crocin biosynthetic pathways in plants.

BACKGROUND: Plants have evolved a panoply of specialized metabolites that increase their environmental fitness. Two examples are caffeine, a purine psychotropic alkaloid, and crocins, a group of glycosylated apocarotenoid pigments. Both classes of compounds are found in a handful of distantly related plant genera (Coffea, Camellia, Paullinia, and Ilex for caffeine; Crocus, Buddleja, and Gardenia for crocins) wherein they presumably evolved through convergent evolution. The closely related Coffea and Gardenia genera belong to the Rubiaceae family and synthesize, respectively, caffeine and crocins in their fruits. RESULTS: Here, we report a chromosomal-level genome assembly of Gardenia jasminoides, a crocin-producing species, obtained using Oxford Nanopore sequencing and Hi-C technology. Through genomic and functional assays, we completely deciphered for the first time in any plant the dedicated pathway of crocin biosynthesis. Through comparative analyses with Coffea canephora and other eudicot genomes, we show that Coffea caffeine synthases and the first dedicated gene in the Gardenia crocin pathway, GjCCD4a, evolved through recent tandem gene duplications in the two different genera, respectively. In contrast, genes encoding later steps of the Gardenia crocin pathway, ALDH and UGT, evolved through more ancient gene duplications and were presumably recruited into the crocin biosynthetic pathway only after the evolution of the GjCCD4a gene. CONCLUSIONS: This study shows duplication-based divergent evolution within the coffee family (Rubiaceae) of two characteristic secondary metabolic pathways, caffeine and crocin biosynthesis, from a common ancestor that possessed neither complete pathway. These findings provide significant insights on the role of tandem duplications in the evolution of plant specialized metabolism.

Mining genes associated with furanocoumarin biosynthesis in an endangered medicinal plant, Glehnia littoralis.

The endangered medicinal plant Glehnia littoralis is one of the important natural source of furanocoumarin, which has been used as mucolytic, antitussive, antitumour and antibacterial. However, the genetic information of furanocoumarin biosynthesis in G. littoralis is scarce at present. The objective of this study was to mine the putative candidate genes involved in the biosynthesis pathwayof furanocoumarin and provide references for gene identification, and functional genomics of G. littoralis. We carried out the transcriptome analysis of leaves and roots in G. littoralis, which provided a dataset for gene mining. Psoralen, imperatorin and isoimperatorin were detected in G. littoralis by high performance liquid chromatography analysis. Candidate key genes were mined based on the annotations and local BLAST with homologous sequences using BioEdit software. The relative expression of genes was analysed using quantitative real-time polymerase chain reaction. Further, the CYP450 genes were mined using phylogenetic analyses using MEGA 6.0 software. Atotal of 156,949 unigenes were generated, of which 9021 were differentially-expressed between leaves and roots. A total of 82 unigenes encoding eight enzymes in furanocoumarin biosynthetic pathway were first obtained. Seven genes that encoded key enzymes in the downstream furanocoumarin biosynthetic pathway and expressed more in roots than leaves were screened. Twenty-six candidate CYP450 unigenes expressed abundantly in roots and were chiefly concentrated in CYP71, CYP85 and CYP72 clans. Finally, we filtered 102 differentially expressed transcription factors (TFs) unigenes. The transcriptome of G. littoralis was characterized which would help to elucidate the furanocoumarin biosynthetic pathway in G. littoralis and provide an invaluable resource for further study of furanocoumarin.