Dataset Information

MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches.

ABSTRACT: The MADS-box gene family is an important transcription factor family involved in floral organogenesis. The previously proposed ABCDE model suggests that different floral organ identities are controlled by various combinations of classes of MADS-box genes. The five-class ABCDE model cannot cover all the species of angiosperms, especially the orchid. Thus, we developed a two-stage approach for MADS-box gene classification to advance the study of floral organogenesis of angiosperms. First, eight classes of reference datasets (A, AGL6, B12, B34, BPI, C, D, and E) were curated and clustered by phylogenetic analysis and unsupervised learning, and they were confirmed by the literature. Second, feature selection and multiple prediction models were curated according to sequence similarity and the characteristics of the MADS-box gene domain using support vector machines. Compared with the BindN and COILS features, the local BLAST model yielded the best accuracy. For performance evaluation, the accuracy of Phalaenopsis aphrodite MADS-box gene classification was 93.3%, which is higher than 86.7% of our previous classification prediction tool, iMADS. Phylogenetic tree construction - the most common method for gene classification yields classification errors and is time-consuming for analysis of massive, multi-species, or incomplete sequences. In this regard, our new system can also confirm the classification errors of all the random selection that were incorrectly classified by phylogenetic tree analysis. Our model constitutes a reliable and efficient MADS-box gene classification system for angiosperms.

SUBMITTER: Chen YT

PROVIDER: S-EPMC6333052 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches.

Chen Yu-Ting YT Chang Chi-Chang CC Chen Chi-Wei CW Chen Kuan-Chun KC Chu Yen-Wei YW

Frontiers in genetics 20190108

The MADS-box gene family is an important transcription factor family involved in floral organogenesis. The previously proposed ABCDE model suggests that different floral organ identities are controlled by various combinations of classes of MADS-box genes. The five-class ABCDE model cannot cover all the species of angiosperms, especially the orchid. Thus, we developed a two-stage approach for MADS-box gene classification to advance the study of floral organogenesis of angiosperms. First, eight cl ...[more]

PMID: 30671085

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Project description:BackgroundThe morphological diversity of flower organs is closely related to functional divergence within the MADS-box gene family. Bryophytes and seedless vascular plants have MADS-box genes but do not have ABCDE or AGAMOUS-LIKE6 (AGL6) genes. ABCDE and AGL6 genes belong to the subgroup of MADS-box genes. Previous works suggest that the B gene was the first ABCDE and AGL6 genes to emerge in plant but there are no mentions about the probable origin time of ACDE and AGL6 genes. Here, we collected ABCDE and AGL6 gene 381 protein sequences and 361 coding sequences from gymnosperms and angiosperms and reconstructed a complete Bayesian phylogeny of these genes. In this study, we want to clarify the probable origin time of ABCDE and AGL6 genes is a great help for understanding the role of the formation of the flower, which can decipher the forming order of MADS-box genes in the future.ResultsThese genes appeared to have been under purifying selection and their evolutionary rates are not significantly different from each other. Using the Bayesian evolutionary analysis by sampling trees (BEAST) tool, we estimated that: the mutation rate of the ABCDE and AGL6 genes was 2.617 × 10-3 substitutions/site/million years, and that B genes originated 339 million years ago (MYA), CD genes originated 322 MYA, and A genes shared the most recent common ancestor with E/AGL6 296 MYA, respectively.ConclusionsThe phylogeny of ABCDE and AGL6 genes subfamilies differed. The APETALA1 (AP1 or A gene) subfamily clustered into one group. The APETALA3/PISTILLATA (AP3/PI or B genes) subfamily clustered into two groups: the AP3 and PI clades. The AGAMOUS/SHATTERPROOF/SEEDSTICK (AG/SHP/STK or CD genes) subfamily clustered into a single group. The SEPALLATA (SEP or E gene) subfamily in angiosperms clustered into two groups: the SEP1/2/4 and SEP3 clades. The AGL6 subfamily clustered into a single group. Moreover, ABCDE and AGL6 genes appeared in the following order: AP3/PI → AG/SHP/STK → AGL6/SEP/AP1. In this study, we collected candidate sequences from gymnosperms and angiosperms. This study highlights important events in the evolutionary history of the ABCDE and AGL6 gene families and clarifies their evolutionary path.

Project description:Alzheimer's disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

Project description:BackgroundBreast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients.MethodsWe integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrichment based on gene coexpression networks (GOEGCN). The GOEGCN considers the two-side distinction analysis between gene coexpression networks for control and experiment groups. The method allows us to study how the modulated coexpressed gene couples impact biological functions at a GO level.ResultsWe modeled the binary classification with weighted DEGs for each subtype. The binary classifier could make a good prediction for an unseen sample, and the experimental results validated the effectiveness of our proposed approaches. The novel enriched GO terms based on GOEGCN for control and experiment groups of each subtype explain the specific biological function changes according to the two-side distinction of coexpression network structures to some extent.ConclusionThe weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the "Sensitivity," "Specificity," "Accuracy," "F1," and "AUC" metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https://github.com/yxchspring/GOEGCN_BRCA_Subtypes.

Dataset Information

MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches.

Publications

MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches.

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