In gene expression data analysis, the problems of cancer classification and gene selection are closely related. Successfully selecting informative genes will significantly improve the classification performance. To identify informative genes from a large number of candidate genes, various methods have been proposed. However, the gene expression data may include some important correlation structures, and some of the genes can be divided into different groups based on their biological pathways. Many existing methods do not take into consideration the exact correlation structure within the data. Therefore, from both the knowledge discovery and biological perspectives, an ideal gene selection method should take this structural information into account. Moreover, the better generalization performance can be obtained by discovering correlation structure within data. In order to discover structure information among data and improve learning performance, we propose a structured penalized logistic regression model which simultaneously performs feature selection and model learning for gene expression data analysis. An efficient coordinate descent algorithm has been developed to optimize the model. The numerical simulation studies demonstrate that our method is able to select the highly correlated features. In addition, the results from real gene expression datasets show that the proposed method performs competitively with respect to previous approaches.
NAC [NO APICAL MERISTEM (NAM), ARABIDOPSIS TRANSCRIPTION ACTIVATOR FACTOR 1/2 (ATAF1/2), and CUP-SHAPED COTYLEDON (CUC2)] transcription factors are key regulators of plant growth, development, and stress responses but were also crucial players during land plant adaptation and crop domestication. Using representative members of green algae, bryophytes, lycophytes, gymnosperms, and angiosperms, we expanded the evolutionary history of NAC transcription factors to unveil the relationships among members of this gene family. We found a massive increase in the number of NAC transcription factors from green algae to lycophytes and an even larger increase in flowering plants.
Dioscorea alata, a key tuber crop for global food security, is threatened by anthracnose disease caused by Colletotrichum gloeosporioides. However, identification of functional resistance genes against C. gloeosporioides in D.
METTL3, an m6A methyltransferase, is integral to the regulation of messenger RNA (mRNA) biogenesis, degradation, and translation through the N6-methyladenosine (m6A) modification. Alterations in m6A homeostasis have been implicated in the development, progression, invasion, and metastasis of certain cancers. The present research aims to examine the consequences of METTL3 knockdown using short hairpin RNA (shRNA) on the proliferation and invasive capabilities of human colorectal and melanoma cancer cell lines.
Introduction: Retinol has a long history of treating skin conditions, including photoaging. However, skin irritation with repeated use of retinol is well documented. The present study assessed the effectiveness of a novel topical formulation, referred to as retinol topical formulation (RTF), to improve the quality of skin health.
Exposure to perfluorooctanoic acid (PFOA) and hexafluoropropylene oxide dimer acid (HFPO-DA) was associated with adipogenesis. However, potential mechanisms remain to be elucidated. Herein, a 3T3-L1 adipocyte model was used to explore the dynamic changes in adipocyte differentiation (2, 4, and 8 days) under PFOA and HFPO-DA exposure.
