SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome

Basith, Shaherin; Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang

doi:10.1016/j.omtn.2019.08.011

Cited by 140 publications

(77 citation statements)

References 80 publications

(164 reference statements)

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“…The sensitivity, specificity and accuracy (Basith et al, 2019;Manavalan et al, 2018aManavalan et al, ,c, 2019a was applied to evaluating the performance of prediction methods. Here, HCC samples were regarded as positive samples; CwoHCC samples were negative samples.…”

Section: Performance Metricsmentioning

confidence: 99%

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Zhang

Tan

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancerrelated death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved "11-gene-pair" which could produce outstanding results. We further investigated the discriminate capability of the "11-gene-pair" for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level.

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Section: Performance Metricsmentioning

confidence: 99%

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Zhang

Tan

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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“…(1) SDM6A: A sequence-based two-layer ensemble approach for the effective prediction of 6mA novel putative sites and non-6mA sites in the rice genome using Integrative Machine-Learning Framework [83]. (2) SNNRice 6mA: Another 6mA identification method for the rice genome using a simple and lightweight deep learning model and the evaluation based on five metrics like accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC)) [84].…”

Section: Bioinformatic Analysis Tools For 6mamentioning

confidence: 99%

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

Karanthamalai

Chodon

Chauhan

et al. 2020

Plants

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N6-methyladenine (6mA) is a DNA base modification at the 6th nitrogen position; recently, it has been resurfaced as a potential reversible epigenetic mark in eukaryotes. Despite its existence, 6mA was considered to be absent due to its undetectable level. However, with the new advancements in methods, considerable 6mA distribution is identified across the plant genome. Unlike 5-methylcytosine (5mC) in the gene promoter, 6mA does not have a definitive role in repression but is exposed to have divergent regulation in gene expression. Though 6mA information is less known, the available evidences suggest its function in plant development, tissue differentiation, and regulations in gene expression. The current review article emphasizes the research advances in DNA 6mA modifications, identification, available databases, analysis tools and its significance in plant development, cellular functions and future perspectives of research.

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“…Next, we further explore the potential molecular mechanism of up-regulated expression of MAPK4 in ALI. Recent decades, DNA methylation is commonly invoked as a mechanism for transcriptional repression [27][28][29], we wondered whether DNA demethylation of CpG island in MAPK4 promoter enhanced the expression of MAPK4 in LPS-induced ALI. Expectedly, bioinformatics analysis indicated that there was a CpG island in the promoter of MAPK4 ( Figure 6A).…”

Section: Nfkb1 and Nr3c1 Negatively Regulate The Expression Of Mapk4mentioning

confidence: 99%

NFKB1/NR3C1-MAPK4 axis regulates the pathology of acute lung injury

Mao

Zhou

et al. 2020

Preprint

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BackgroundAcute lung injury (ALI) is a serious disease with highly morbidity and mortality that causes serious health problems worldwide. MAPK4, a member of atypical MAPK family, has been implicated in the development of cancer. Herein, the current study aimed to investigate the possible role of MAPK4 in the pathology of ALI to identify potential candidates for ALI therapy. MethodsMurine ALI model was established in WT or MAPK4 -/mice and the expressions of MAPK4 were measured. The survival ratio of ALI model mice was observed. Moreover, the changes of pathologic injury and infiltration of inflammatory cells, as well as the related signaling pathways, in lung tissues were analyzed. Furthermore, the possible molecular mechanism of MAPK4 expression in ALI was analyzed by massARRAY and EMSA assay. Finally, the effect of MAPK4 silencing using shRNA interference on the pathology of ALI was identified. the expression of MAPK4 by binding to the core sequence of MAPK4 promoter. Importantly, MAPK4-shRNA treatment could significantly reduce the pathology of lung tissues and prolong survival time of ALI mice. Altogether, our study reveals an unknown role of MAPK4 in the pathology of ALI, indicating that MAPK4 might be a new therapeutic target for clinic therapy against ALI. Materials And MethodsMice MAPK4 deficiency (MAPK4 -/-) mice breeding pair in a C57BL/6 background were purchased from The Jackson Laboratory (027666). Animals were housed under specific pathogen-free conditions at Zunyi Medical University. Cell cultureRaw264.7 cells were purchased from Conservation Genetics CAS Kunming Cell Bank (KCB200603YJ), and were cultured in high glucose DEME containing 10% fetal bovine serum at 37°C in 5% CO 2 . Establishment of ALI ModelWT and MAPK4 -/mice (7 to 9 week-old) were challenged with i.p. injection of 10mg/kg LPS (Escherichia coli 0111:B4; Sigma) dissolved in sterile PBS as shown in our previous study [17]. Then the body weight and lung weight index (lung weight/body weight) was detected at indicated time. Bronchoalveolar LavageImmediately after euthanasia, 1 ml aliquots of PBS were slowly infused in the murine lungs through the tracheostomy and then withdrawn gently. This lavage was repeated three times using the same syringe. The collected lavage fluid was stored in a 10ml tube on ice. The fluid was centrifuged at 1000rpm and 4°C for 10 min, and the cell sediment was washed with PBS. The cell-free supernatant was centrifuged again at 14,000g and 4°C for 10 min, stored at −80°C and used for determination of cytokines content via ELISA. To the pellet, red blood lysis buffer (Solarbio, R1010) were used for 15 min and washed with PBS. Next, the pellet was resuspended for analysis. Lung edema determinationLungs from mice were excised and completely dried in the oven at 60°C 24h for calculation of lung wet/dry ratio.

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SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome

Cited by 140 publications

References 80 publications

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

DNA N6-Methyladenine Modification in Plant Genomes—A Glimpse into Emerging Epigenetic Code

NFKB1/NR3C1-MAPK4 axis regulates the pathology of acute lung injury

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