lncRNA DGCR5在非小细胞肺癌组织中的表达及其与临床病理特征的相关性分析

摘要 目的：探究lncRNA DGCR5在非小细胞肺癌(NSCLC)组织中的表达及其与临床病理特征的相关性。方法：选取2020年1月至2021年12月在我院肿瘤科收治的进行手术治疗的NSCLC患者86例,在手术期间从患者获得肿瘤和非肿瘤的肺癌旁组织样本。采用qRT-PCR测定肿瘤组织及癌旁组织中lncRNA DGCR5表达水平。分析lncRNA DGCR5表达水平与NSCLC患者性别、年龄、临床分期、T分期、N分期等临床病理参数的关系,lncRNA DGCR5表达水平与患者预后总生存期（OS）和无进展生存期（PFS）的关系。结果：与癌旁组织相比,lncRNA DGCR5在NSCLC肿瘤组织中的表达水平相对较低,差异具有统计学意义（P<0.01）。lncRNA DGCR5表达与肿瘤分化程度、TNM分期、肿瘤体积、淋巴转移和远处转移之间存在明显相关性,差异具有统计学意义（P<0.05）。采用Kaplan-Meier法进行生存分析,研究发现lncRNA DGCR5高表达组中位OS及中位DFS分别显著高于lncRNA DGCR5低表达组（P<0.05）。低分化程度、II+ IIIa临床分期、N1-N3淋巴转移、远处转移、及lncRNA DGCR5 低表达均与NSCLC患者总生存率和无进展生存率相关。结论：LncRNA DGCR5在NSCLC患者肿瘤组织中的表达量降低,NSCLC患者血LncRNA DGCR5表达水平与分化程度、TNM分期、淋巴转移、远处转移及预后具有相关性。LncRNA DGCR5可作为早期诊断和治疗NSCLC的新型生物标志物。     

人NFE2基因上游增强子lncRNA调控NFE2基因转录和K562细胞增殖

目的 转录因子NFE2的异常表达在许多骨髓增殖性肿瘤患者中被观察到,然而造成这种异常的转录调控机制尚不明确,本研究旨在探究参与NFE2转录调控的元件和分子机制。方法 首先通过公共数据库中ChIP-seq数据和ATAC-seq数据预测NFE2基因的潜在增强子元件,并通过双荧光素酶报告实验进行体外验证。随后,通过PRO-seq和GRO-seq数据结合RACE技术克隆这些增强子RNA转录本,经在线编码潜能预测工具分析认为其为lncRNA,通过RT-qPCR检测该lncRNA在不同白血病细胞系中和这些细胞诱导分化前后的表达变化及其亚细胞定位。最后,通过慢病毒系统在K562细胞中过表达和敲降该lncRNA以探究其功能。结果 鉴定出调控NFE2转录的3个增强子元件,分别位于NFE2转录起始位点-3.6k,-6.2k和+6.3k区域,这些元件插入NFE2启动子上游均能增强下游萤火虫荧光素酶的表达。克隆出-3.6k增强子负链方向的转录本,将其鉴定为-3.6k-lncRNA。本研究发现,该lncRNA在K562、U937和HL-60这3种白血病细胞系中均有一定程度的表达,且均定位于细胞核内。当该lncRNA在K562细胞中过表达,NFE2水平随之提高,细胞增殖和细胞迁移能力受到抑制;当其被敲降时,NFE2水平相应降低而K562细胞增殖能力随之升高。结论 本文鉴定了调控人NFE2基因转录的3个增强子元件和一条增强子lncRNA转录本,并验证了该lncRNA对NFE2转录的正调控作用以及对K562细胞增殖能力具有抑制作用。     

重点疾病非计划重返住院的影响因素分析

目的 研究某三甲综合医院2012年18类重点疾病非计划重返住院的影响因素。方法 对样本院2012年18类重点疾病7 406例患者进行分析,研究其出院后15天、31天非计划重返住院的原因。 结果 9类疾病33名患者出现了非计划再次入院情况,慢性病和60岁以上老年人更有可能非计划重返住院,患者出院时疾病状态、出院主张、病情加重或病情复发、住院天数等是影响非计划重返住院的主要因素。 结论 要从熟练掌握相关疾病的临床治愈好转标准,加强医患沟通,加强健康宣教,加强出院随访等方面来提高医院的医疗质量。     

血清外泌体lncRNA PCGEM1、miR-129-5p与非小细胞肺癌患者临床病理特征及预后的关系

摘要 目的：研究血清外泌体长链非编码核糖核酸（lncRNA）前列腺癌基因表达标记1（PCGEM1）、微小核糖核酸（miR）-129-5p与非小细胞肺癌（NSCLC）患者临床病理特征及预后的关系。方法：选取2016年2月-2018年1月南京脑科医院收治的125例NSCLC患者作为NSCLC组,同期选取体检的70例健康人群作为健康组。采集两组静脉血,提取血清外泌体;采用实时定量聚合酶链式反应（qRT-PCR）检测血清外泌体lncRNA PCGEM1、miR-129-5p表达情况;采用Pearson相关性分析lncRNA PCGEM1与miR-129-5p的关系。并分析血清外泌体lncRNA PCGEM1、miR-129-5p与NSCLC患者临床病理特征的关系。对NSCLC患者行5年随访,绘制Kaplan-Meier曲线分析预后情况,多因素Cox比例风险回归模型分析预后不良危险因素,受试者工作特征（ROC）曲线分析lncRNA PCGEM1、miR-129-5p对NSCLC预后的预测价值。结果：：NSCLC组lncRNA PCGEM1相对表达量高于健康组,miR-129-5p相对表达量低于健康组（P＜0.05）。血清外泌体lncRNA PCGEM1相对表达量与miR-129-5p表达呈负相关（r= -0.420,P＜0.05）。血清外泌体lncRNA PCGEM1、miR-129-5p表达与患者TNM分期、分化程度、淋巴结转移有关（P＜0.05）。Kplan-Meier生存曲线显示,lncRNA PCGEM1低表达组5年生存率69.05%高于lncRNA PCGEM1高表达组35.53%,miR-129-5p高表达组5年生存率68.09%高于miR-129-5p低表达组33.80%。多因素Cox比例风险回归显示,TNM分期III期、有淋巴结转移、lncRNA PCGEM1高表达、miR-129-5p低表达为NSCLC患者预后不良的独立危险因素（P＜0.05）。ROC曲线显示,lncRNA PCGEM1、miR-129-5p联合检测对NSCLC预后的预测曲线下面积（AUC）为0.865,预测价值高于两者单独预测。结论：NSCLC患者血清外泌体lncRNA PCGEM1表达上调、miR-129-5p表达下调,二者表达与NSCLC患者TNM分期、分化程度、淋巴结转移有关,且与患者预后密切相关,对NSCLC预后不良具有较好预测价值。     

基于注意力机制的RNA碱基关联图预测方法

目的 长链非编码RNA在遗传、代谢和基因表达调控等方面发挥着重要作用。然而，传统的实验方法解析RNA的三级结构耗时长、费用高且操作要求高。此外，通过计算方法来预测RNA的三级结构在近十年来无突破性进展。因此，需要提出新的预测算法来准确的预测RNA的三级结构。所以，本文发展可以用于提高RNA三级结构预测准确性的碱基关联图预测方法。方法 为了利用RNA理化特征信息，本文应用多层全卷积神经网络和循环神经网络的深度学习算法来预测RNA碱基间的接触概率，并通过注意力机制处理RNA序列中碱基间相互依赖的特征。结果 通过多层神经网络与注意力机制结合，本文方法能够有效得到RNA特征值中局部和全局的信息，提高了模型的鲁棒性和泛化能力。检验计算表明，所提出模型对序列长度L的4种标准（L/10、L/5、L/2、L）碱基关联图的预测准确率分别达到0.84、0.82、0.82和0.75。结论 基于注意力机制的深度学习预测算法能够提高RNA碱基关联图预测的准确率，从而帮助RNA三级结构的预测。     

基于卷积神经网络的大肠杆菌启动子预测

目的 基于位点特异性打分矩阵（position-specific scoring matrices,PSSM）的预测模型已经取得了良好的效果,基于PSSM的各种优化方法也在不断发展,但准确率相对较低,为了进一步提高预测准确率,本文基于卷积神经网络（convolutional neural networks,CNN）算法做了进一步研究。方法 采用PSSM将启动子序列处理成数值矩阵,通过CNN算法进行分类。大肠杆菌K-12（Escherichia coli K-12,E.coli K-12,下文简称大肠杆菌）的Sigma38、Sigma54和Sigma70 3种启动子序列被作为正集,编码（Coding）区和非编码（Non-coding）区的序列为负集。结果 在预测大肠杆菌启动子的二分类中,准确率达到99%,启动子预测的成功率接近100%;在对Sigma38、Sigma54、Sigma70 3种启动子的三分类中,预测准确率为98%,并且针对每一种序列的预测准确率均可以达到98%以上。最后,本文以Sigma38、Sigma54、Sigma70 3种启动子分别和Coding区或者Non-coding区序列做四分类,预测得到的准确性为0.98,对3种Sigma启动子均衡样本的十交叉检验预测精度均可以达到0.95以上,海明距离为0.016,Kappa系数为0.97。结论 相较于支持向量机（support vector machine,SVM）等其他分类算法,CNN分类算法更具优势,并且基于CNN的分类优势,编码方式亦可以得到简化。     

基于DNA变异的中国汉族人群脱发表型推断及预测模型评估

目的 男性型脱发（male pattern baldness,MPB）,又称为雄激素性脱发（AGA）,是一种常见的男性脱发类型,大约80%的表型差异可以用遗传因素解释。目前的MPB遗传推断研究主要基于欧洲人群,东亚人群相关研究较少。本研究在中国人群中对欧洲人群MPB关联位点进行验证分析,并建立遗传推断模型。方法 本研究调查了486个与欧洲人群MPB相关单核苷酸多态性（SNP）位点在312名中国汉族男性中的关联性,分别使用逐步回归和Lasso回归方法对关联出的位点进行筛选。使用逻辑回归算法构建预测模型,通过十折交叉验证的方法评估。之后进一步比较了逻辑回归、k近邻分类器、随机森林、支持向量机4种常用分类器模型对MPB的预测准确性。结果 有174个SNP位点与中国汉族男性的MPB显著相关（P＜0.05）。通过不同的筛选方法,分别得到了22个SNP和25个SNP的位点集合。基于上述位点集合建立了22-SNP和 25-SNP两种逻辑回归预测模型。以AUC（ROC曲线下方的面积大小,area under curve）来衡量,两种模型对MPB预测的准确性分别为0.85和0.84;经十折交叉验证后预测准确性分别下降至0.81和0.77。当加入年龄作为预测因子后,两种模型的AUC均达到最大值0.89。从运行结果来看,逻辑回归预测模型较本研究中的其他分类器模型具有明显优势。结论 总体而言,虽然预测模型的准确性尚未达到临床期望水平,但SNP在MPB的遗传预测方面仍具备很大的潜力,可以为MPB的早期诊断、临床干预和法庭科学应用提供参考。     

胃癌组织长链非编码RNA HIT000218960的表达及其与患者临床病理特征及预后的关系研究

摘要 目的：探讨胃癌组织长链非编码核糖核酸（lncRNA）HIT000218960的表达及其与患者临床病理特征及预后的关系。方法：选择2018年1月至2019年6月于中国人民解放军联勤保障部队第九七Ο医院进行手术切除治疗的103例胃癌患者及进行胃粘膜活检的健康体检志愿者62例,取其对应组织,应用逆转录-定量聚合酶链式反应（RT-qPCR）检测组织中lncRNA HIT000218960及高迁移率族蛋白A2（HMGA2）信使RNA（mRNA）表达,应用免疫组织化学法检测组织中HMGA2阳性表达。分析lncRNA HIT000218960表达与胃癌患者临床病理特征的关系。随访3年,应用Kaplan-Meier生存曲线分析不同lncRNA HIT000218960分组患者预后情况,并应用Cox回归分析胃癌患者预后的影响因素。结果：胃癌组织中lncRNA HIT000218960、HMGA2 mRNA表达水平显著高于正常胃粘膜组织（P<0.05）,HMGA2蛋白阳性表达率显著高于正常胃粘膜组织（P<0.05）。胃癌组织中lncRNA HIT000218960表达与肿瘤直径、组织分化程度、TNM分期、淋巴结转移显著相关（P<0.05）。lncRNA HIT000218960水平与HMGA2 mRNA表达呈正相关（r=0.462,P<0.05）。lncRNA HIT000218960低表达组3年生存率显著高于lncRNA HIT000218960高表达组（P<0.05）。Cox回归显示,肿瘤组织中低分化、TNM分期Ⅲ期、淋巴结转移、lncRNA HIT000218960高表达、HMGA2 mRNA高表达是胃癌患者预后不良的危险因素（P＜0.05）。结论：胃癌组织中存在lncRNA HIT000218960异常高表达,其与胃癌恶性进展及患者预后不良有关。     

经阴道彩色多普勒超声联合lncRNA CCAT1在上皮性卵巢癌诊断中的临床价值

摘要 目的：探讨经阴道彩色多普勒超声联合血清长链非编码 RNA （lncRNA）结直肠相关转录本1（CCAT1）对上皮性卵巢癌的临床诊断价值。方法：以2020年5月至2022年5月本院收治的88例上皮卵巢肿瘤患者为研究对象,依据病理检查结果分为良性组（n=49）和恶性组（n=39）。对所有研究对象进行经阴道彩色多普勒超声检查及lncRNA CCAT1检测。采用受试者特征工作曲线（ROC）评价彩色多普勒超声参数联合血清lncRNA CCAT1对上皮性卵巢癌及其恶性程度的评估价值。结果：恶性组的阻力指数（RI）、搏动指数（PI）低于良性组,收缩期峰值血流速度（PSV）、舒张末期血流速度（EDV）及血清lncRNA CCAT1水平均高于良性组（P<0.05）;恶性组中Ⅲ~Ⅳ期患者的RI、PI低于Ⅰ~Ⅱ期患者,PSV、EDV及血清lncRNA CCAT1水平高于Ⅰ~Ⅱ期患者（P<0.05）。ROC分析结果显示,RI、PI、PSV、EDV及lncRNA CCAT1联合评价上皮性卵巢癌的曲线下面积（AUC）为0.977,联合评估效能均优于各指标单独评估;同时RI、PI、PSV、EDV及lncRNA CCAT1联合评价上皮性卵巢癌恶性程度的AUC为0.979,联合评估效能均优于各指标单独评估。结论：经阴道彩色多普勒超声联合血清lncRNA CCAT1检测对上皮性卵巢癌及其恶性程度均具有一定的评估价值,且各参数联合lncRNA CCAT1评估效能更佳。     

DLGCN:基于图卷积网络的药物-lncRNA[]关联预测

为实现高通量识别新的药物-长链非编码RNA(Long non-coding RNA, lncRNA)关联,本文提出了一种基于图卷积网络模型来识别潜在药物-lncRNA关联的方法DLGCN(Drug-LncRNA graph convolution network)。首先,基于药物的结构信息和lncRNA的序列信息分别构建了药物-药物和lncRNA-lncRNA相似性网络,并整合实验证实的药物-lncRNA关联构建了药物-lncRNA异质性网络。然后,将注意力机制和图卷积运算应用于该网络中,学习药物和lncRNA的低维特征,基于整合的低维特征预测新的药物-lncRNA关联。通过效能评估,DLGCN的受试者工作特性曲线下面积(Area under receiver operating characteristic, AUROC)达到0.843 1,优于经典的机器学习方法和常见的深度学习方法。此外,DLGCN预测到姜黄素能够调控lncRNA MALAT1的表达,已被最近的研究证实。DLGCN能够有效预测药物-lncRNA关联,为肿瘤治疗新靶点的识别和抗癌药物的筛选提供了重要参考。     

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations

Background

Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.

Results

In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications.

Conclusions

Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.

     

A Network Based Method for Analysis of lncRNA-Disease Associations and Prediction of lncRNAs Implicated in Diseases

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer''s disease, pancreatic cancer, and gastric cancer were verified by other independent studies.     

Predicting binary,discrete and continued lncRNA-disease associations via a unified framework based on graph regression

Background

In human genomes, long non-coding RNAs (lncRNAs) have attracted more and more attention because their dysfunctions are involved in many diseases. However, the associations between lncRNAs and diseases (LDA) still remain unknown in most cases. While identifying disease-related lncRNAs in vivo is costly, computational approaches are promising to not only accelerate the possible identification of associations but also provide clues on the underlying mechanism of various lncRNA-caused diseases. Former computational approaches usually only focus on predicting new associations between lncRNAs having known associations with diseases and other lncRNA-associated diseases. They also only work on binary lncRNA-disease associations (whether the pair has an association or not), which cannot reflect and reveal other biological facts, such as the number of proteins involved in LDA or how strong the association is (i.e., the intensity of LDA).

Results

To address abovementioned issues, we propose a graph regression-based unified framework (GRUF). In particular, our method can work on lncRNAs, which have no previously known disease association and diseases that have no known association with any lncRNAs. Also, instead of only a binary answer for the association, our method tries to uncover more biological relationship between a pair of lncRNA and disease, which may provide better clues for researchers. We compared GRUF with three state-of-the-art approaches and demonstrated the superiority of GRUF, which achieves 5%~16% improvement in terms of the area under the receiver operating characteristic curve (AUC). GRUF also provides a predicted confidence score for the predicted LDA, which reveals the significant correlation between the score and the number of RNA-Binding Proteins involved in LDAs. Lastly, three out of top-5 LDA candidates generated by GRUF in novel prediction are verified indirectly by medical literature and known biological facts.

Conclusions

The proposed GRUF has two advantages over existing approaches. Firstly, it can be used to work on lncRNAs that have no known disease association and diseases that have no known association with any lncRNAs. Secondly, instead of providing a binary answer (with or without association), GRUF works for both discrete and continued LDA, which help revealing the pathological implications between lncRNAs and diseases.

     

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Background

Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques.

Results

CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC?=?0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports.

Conclusions

Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

     

CRlncRNA: a manually curated database of cancer-related long non-coding RNAs with experimental proof of functions on clinicopathological and molecular features

Background

Recent studies demonstrated that long non-coding RNAs (lncRNAs) could be intricately implicated in cancer-related molecular networks, and related to cancer occurrence, development and prognosis. However, clinicopathological and molecular features for these cancer-related lncRNAs, which are very important in bridging lncRNA basic research with clinical research, fail to well settle to integration.

Results

After manually reviewing more than 2500 published literature, we collected the cancer-related lncRNAs with the experimental proof of functions. By integrating from literature and public databases, we constructed CRlncRNA, a database of cancer-related lncRNAs. The current version of CRlncRNA embodied 355 entries of cancer-related lncRNAs, covering 1072 cancer-lncRNA associations regarding to 76 types of cancer, and 1238 interactions with different RNAs and proteins. We further annotated clinicopathological features of these lncRNAs, such as the clinical stages and the cancer hallmarks. We also provided tools for data browsing, searching and download, as well as online BLAST, genome browser and gene network visualization service.

Conclusions

CRlncRNA is a manually curated database for retrieving clinicopathological and molecular features of cancer-related lncRNAs supported by highly reliable evidences. CRlncRNA aims to provide a bridge from lncRNA basic research to clinical research. The lncRNA dataset collected by CRlncRNA can be used as a golden standard dataset for the prospective experimental and in-silico studies of cancer-related lncRNAs. CRlncRNA is freely available for all users at http://crlnc.xtbg.ac.cn.

     

Measuring disease similarity and predicting disease-related ncRNAs by a novel method

Background

Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.

Methods

Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.

Results

The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ²?=?0.1315, p?=?2.2e-16). Finally, the disease similarity computed by infDisSim was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC (0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting novel disease-related lncRNAs and miRNAs, respectively.

Conclusions

The high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related lncRNAs and miRNAs.

     

A Computational Framework to Infer Human Disease-Associated Long Noncoding RNAs

As a major class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been implicated in various critical biological processes. Accumulating researches have linked dysregulations and mutations of lncRNAs to a variety of human disorders and diseases. However, to date, only a few human lncRNAs have been associated with diseases. Therefore, it is very important to develop a computational method to globally predict potential associated diseases for human lncRNAs. In this paper, we developed a computational framework to accomplish this by combining human lncRNA expression profiles, gene expression profiles, and human disease-associated gene data. Applying this framework to available human long intergenic noncoding RNAs (lincRNAs) expression data, we showed that the framework has reliable accuracy. As a result, for non-tissue-specific lincRNAs, the AUC of our algorithm is 0.7645, and the prediction accuracy is about 89%. This study will be helpful for identifying novel lncRNAs for human diseases, which will help in understanding the roles of lncRNAs in human diseases and facilitate treatment. The corresponding codes for our method and the predicted results are all available at http://asdcd.amss.ac.cn/MingXiLiu/lncRNA-disease.html.     

A Computationally Efficient Correlational Neural Network for Automated Prediction of Chronic Kidney Disease

ObjectivesIn this paper, we propose a computationally efficient Correlational Neural Network (CorrNN) learning model and an automated diagnosis system for detecting Chronic Kidney Disease (CKD). A Support Vector Machine (SVM) classifier is integrated with the CorrNN model for improving the prediction accuracy.Material and methodsThe proposed hybrid model is trained and tested with a novel sensing module. We have monitored the concentration of urea in the saliva sample to detect the disease. Experiments are carried out to test the model with real-time samples and to compare its performance with conventional Convolutional Neural Network (CNN) and other traditional data classification methods.ResultsThe proposed method outperforms the conventional methods in terms of computational speed and prediction accuracy. The CorrNN-SVM combined network achieved a prediction accuracy of 98.67%. The experimental evaluations show a reduction in overall computation time of about 9.85% compared to the conventional CNN algorithm.ConclusionThe use of the SVM classifier has improved the capability of the network to make predictions more accurately. The proposed framework substantially advances the current methodology, and it provides more precise results compared to other data classification methods.