MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requ...MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.展开更多
燃气管网是现代城市能源供应体系中不可或缺的一环,然而随着使用年限的增长,燃气管网基础设施不断老化,燃气泄漏事故频繁发生,对燃气管网的稳定与安全运营构成了严峻挑战与潜在风险。面对复杂多变的运行环境,城市燃气管网的运营与紧急...燃气管网是现代城市能源供应体系中不可或缺的一环,然而随着使用年限的增长,燃气管网基础设施不断老化,燃气泄漏事故频繁发生,对燃气管网的稳定与安全运营构成了严峻挑战与潜在风险。面对复杂多变的运行环境,城市燃气管网的运营与紧急响应面临着数据匮乏、多部门协作不畅等难题,尤其在事故预防与应急处理方面显得尤为突出。本文提出了一种基于知识图谱的燃气管网事故演化预警模型,突破了传统预警系统对现场监测数据和历史事故数据的过度依赖的难题。运用双向长短期记忆网络联合条件随机场模型(bidirectional long short-term memory-conditional random field,Bi-LSTM-CRF),高度精确地从燃气管道事故叙述中解析出因果链,同时,整合文本中的多维度特征信息,旨在进一步增强因果抽取的效能与精确度。然后,借助Neo4j图数据库,构建了涵盖复杂因果关系的燃气管网事故演化知识图谱。最后,通过选取具体案例进行了预测性的验证分析。预测结果显示,与传统的预警方法相比,该模型不仅能够有效预警燃气管网事故的发生,更能精准把握事故的发展趋势和潜在路径,为紧急响应策略的制定提供坚实依据与强大助力。展开更多
针对邮轮推舱序列自动规划问题,采用投影法建立推舱路径规划模型,并提出一种基于改进双向快速搜索随机树(Bidirectional Rapidly-Exploring Random Tree,Bi-RRT)算法嵌入的贪心算法进行邮轮推舱序列规划的方法。以大型邮轮H1508船甲板...针对邮轮推舱序列自动规划问题,采用投影法建立推舱路径规划模型,并提出一种基于改进双向快速搜索随机树(Bidirectional Rapidly-Exploring Random Tree,Bi-RRT)算法嵌入的贪心算法进行邮轮推舱序列规划的方法。以大型邮轮H1508船甲板中段区域为例,在Unity3D软件中对预制模块化舱室单元(Pre-fabricated Modular Cabin Unit,PMCU)的推舱序列规划进行仿真试验。试验结果表明,该方法可兼顾避障验证与序列规划,比传统蛇形推舱序列规划具有更高的效率。展开更多
针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局...针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局注意力机制进行词向量编码输入到BiLSTM-CRF序列标注模型中,双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)提取上下文的信息进行拼接生成双向的词向量,再通过条件随机场(conditional random field, CRF)计算联合概率增加对邻近词标签的依赖性优化整个序列,从而解决标注偏置等问题给出的错误标注。纠错阶段根据检错模型输出的结果采用不同策略分类纠错,将标注为错字、缺字的错误使用ERNIE掩码语言模型和混淆集匹配进行预测,对多字、乱序错误直接纠正。实验结果表明,引入序列标注根据错误类型进行分类纠错有效提升了纠错率,在SIGHAN数据集上测试F1达到了81.8%。展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61902215,61872220 and 61701279.
文摘MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.
文摘燃气管网是现代城市能源供应体系中不可或缺的一环,然而随着使用年限的增长,燃气管网基础设施不断老化,燃气泄漏事故频繁发生,对燃气管网的稳定与安全运营构成了严峻挑战与潜在风险。面对复杂多变的运行环境,城市燃气管网的运营与紧急响应面临着数据匮乏、多部门协作不畅等难题,尤其在事故预防与应急处理方面显得尤为突出。本文提出了一种基于知识图谱的燃气管网事故演化预警模型,突破了传统预警系统对现场监测数据和历史事故数据的过度依赖的难题。运用双向长短期记忆网络联合条件随机场模型(bidirectional long short-term memory-conditional random field,Bi-LSTM-CRF),高度精确地从燃气管道事故叙述中解析出因果链,同时,整合文本中的多维度特征信息,旨在进一步增强因果抽取的效能与精确度。然后,借助Neo4j图数据库,构建了涵盖复杂因果关系的燃气管网事故演化知识图谱。最后,通过选取具体案例进行了预测性的验证分析。预测结果显示,与传统的预警方法相比,该模型不仅能够有效预警燃气管网事故的发生,更能精准把握事故的发展趋势和潜在路径,为紧急响应策略的制定提供坚实依据与强大助力。
文摘针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局注意力机制进行词向量编码输入到BiLSTM-CRF序列标注模型中,双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)提取上下文的信息进行拼接生成双向的词向量,再通过条件随机场(conditional random field, CRF)计算联合概率增加对邻近词标签的依赖性优化整个序列,从而解决标注偏置等问题给出的错误标注。纠错阶段根据检错模型输出的结果采用不同策略分类纠错,将标注为错字、缺字的错误使用ERNIE掩码语言模型和混淆集匹配进行预测,对多字、乱序错误直接纠正。实验结果表明,引入序列标注根据错误类型进行分类纠错有效提升了纠错率,在SIGHAN数据集上测试F1达到了81.8%。
