摘要：【中文】本发明公开了一种基于别名标准化的领域专业词汇词嵌入向量训练方法、系统及介质，本发明领域专业词汇词嵌入向量训练方法的实施步骤包括：获取领域专业词汇的正规名称及其别名，建立别名表；对训练文献进行别名标准化；使用词嵌入向量计算工具对标准化后的训练文献进行学习得到训练文献中所有单词的词嵌入向量；将别名表中所有别名的词嵌入向量设置为其对应的正规名称的词嵌入向量。本发明能够克服领域专业词汇的词嵌入向量不准确的问题，通过别名标准化的方法将相同含义的词汇统一用标准化的词汇表示，使得专业词汇在文献中出现的次数大大增加，从而大幅度提高了专业词汇的词嵌入向量的准确性，为专业领域的命名实体识别奠定了坚实的基础。 【EN】The invention discloses a field professional vocabulary word embedding vector training method, a system and a medium based on alias standardization, and the field professional vocabulary word embedding vector training method comprises the following implementation steps: acquiring a normal name and an alias of a field professional vocabulary, and establishing an alias table; performing alias normalization on the training documents; learning the standardized training documents by using a word embedding vector calculation tool to obtain word embedding vectors of all words in the training documents; and setting the word embedding vectors of all the aliases in the alias table as the corresponding word embedding vectors of the regular names. The invention can overcome the problem that the word embedding vector of the professional vocabulary in the field is inaccurate, and the vocabulary with the same meaning is uniformly expressed by the standardized vocabulary by the alias standardization method, so that the occurrence frequency of the professional vocabulary in the literature is greatly increased, the word embedding vector accuracy of the professional vocabulary is greatly improved, and a solid foundation is laid for the named entity recognition in the professional field.

摘要：【中文】本发明公开了一种基于神经网络的基因突变致病性检测方法、系统及介质，本发明方法包括输入待检测的基因检测VCF文件以及HPO表型；根据待检测的基因检测VCF文件以及HPO表型获取各个基因变异的特征值；对于每一种基因变异，将该基因变异的特征值输入训练好的神经网络模型得到该基因变异的致病性综合分析结果，神经网络模型被预先训练建立了各个基因变异的特征值、各个基因变异的致病性综合分析结果之间的映射关系。本发明不仅克服了人工分析的主观性缺陷，而且能够综合考虑影响基因突变致病性的各种因素，使得综合分析的结果更为客观有效，大大提高了对基因突变致病性分析的准确性，提高了基因解读的效率。 【EN】The invention discloses a gene mutation pathogenicity detection method, a system and a medium based on a neural network, wherein the method comprises the steps of inputting a gene detection VCF file to be detected and an HPO phenotype; acquiring characteristic values of various genetic variations according to a gene detection VCF file to be detected and the HPO phenotype; for each genetic variation, the characteristic value of the genetic variation is input into a trained neural network model to obtain a pathogenicity comprehensive analysis result of the genetic variation, and the neural network model is trained in advance to establish a mapping relation between the characteristic value of each genetic variation and the pathogenicity comprehensive analysis result of each genetic variation. The invention not only overcomes the subjective defect of manual analysis, but also can comprehensively consider various factors influencing the pathogenicity of the gene mutation, so that the result of the comprehensive analysis is more objective and effective, the accuracy of the pathogenicity analysis of the gene mutation is greatly improved, and the gene reading efficiency is improved.