In view of huge search space in drug design, machine learning has become a powerful method to predict the affinity between small molecular drug and targeting protein with the development of artificial intelligence tec...In view of huge search space in drug design, machine learning has become a powerful method to predict the affinity between small molecular drug and targeting protein with the development of artificial intelligence technology. However, various machine learning algorithms including massive different parameters make the prediction framework choice to be quite difficult. In this work, we took a recent drug design competition(from XtalPi company on the DataCastle platform) as the typical case to find the optimized parameters for different machines learning algorithms and the most effective algorithm. After the parameter optimizations, we compared the typical machine learning methods as decision tree(XGBoost, LightGBM) and artificial neural network(MLP, CNN) with root-mean-square error(RMSE) and coefficient of determination(R^2) evaluation. As a result, decision tree is more effective than the neural network as LightGBM>XGBoost>CNN>MLP in the affinity prediction of the specific drug design problem with ～160000 samples. For a much larger screening task in a more complicated drug design study, the sophisticated neural network model may go beyond the decision tree algorithm after generalization enhancing and overfitting reducing. The advanced machine learning methods could extract more information of protein-ligand bindings than traditional ones and improve the screen efficiency of drug design up to 200–1000 times.展开更多
目的将神经网络模型与传统中药药性理论(四性、五味、归经)相结合来预测分析中药肾毒性,方法通过文献检索具有肾毒性证据的中药,并将《中华本草(精选本)》中去除上述肾毒性中药的其他中药作为非肾毒性中药纳入数据。以《中华本草》为标...目的将神经网络模型与传统中药药性理论(四性、五味、归经)相结合来预测分析中药肾毒性,方法通过文献检索具有肾毒性证据的中药,并将《中华本草(精选本)》中去除上述肾毒性中药的其他中药作为非肾毒性中药纳入数据。以《中华本草》为标准,确定每味中药的四性、五味和归经归属,分别进行肾毒性/非肾毒性中药与其四性、五味、归经因素的相关性检验,筛选出相关性变量因素,用于构建神经网络模型(Neural Networks Model,NNM)。同时,绘制模型的"受试者工作特征曲线"(Receiver Operator Characteristic Curve,ROC曲线),并计算曲线下面积(Area Under the Curve,AUC),用于评估模型的预测能力。结果肾毒性/非肾毒性中药与四性、五味归属具有相关性(P<0.05),与归经归属无相关性(P>0.05)。NNM结果显示,热性、辛味、温性和苦味是影响中药肾毒性的前4位重要因素,热性排在重要性第1位,模型ROC曲线的AUC计算结果为0.739。结论将传统中药理论与现代数理统计方法相结合建立的中药肾毒性神经网络模型具有一定的预测性,该建模方法可为中药肾毒性及中药毒理学研究提供一定的参考。展开更多
Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The ...Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.展开更多
背景:增生性瘢痕是以成纤维细胞过度增殖、表皮增厚和角质层功能不良为特征的皮肤纤维化疾病,目前其具体发病机制仍不清楚。目的:基于生物信息学筛选增生性瘢痕相关数据集的核心(Hub)基因及重要信号通路,再用细胞实验加以验证,预测对其...背景:增生性瘢痕是以成纤维细胞过度增殖、表皮增厚和角质层功能不良为特征的皮肤纤维化疾病,目前其具体发病机制仍不清楚。目的:基于生物信息学筛选增生性瘢痕相关数据集的核心(Hub)基因及重要信号通路,再用细胞实验加以验证,预测对其可能有治疗作用的小分子药物。方法:从基因表达综合数据库搜索增生性瘢痕相关的数据集,通过R软件筛选差异表达基因,对差异表达基因进行基因本体论和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Gnomes,KEGG)富集分析,使用String在线平台构建差异表达基因的蛋白质相互作用网络,然后分别利用Cytoscape软件中的Cytohubba和MCODE插件筛选出蛋白质相互作用网络中的关键基因和核心模块,进一步将上述关键基因和构成核心模块的基因求交集得到Hub基因,通过荧光定量PCR验证Hub基因mRNA在人增生性瘢痕与正常皮肤表皮干细胞中的表达差异,并利用人类蛋白图谱中组织学数据验证Hub基因编码蛋白在2种组织中表达量和分布的差异,最后用connectivity map数据库预测针对增生性瘢痕的潜在作用药物。结果与结论:①筛选出的差异表达基因中上调基因102个、下调基因702个,基因本体论和KEGG分析结果显示,富集的信号通路及生物学过程主要涉及紧密连接、花生四烯酸代谢、细胞外基质受体交互、表皮发育和角质化等;②取交集得到8个Hub基因与调控胆固醇代谢的甲羟戊酸途径密切相关,分别是HMGCS1、DHCR7、MSMO1、FDPS、MVK、HMGCR、MVD和ACAT2;③荧光定量PCR结果显示,相比正常皮肤组,增生性瘢痕组HMGCS1、DHCR7、MSMO1、FDPS、HMGCR、MVD和ACAT2 mRNA的表达均显著下降(P<0.05),而MVK mRNA的表达无明显变化(P>0.05);④除MVK外,其余Hub基因编码蛋白在正常皮肤组织中表达水平均高于增生性瘢痕组织(P<0.05);⑤评分排列前10的候选药物包括蛋白激酶A抑制剂(H-89)�展开更多
基金supported by the National Natural Science Foundation of China (31571026, 21727817)
文摘In view of huge search space in drug design, machine learning has become a powerful method to predict the affinity between small molecular drug and targeting protein with the development of artificial intelligence technology. However, various machine learning algorithms including massive different parameters make the prediction framework choice to be quite difficult. In this work, we took a recent drug design competition(from XtalPi company on the DataCastle platform) as the typical case to find the optimized parameters for different machines learning algorithms and the most effective algorithm. After the parameter optimizations, we compared the typical machine learning methods as decision tree(XGBoost, LightGBM) and artificial neural network(MLP, CNN) with root-mean-square error(RMSE) and coefficient of determination(R^2) evaluation. As a result, decision tree is more effective than the neural network as LightGBM>XGBoost>CNN>MLP in the affinity prediction of the specific drug design problem with ～160000 samples. For a much larger screening task in a more complicated drug design study, the sophisticated neural network model may go beyond the decision tree algorithm after generalization enhancing and overfitting reducing. The advanced machine learning methods could extract more information of protein-ligand bindings than traditional ones and improve the screen efficiency of drug design up to 200–1000 times.
文摘目的将神经网络模型与传统中药药性理论(四性、五味、归经)相结合来预测分析中药肾毒性,方法通过文献检索具有肾毒性证据的中药,并将《中华本草(精选本)》中去除上述肾毒性中药的其他中药作为非肾毒性中药纳入数据。以《中华本草》为标准,确定每味中药的四性、五味和归经归属,分别进行肾毒性/非肾毒性中药与其四性、五味、归经因素的相关性检验,筛选出相关性变量因素,用于构建神经网络模型(Neural Networks Model,NNM)。同时,绘制模型的"受试者工作特征曲线"(Receiver Operator Characteristic Curve,ROC曲线),并计算曲线下面积(Area Under the Curve,AUC),用于评估模型的预测能力。结果肾毒性/非肾毒性中药与四性、五味归属具有相关性(P<0.05),与归经归属无相关性(P>0.05)。NNM结果显示,热性、辛味、温性和苦味是影响中药肾毒性的前4位重要因素,热性排在重要性第1位,模型ROC曲线的AUC计算结果为0.739。结论将传统中药理论与现代数理统计方法相结合建立的中药肾毒性神经网络模型具有一定的预测性,该建模方法可为中药肾毒性及中药毒理学研究提供一定的参考。
基金supported by the National Natural Science Foundation of China,No.32130060(to XG).
文摘Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.
文摘背景:增生性瘢痕是以成纤维细胞过度增殖、表皮增厚和角质层功能不良为特征的皮肤纤维化疾病,目前其具体发病机制仍不清楚。目的:基于生物信息学筛选增生性瘢痕相关数据集的核心(Hub)基因及重要信号通路,再用细胞实验加以验证,预测对其可能有治疗作用的小分子药物。方法:从基因表达综合数据库搜索增生性瘢痕相关的数据集,通过R软件筛选差异表达基因,对差异表达基因进行基因本体论和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Gnomes,KEGG)富集分析,使用String在线平台构建差异表达基因的蛋白质相互作用网络,然后分别利用Cytoscape软件中的Cytohubba和MCODE插件筛选出蛋白质相互作用网络中的关键基因和核心模块,进一步将上述关键基因和构成核心模块的基因求交集得到Hub基因,通过荧光定量PCR验证Hub基因mRNA在人增生性瘢痕与正常皮肤表皮干细胞中的表达差异,并利用人类蛋白图谱中组织学数据验证Hub基因编码蛋白在2种组织中表达量和分布的差异,最后用connectivity map数据库预测针对增生性瘢痕的潜在作用药物。结果与结论:①筛选出的差异表达基因中上调基因102个、下调基因702个,基因本体论和KEGG分析结果显示,富集的信号通路及生物学过程主要涉及紧密连接、花生四烯酸代谢、细胞外基质受体交互、表皮发育和角质化等;②取交集得到8个Hub基因与调控胆固醇代谢的甲羟戊酸途径密切相关,分别是HMGCS1、DHCR7、MSMO1、FDPS、MVK、HMGCR、MVD和ACAT2;③荧光定量PCR结果显示,相比正常皮肤组,增生性瘢痕组HMGCS1、DHCR7、MSMO1、FDPS、HMGCR、MVD和ACAT2 mRNA的表达均显著下降(P<0.05),而MVK mRNA的表达无明显变化(P>0.05);④除MVK外,其余Hub基因编码蛋白在正常皮肤组织中表达水平均高于增生性瘢痕组织(P<0.05);⑤评分排列前10的候选药物包括蛋白激酶A抑制剂(H-89)�
