Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently.The pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient costs.Blockchain(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake circulation.The designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)method.With this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical Industries.Firstly,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical industry.In addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and consumers.The spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN model.The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work.The presented model is tested on the UCI repository’s open-access drug reviews database.

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large poten-tial for providing valuable insights into these cases.Therefore,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘Drugs.Com’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)network.The CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based methods.Performance analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical sig-nificance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.

Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.

Multi-manufacturer drug identification based on near infrared spectroscopy and deep transfer learning

Near infrared(NIR)spectrum analysis technology has outstanding advantages such as rapid,nondestructive,pollution-free,and is widely used in food,pharmaceutical,petrochemical,agricultural products production and testing industries.Convolutional neural network(CNN)is one of the most successful methods in big data analysis because of its powerful feature ex-traction and abstraction ability,and it is especially suitable for solving multi-classification problems.CNN-based transfer learning is a machine learning technique,which migrates para-meters of trained model to the new one to improve the performance.The transfer learning strategy can speed up the learning efficiency of the model instead of learning from scratch.In view of the difficulty in acquisition of drug NIR spectral data and high labeling cost,this paper proposes three simple but very effective transfer learning methods for multi-manufacturer identification of drugs based on one-dimensional CNN.Compared with the original CNN,the transfer learning method can achieve better classification performance with fewer NIR spectral data,which greatly reduces the dependence on labeled NIR spectral data.At the same time,this paper also compares and discusses three different transfer learning methods,and selects the most suitable transfer learning model for drug NIR spectral data analysis.Compared with the current popular methods,such as SVM,BP,AE and ELM,the proposed method achieves higher classification accuracy and scalability in multi-variety and multi-manufacturer NIR spectrum classification experiments.

Problem-Based Learning of Drug Use and Abuse during COVID-19 Contingency

Introduction: Nursing students’ experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others. Problem-based learning (PBL) is a pedagogic strategy to strengthen significant learning;then the objective was to establish PBL influence in nursing students’ experiences on drug use and abuse during COVID-19 contingency. Methods: Qualitative, phenomenological and descriptive paradigm, 12 female and male nursing students aged 20 - 24 years old from the 5th and 6th semesters participated. Information collection was through semi-structured interview and a deep one in four cases. A guide of questions about: How the pandemic impacted your life? How did you face it? And what did you learn during this process? Those questions were used. Qualitative data analysis was based on De Souza Minayo, and signed informed consent was obtained from participants. Results: Students’ experiences allowed four categories to emerge, with six sub-categories. Category I. Students’ experiences on drug use and abuse facing the sanitary contingency;Category II. Students’ skills development to identify a problem and design of appropriate solutions;Category III. Developing skills to favor interpersonal relationships;Category IV. Influence of PBL in nursing students’ experiences on drug use and abuse during the COVID-19 contingency. Conclusion: PBL favored analysis and thoughts in nursing students’ experiences on drug use and abuse during the COVID-19 contingency, they worked collaboratively, developed resilience to daily life situations, and implemented stress coping strategies with their significant learning, which diminished their risk behavior.

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate.Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system.Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response.A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks.Human hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab.Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept.Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells.Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data.The proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in vitro.The developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other approaches.The high performance reveals the effectiveness of the approach.It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.

Visually Impaired Individuals in Brazil and Portugal and Learning about Drugs through a Board Game

Objective: To compare the learning of visually impaired individuals after the use of the educational game “Drugs: playing it clean”. Method: Quasi-experimental, comparative, before-after study. Results: The participants’ mean age in Brazil was lower than in Portugal;a significant difference in information acquisition was found between the pre and post-test for the low-complexity (Brazil p = 0.018 and Portugal p = 0.002), without a difference in the number of correct answers for the medium/high-complexity questions between the two countries (p = 0.655 and p = 0.0792);when comparing the number of correct answers before and after the game intervention, an increase was found in Brazil and Portugal, respectively (21.8% - 61.1%;11.2% - 38.9%);a significant difference was found in the number of correct answers between the low and medium/high-complexity questions (p = 0.030). Conclusion: The educational game permits information access and can be used as a teaching-learning strategy.

SpectraTr:A novel deep learning model for qualitative analysis of drug spectroscopy based on transformer structure

The drug supervision methods based on near-infrared spectroscopy analysis are heavily dependent on the chemometrics model which characterizes the relationship between spectral data and drug categories.The preliminary application of convolution neural network in spectral analysis demonstrates excellent end-to-end prediction ability,but it is sensitive to the hyper-parameters of the network.The transformer is a deep-learning model based on self-attention mechanism that compares convolutional neural networks(CNNs)in predictive performance and has an easy-todesign model structure.Hence,a novel calibration model named SpectraTr,based on the transformer structure,is proposed and used for the qualitative analysis of drug spectrum.The experimental results of seven classes of drug and 18 classes of drug show that the proposed SpectraTr model can automatically extract features from a huge number of spectra,is not dependent on pre-processing algorithms,and is insensitive to model hyperparameters.When the ratio of the training set to test set is 8:2,the prediction accuracy of the SpectraTr model reaches 100%and 99.52%,respectively,which outperforms PLS DA,SVM,SAE,and CNN.The model is also tested on a public drug data set,and achieved classification accuracy of 96.97%without preprocessing algorithm,which is 34.85%,28.28%,5.05%,and 2.73%higher than PLS DA,SVM,SAE,and CNN,respectively.The research shows that the SpectraTr model performs exceptionally well in spectral analysis and is expected to be a novel deep calibration model after Autoencoder networks(AEs)and CNN.

Deep Learning in Medical Imaging and Drug Design

Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.

Using the improved position specific scoring matrix and ensemble learning method to predict drug-binding residues from protein sequences

Identification of the drug-binding residues on the surface of proteins is a vital step in drug discovery and it is important for understanding protein function. Most previous researches are based on the structural information of proteins, but the structures of most proteins are not available. So in this article, a sequence-based method was proposed by combining the support vector machine (SVM)-based ensemble learning and the improved position specific scoring matrix (PSSM). In order to take the local environment information of a drug-binding site into account, an improved PSSM profile scaled by the sliding window and smoothing window was used to improve the prediction result. In addition, a new SVM-based ensemble learning method was developed to deal with the imbalanced data classification problem that commonly exists in the binding site predictions. When performed on the dataset of 985 drug-binding residues, the method achieved a very promising prediction result with the area under the curve (AUC) of 0.9264. Furthermore, an independent dataset of 349 drug- binding residues was used to evaluate the pre- diction model and the prediction accuracy is 84.68%. These results suggest that our method is effective for predicting the drug-binding sites in proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Ensem_DBS.zip.

Antidiabetic treatment on memory and spatial learning: From the pancreas to the neuron

The detrimental effects of constant hyperglycemia on neural function have been quantitatively and qualitatively evaluated in the setting of diabetes mellitus. Some of the hallmark features of diabetic encephalopathy (DE) are impaired synaptic adaptation and diminished spatial learning capacity. Chronic and progressive cognitive dysfunction, perpetuated by several positive feedback mechanisms in diabetic subjects, facilitates the development of early-onset dementia and Alzheimer’s disease. Despite the numerous clinical manifestations of DE having been described in detail and their pathophysiological substrate having been elucidated in both type 1 and type 2 diabetes mellitus, an effective therapeutic approach is yet to be proposed. Therefore, the aim of this review is to summarize the growing body of evidence concerning the effect of current antidiabetic treatment options on diabetic and non-DE.

Drug–Target Interaction Prediction Model Using Optimal Recurrent Neural Network

Drug-target interactions prediction(DTIP)remains an important requirement in thefield of drug discovery and human medicine.The identification of interaction among the drug compound and target protein plays an essential pro-cess in the drug discovery process.It is a lengthier and complex process for pre-dicting the drug target interaction(DTI)utilizing experimental approaches.To resolve these issues,computational intelligence based DTIP techniques were developed to offer an efficient predictive model with low cost.The recently devel-oped deep learning(DL)models can be employed for the design of effective pre-dictive approaches for DTIP.With this motivation,this paper presents a new drug target interaction prediction using optimal recurrent neural network(DTIP-ORNN)technique.The goal of the DTIP-ORNN technique is to predict the DTIs in a semi-supervised way,i.e.,inclusion of both labelled and unlabelled instances.Initially,the DTIP-ORNN technique performs data preparation process and also includes class labelling process,where the target interactions from the database are used to determine thefinal label of the unlabelled instances.Besides,drug-to-drug(D-D)and target-to-target(T-T)interactions are used for the weight initia-tion of the RNN based bidirectional long short term memory(BiLSTM)model which is then utilized to the prediction of DTIs.Since hyperparameters signifi-cantly affect the prediction performance of the BiLSTM technique,the Adam optimizer is used which mainly helps to improve the DTI prediction outcomes.In order to ensure the enhanced predictive outcomes of the DTIP-ORNN techni-que,a series of simulations are implemented on four benchmark datasets.The comparative result analysis shows the promising performance of the DTIP-ORNN method on the recent approaches.

基于知识图谱嵌入与深度学习的药物不良反应预测

识别药物潜在的不良反应,有助于辅助医生进行临床用药决策。针对以往研究的特征高维稀疏、需要为每种不良反应构建独立预测模型且预测精度较低的问题,本文开发一种基于知识图谱嵌入和深度学习的药物不良反应预测模型,能够对实验所覆盖的不良反应进行统一预测。一方面,知识图谱及其嵌入技术能够融合药物之间的关联信息,缓解特征矩阵高维稀疏的不足;另一方面,深度学习的高效训练能力能够提升模型的预测精度。本文使用药物特征数据构建药物不良反应知识图谱;通过分析不同嵌入策略下知识图谱的嵌入效果,选择最佳嵌入策略以获得样本向量;然后构建卷积神经网络模型对不良反应进行预测。结果表明,在DistMult嵌入模型和400维嵌入策略下,卷积神经网络模型预测效果最佳;重复实验的准确率、F_1分数、召回率和曲线下面积的平均值分别为0.887、0.890、0.913和0.957,优于文献报道中的方法。所得预测模型具有较好的预测精度和稳定性,可以为安全用药提供有效参考。

基于BERT和CNN的药物不良反应个例报道文献分类方法

在临床上,药物不良反应导致的死亡和用药不当造成的住院及门诊费急剧升高,成为临床安全合理用药面临的主要问题之一。目前对药物不良反应的回顾性分析和文献分析多以公开发表的文献资料为依据。学术文献作为重要的数据来源之一,如何自动批量地对其进行数据处理尤为重要。针对医药文本独特的表述方式,基于BERT及其组合模型进行文本分类技术比对实验,建立对药物不良反应个例报道文献数据进行高效快速分类的方法,进而分辨出药物不良反应的类型,有效预警药害事件。实验结果表明,使用BERT模型的分类准确率达到99.75%,其可以准确高效地对药物不良反应个例报道文献进行分类,在辅助医疗、构建医学文本结构化数据等方面均具有重要的价值和意义,进而能够更好地维护公众健康。

基于大数据挖掘下多重耐药菌风险评估的研究价值

目的 基于大数据构建多重耐药菌感染的风险预测模型,并对其应用价值进行评估。方法 收集2018年1月至2022年12月于新疆医科大学第五附属医院诊治的405例患者,根据是否发生多重耐药菌(multidrug-resistant organisms,MDRO)感染分为非MDRO组(n=324)和MDRO组(n=81),比较并分析各指标与MDRO发生风险的相关性。构建大数据风险预测模型,分析各指标重要性,验证其准确性。结果 MDRO组合并糖尿病、原发肺部感染的患者比例,机械通气、广谱抗菌药物使用时间及降钙素原水平显著高于非MDRO组,而血红蛋白、白蛋白水平显著低于非MDRO组(均P <0.05);相关性分析显示,合并糖尿病、原发肺部感染等因素与MDRO风险的相关性较高,且合并糖尿病与原发肺部感染及联合使用抗生素等指标间相关性较高;大数据模型示抗生素使用时间、吞咽功能障碍等因素重要性较高,而血红蛋白及白蛋白重要性较低;大数据模型预测MDRO发生风险的AUC显著高于Logistic回归模型(Z=2.415,P=0.016),两种预测模型的训练集预测准确率差异无统计学意义(P>0.05);但测试集大数据模型预测准确率、敏感度及特异度均显著高于Logistic回归模型(χ^(2)=9.062,5.385,4.267;均P<0.05)。结论 合并糖尿病、原发肺部感染及联合使用抗生素等因素与MDRO发生风险具有一定相关性,基于MDRO危险因素指标的大数据模型对MDRO发生风险具有较高预测价值。

西藏自治区人民医院实施国家重点监控药品干预成效分析

目的:评价西藏自治区人民医院对重点监控药品进行重点干预的成效,为优化重点监控药品干预策略、促进临床合理用药提供参考。方法:西藏自治区人民医院于2020年制订《西藏自治区人民医院重点监控药品管理规定》,建立《西藏自治区人民医院重点监控药品目录》,同时开展重点监控药品处方及医嘱专项点评、采取点评结果公示及绩效考核挂钩等目标性干预措施,对比西藏自治区人民医院2019年4月-2020年3月(干预前)与2020年4月-2021年3月(干预后第一年)及2021年4月-2022年3月(干预后第二年)重点监控药品临床使用数据变化,评价重点干预措施对该类药品的管理成效及临床使用的影响。结果:该院干预后第一年及第二年的重点监控药品销售金额分别为1427.01万元、1388.12万元,低于干预前的2004.29万元;干预后重点监控药品销售金额占药品总销售金额比例分别为8.33%、7.47%,低于干预前的10.11%。重点监控药品各品种的DDC普遍较高,患者的经济负担较重。结论:西藏自治区人民医院重点监控药品的干预取得了一定成效,但医院应在此基础上采取有力措施,提高重点监控药品合理使用,进一步减轻患者经济负担。

应用失效模式与效应分析法提高药房盘点质量探索

目的:优化现代化门诊药房药品盘点过程以提升盘点质量。方法:通过文献检索、头脑风暴等方法绘制药品盘点流程图并收集每个子流程的潜在失效模式及失效原因,应用失效模式与效应分析法(Failure Mode and Effect Analysis,FMEA)对各失效模式发生的可能性、严重性和可侦测度进行评分及风险优先值(RPN)计算,量化并确定高风险失效模式,制定改进措施并实施,分析改善效果。结果:确定了盘点的3个主流程和12个子流程,以及各子流程相关的21项失效模式和38项失效原因,高风险因素共15项,制定针对性改进措施28项。干预改进后,各高风险失效模式RPN值均显著降低,其中最高的4项由392、288、280、280分别降至42、48、56、63,均处于相对低风险区域;干预管理前后复盘相符率由82.4%上涨至96.2%,盘存时长由180.2 min降至155.3 min。结论:FMEA法在药品盘点过程存在问题分析改进中的价值是肯定的,制定的各项改进措施,尤其是针对现代化药房自动化设备盘存模块的相关措施,以及低代码平台在智能化盘点中的应用等对于盘点质量的提升作用非常显著,值得借鉴并推广运用来提升药品经济和质量管理。

基于Tri-training的社交媒体药物不良反应实体抽取

社交媒体因其数据的实时性,对其充分利用可以弥补传统医疗文献药物不良反应中实体抽取的迟滞性问题,但社交媒体文本面临标注数据成本高、数据噪声大等问题,使得模型难以发挥良好的效果。针对社交媒体大量未标注语料存在标注成本高的问题,采用Tri-training半监督的方法进行社交媒体药物不良反应实体抽取,通过三个学习器Transformer+CRF、BiLSTM+CRF和IDCNN+CRF对未标注数据进行标注,再利用一致性评价函数迭代地扩展训练集,最后通过加权投票整合模型输出标签。针对社交媒体的文本不正式性(口语化严重、错别字等)问题,通过融合字与词两个粒度的向量作为整个模型嵌入层的输入,来提取更丰富的语义信息。实验结果表明,提出的模型在“好大夫在线”网站获取的数据集上取得了良好表现。

自适应球形演化的药物-靶标相互作用预测方法

相较于传统药物的研发,药物-靶标的预测方法能够有效降低成本,加快研发进程,但是在实际应用中存在数据集平衡度低、预测精确率不高等问题。基于此,提出一种自适应球形演化的药物-靶标相互作用预测方法ASEKELM(self-Adaptive Spherical Evolution based on Kernel Extreme Learning Machine)。该方法根据结构相似的药物与靶标更易存在相互作用的原理筛选出高置信度的负样本;并且为了解决球形演化算法易陷入局部最优的问题,利用搜索因子历史记忆的反馈机制及群大小线性递减的策略(LPSR),实现全局搜索和局部搜索的平衡,提高算法的寻优能力;然后利用自适应球形演化算法对核极限学习机(KELM)的参数进行优化。在基于黄金标准的数据集上将ASEKELM与NetLapRLS(Network Laplacian Regularized Least Square)、BLM-NII(Bipartite Local Model with Neighbor-based Interaction profile Inferring)等算法进行对比,验证算法的性能。实验结果表明,在酶(E)、G-蛋白偶联受体(GPCR)、离子通道(IC)和核受体(NR)数据集中,ASE-KELM的ROC曲线下面积(AUC)与PR曲线下面积(AUPR)均优于对比算法;且基于DrugBank等数据库,ASE-KELM在预测新药物-靶标对的验证过程中表现良好。