interactions

2023-12-24An integrated method for predicting binding sites of protein-RNA interactions based on data balancin
会议地点：腾讯会议关键词：数据平衡；蛋白质-RNA相互作用作者：TongZhou,JieRong,YangLiu,WeikangGong,ChunhuaLi期刊：Bioinformatics年份：2022论文原文:https://academic.oup.com/bioinformatics/article-abstract/38/9/2452/6543608补充材料：主要内容问题：识别蛋白质-RNA相互作用
2023-12-18Predicting Drug-Target Interactions. drug-target interactions prediction
2023[j22]JunjunZhang, MinzhuXie:Graphregularizednon-negativematrixfactorizationwithL2,1 normregularizationtermsfordrug-targetinteractionsprediction. BMCBioinform. 24(1): 375 (2023)2022[j21]JunjunZhan
2023-12-08Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for
Graphregularizednon-negativematrixfactorizationwithpriorknowledgeconsistencyconstraintfordrug-targetinteractionspredictionJunjunZhang 1, MinzhuXie 2 3Affiliations expandPMID: 36581822 PMCID: PMC9798666 DOI: 10.1186/s1285
2023-12-08LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm
LPI-IBWA:PredictinglncRNA-proteininteractionsbasedonanimprovedBi-RandomwalkalgorithmMinzhuXie 1, RuijieXie 2, HaoWang 3Affiliations expandPMID: 37972912 DOI: 10.1016/j.ymeth.2023.11.007 SigninAbstractManystudies
2023-10-12Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
目录概Fi-GNN代码LiZ.,CuiZ.,WuS.,ZhangX.andWangL.Fi-GNN:Modelingfeatureinteractionsviagraphneuralnetworksforctrprediction.CIKM,2019.概"图网络"用在精排阶段(算哪门子图网络啊).Fi-GNN一个item可能有多种field,比如:\[\underbrace
2023-09-14Paper reading: Improving Deep Forest by Exploiting High-order Interactions
目录研究动机文章贡献本文方法通过gRIT和ERF提取特征交互特征交互的稳定性分数自适应层次生成实验结果合成数据集实验真实数据集实验数据集实验设置实验结果计算复杂度优点和创新点PaperReading是从个人角度进行的一些总结分享，受到个人关注点的侧重和实力所限，可能有理解不
2023-02-02wpf中Interaction.Behaviors详解
在WPF4.0中，引入了一个比较实用的库——Interactions，这个库主要是通过附加属性来对UI控件注入一些新的功能，除了内置了一系列比较好用的功能外，还提供了比较良好的扩展接口。
2022-09-05【ARXIV2207】HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
【ARXIV2207】HorNet:EfficientHigh-OrderSpatialInteractionswithRecursiveGatedConvolutions论文地址：https://hornet.ivg-research.xyz代码地址：https://githu
2022-08-22Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networ
动机本文是2017年IJCAI上的一篇论文。FM方法通过结合二阶特征交互来增强线性回归模型，它将这些特征交互一视同仁，给予它们一个相同的权重，但是并不是所有特征的交互都是有意