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Found 12 papers, 1 papers with code
The Graph Convolutional Network with Multi-representation Alignment for Drug Synergy Prediction
The computational model based on deep learning concatenates the representation of multiple drugs and the corresponding cell line feature as input, and the output is whether the drug combination can have an inhibitory effect on the cell line.
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction
Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored.
ALT: An Automatic System for Long Tail Scenario Modeling
In this paper, we consider the problem of long tail scenario modeling with budget limitation, i. e., insufficient human resources for model training stage and limited time and computing resources for model inference stage.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.
Self-supervised Learning for Label Sparsity in Computational Drug Repositioning
Specifically, we take the drug-disease association prediction problem as the main task, and the auxiliary task is to use data augmentation strategies and contrast learning to mine the internal relationships of the original drug features, so as to automatically learn a better drug representation without supervised labels.
The Computational Drug Repositioning without Negative Sampling
The limitations of these works are mainly due to the following two reasons: firstly, previous works used negative sampling techniques to treat unvalidated drug-disease associations as negative samples, which is invalid in real-world settings; secondly, the inner product cannot fully take into account the feature information contained in the latent factor of drug and disease.
The Neural Metric Factorization for Computational Drug Repositioning
We novelly consider the latent factor vector of drugs and diseases as a point in the high-dimensional coordinate system and propose a generalized Euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product.
Hybrid Attentional Memory Network for Computational drug repositioning
Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations.
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
The insurance industry has been creating innovative products around the emerging online shopping activities.
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks
Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems.
Heterogeneous Graph Neural Networks for Malicious Account Detection
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.
