Search Results for author: Xinxing Yang

Found 12 papers, 1 papers with code

The Graph Convolutional Network with Multi-representation Alignment for Drug Synergy Prediction

no code implementations27 Nov 2023 Xinxing Yang, Genke Yang, Jian Chu

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

no code implementations18 Jul 2023 Xinxing Yang, Genke Yang, Jian Chu

Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored.

Contrastive Learning Drug Discovery +1

ALT: An Automatic System for Long Tail Scenario Modeling

no code implementations19 May 2023 Ya-Lin Zhang, Jun Zhou, Yankun Ren, Yue Zhang, Xinxing Yang, Meng Li, Qitao Shi, Longfei Li

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.

Meta-Learning Neural Architecture Search +1

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

no code implementations13 Feb 2023 Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, Jun Zhou, Chaochao Chen, Fei Wu, Guanfeng Liu, Yan Wang

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.

counterfactual Multi-Task Learning +1

Self-supervised Learning for Label Sparsity in Computational Drug Repositioning

no code implementations1 Jun 2022 Xinxing Yang, Genke Yang, Jian Chu

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.

Data Augmentation Drug Discovery +1

The Computational Drug Repositioning without Negative Sampling

no code implementations29 Nov 2021 Xinxing Yang, Genke Yang, Jian Chu

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

no code implementations16 Sep 2021 Xinxing Yang, Genke Yangand Jian Chu

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.

Common Sense Reasoning Drug Discovery

Hybrid Attentional Memory Network for Computational drug repositioning

no code implementations12 Jun 2020 Jieyue He, Xinxing Yang, Zhuo Gong, lbrahim Zamit

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.

Collaborative Filtering Drug Discovery

SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

no code implementations5 Mar 2020 Qitao Shi, Ya-Lin Zhang, Longfei Li, Xinxing Yang, Meng Li, Jun Zhou

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.

BIG-bench Machine Learning Feature Engineering

Heterogeneous Graph Neural Networks for Malicious Account Detection

1 code implementation27 Feb 2020 Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song

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

no code implementations18 Jun 2019 Shaosheng Cao, Xinxing Yang, Cen Chen, Jun Zhou, Xiaolong Li, Yuan Qi

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.

Fraud Detection

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