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Graph Transformer for drug response prediction [article]

Tuan Thanh Nguyen, Thang Chu
2021 bioRxiv   pre-print
This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy.  ...  Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations.  ...  To enhance the prediction accuracy, Graph Transformer considers the positional encoding, which encodes the distance-aware information.  ... 
doi:10.1101/2021.11.29.470386 fatcat:odgatllrcjbenb2fdbefdtvx2a

DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning

Jesús Fernando Cevallos Moreno, Peyman Zarrineh, Aminael Sánchez-Rodríguez, Massimo Mecella
2022 F1000Research  
Deep graph auto-encoders and an adaptive-sparsity generative model are the algorithmic core of DeepReGraph.  ...  mechanisms into a suitable objective function for graph embedding.  ...  Caulier for their valuable insights. References  ... 
doi:10.12688/f1000research.114698.1 fatcat:td5yj4szo5g55heqi3sjpmnv5i

Advances in Collaborative Filtering and Ranking [article]

Liwei Wu
2020 arXiv   pre-print
graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative  ...  In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with  ...  Conclusion In this chapter, we proposed Graph DNA, a deep neighborhood aware encoding scheme for collaborative filtering with graph information.  ... 
arXiv:2002.12312v1 fatcat:eam7lntrrremlpgs4dcq427dm4

Adversarial Attack and Defense on Graph Data: A Survey [article]

Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, Bo Li
2020 arXiv   pre-print
Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis.  ...  Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial  ...  Such mechanism could improve the robustness of GCN-based collaborative filtering models.  ... 
arXiv:1812.10528v3 fatcat:5eiqm6f7xzdltc5klvef44jghe

A A Survey of the Link Prediction on Static and Temporal Knowledge Graph

Thanh Le, Hoang Nguyen, Bac Le
2021 Research and Development on Information and Communication Technology  
Finally, from the overview of the link prediction problem, we propose some directions to improve the models for future studies.  ...  Based on that, the correlation of the two graph types in link prediction is drawn.  ...  Usually, the recommendation system has applied collaborative filtering algorithms to produce reasonable recommendations.  ... 
doi:10.32913/mic-ict-research.v2021.n2.972 fatcat:vuvve5rzsbfgzpz5ax3sbqqxli

Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation [article]

Jing Yi, Xubin Ren, Zhenzhong Chen
2022 arXiv   pre-print
In this paper, we propose a multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) for tag recommendation, which couples item collaborative information and item multi-auxiliary information  ...  In addition, an inductive variational graph auto-encoder is designed where new item nodes could be inferred in the test phase, such that item social embeddings could be exploited for new items.  ...  Moreover, an inductive variational graph auto-encoder (VGAE) is proposed to solve the problem of adding nodes in the graph by sub-graph sampling and neighborhood aggregation.  ... 
arXiv:2204.09422v1 fatcat:zozcuc526fbjxp4d5t7kah3t24

Personalized News Recommendation: Methods and Challenges [article]

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 arXiv   pre-print
Next, we introduce the public datasets and evaluation methods for personalized news recommendation.  ...  We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face.  ...  collaborative filtering.  ... 
arXiv:2106.08934v3 fatcat:iagqsw73hrehxaxpvpydvtr26m

2020 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 17

2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
., +, TCBB March-April 2020 704-711 Pancreas Data-Driven Robust Control for a Closed-Loop Artificial Pancreas. 1981 -1993  ...  Deep Collaborative Filtering for Prediction of Disease Genes. Zeng, X., +, Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity.  ...  -Feb. 2020 321-326 Deep Collaborative Filtering for Prediction of Disease Genes. Zeng, X., +, TCBB Sept.-Oct. 2020 1639-1647 Efficient Mining Multi-Mers in a Variety of Biological Sequences.  ... 
doi:10.1109/tcbb.2020.3047571 fatcat:x3kmrpexsve6bnjtd3dh6ntkyy

Recent Advances in Network-based Methods for Disease Gene Prediction [article]

Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li
2020 arXiv   pre-print
Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods.  ...  To summarize, we first elucidate the task definition for disease gene prediction.  ...  PCFM [47] Probability-based collaborative filtering models with different regularization terms.  ... 
arXiv:2007.10848v1 fatcat:zhrspbsj6zfpfhwa42mzjp4lvy

Graphical pangenomics

Erik Garrison, Richard Durbin
2018 Zenodo  
I find that variation aware read alignment can eliminate reference bias at known variants, and this is of particular importance in the analysis of ancient DNA, where existing approaches result in significant  ...  As this model combines both sequence and variation information in one structure it serves as a natural basis for resequencing.  ...  the nodes in their neighborhood in the graph 16 .  ... 
doi:10.5281/zenodo.3269840 fatcat:glracsk2jvgb3lehvepq2jl5l4

Machine Learning Applications for Therapeutic Tasks with Genomics Data [article]

Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun
2021 arXiv   pre-print
We also pinpoint seven important challenges in this field with opportunities for expansion and impact.  ...  In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development.  ...  Graph Neural Networks aggregate information from the local neighborhood to update the node embedding. g. Autoencoders reconstruct the input from an encoded compact latent space. h.  ... 
arXiv:2105.01171v1 fatcat:d2nbrjt4tvak7momoxxjlmqk2m

Big networks: A survey

Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He Guo, Feng Xia
2020 Computer Science Review  
, visited, and bought. • Collaborative-filtering: Recommendation methods designed on the basis of collaborative-based filtering notify users by collaborating information from multiple users history.  ...  There are different approaches to design a recommendation method, such as content-based filtering, collaborative-filtering, and hybrid-filtering [141] . • Content-based filtering: Recommendation methods  ... 
doi:10.1016/j.cosrev.2020.100247 fatcat:pmuxvvbprnc4jcy3gqmrjyf4tq

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Learning and Hand Crafted Features for Mining Simulation Data DAY 2 -Jan 13, 2021 Rodrigo-Bonet, Esther; Nguyen, Minh Duc; Deligiannis, Nikos 1300 Temporal Collaborative Filtering with Graph  ...  Ranks for Compressing Neural Networks DAY 4 -Jan 15, 2021 Deng, Jiehui; Wan, Sheng; Wang, Xiang; Tu, enmei; Huang, Xiaolin; Yang, Jie; Gong, Chen 2767 Edge-Aware Graph Attention Network for Ratio of Edge-User  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Novel prediction methods for virtual drug screening [article]

Josip Mesarić
2022 arXiv   pre-print
Deep learning is to stay in drug discovery but has a long way to go.  ...  One of key parts of the early drug discovery process has become virtual drug screening -- a method used to narrow down search for potential drugs by running computer simulations of drug-target interactions  ...  [67] for DNA/RNA docking based on a hybrid model of template-based modeling and free docking.  ... 
arXiv:2202.06635v1 fatcat:cab5pvnvw5httnuksmb4ke2piy

Random Fields in Physics, Biology and Data Science

Enrique Hernández-Lemus
2021 Frontiers in Physics  
A random field is the representation of the joint probability distribution for a set of random variables.  ...  For strictly positive probability densities, a Markov random field is also a Gibbs field, i.e., a random field supplemented with a measure that implies the existence of a regular conditional distribution  ...  By combing a deep learning approach (a convolutional neural network) with MRF models, Liu and collaborators [166] devised an effective algorithm for semantic segmentation [167] , which they called a  ... 
doi:10.3389/fphy.2021.641859 fatcat:2bi74vqkureefmtzwinma2yiwq
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