Proceedings Article10.1145/3488560.3498435
Can
Weijie Bian,Kailun Wu,Lejian Ren,Qi Pi,Yujing Zhang,Can Xiao,Xiang-Rong Sheng,Yongzhe Zhu,Zhangming Chan,Na Mou,Xinchen Luo,S. Xiang,Guorui Zhou,Xiaoqiang Zhu,Hongbo Deng +14 more
- 11 Feb 2022
43
TL;DR: In this article , the authors describe a drug-drug synergy mediated by allosteric cross-talk in chromatin, whereby the binding of one drug alters the activity of the second.
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Abstract: Exploitation of drug–drug synergism and allostery could yield superior therapies by capita-lizing on the immensely diverse, but highly specific, potential associated with the biological macromolecular landscape. Here we describe a drug–drug synergy mediated by allosteric cross-talk in chromatin, whereby the binding of one drug alters the activity of the second. We found two unrelated drugs, RAPTA-T and auranofin, that yield a synergistic activity in killing cancer cells, which coincides with a substantially greater number of chromatin adducts formed by one of the compounds when adducts from the other agent are also present. We show that this occurs through an allosteric mechanism within the nucleosome, whereby defined histone adducts of one drug promote reaction of the other drug at a distant, specific histone site. This opens up possibilities for epigenetic targeting and suggests that allosteric modulation in nucleosomes may have biological relevance and potential for therapeutic interventions. additive effect, a CI o 1 indicates synergy and a CI 4 1 antagonism. The creators of the CompuSyn software have proposed that CI values be interpreted as follows: CI 1.1, antagonistic effect; CI ¼ 0.9–1.1, additive effect; mild synergism; CI ¼ 0.3–0.7, synergism; strong synergism; and CI o synergism.
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References
Neural Collaborative Filtering
Xiangnan He,Lizi Liao,Hanwang Zhang,Liqiang Nie,Xia Hu,Tat-Seng Chua +5 more
- 03 Apr 2017
TL;DR: This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng,Levent Koc,Jeremiah Harmsen,Tal Shaked,Tushar Deepak Chandra,Hrishi Aradhye,Glen Anderson,Greg S. Corrado,Wei Chai,Mustafa Ispir,Rohan Anil,Zakaria Haque,Lichan Hong,Vihan Jain,Xiaobing Liu,Hemal Shah +15 more
- 15 Sep 2016
TL;DR: Wide & Deep learning is presented---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems and is open-sourced in TensorFlow.
4K
Factorization Machines
Steffen Rendle
- 13 Dec 2010
TL;DR: Factorization Machines (FM) are introduced which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models and can mimic these models just by specifying the input data (i.e. the feature vectors).
3.5K
A new model for learning in graph domains
Marco Gori,Gabriele Monfardini,Franco Scarselli +2 more
- 27 Dec 2005
TL;DR: A new neural model, called graph neural network (GNN), capable of directly processing graphs, which extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs.
2.4K
Deep Interest Network for Click-Through Rate Prediction
Guorui Zhou,Xiaoqiang Zhu,Chenru Song,Ying Fan,Han Zhu,Xiao Ma,Yan Yanghui,Junqi Jin,Han Li,Kun Gai +9 more
- 19 Jul 2018
TL;DR: A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.