Drug-target interaction prediction by learning from local information and neighbors

By Jianxiang Mei, Chee Keong Kwoh, Peng Yang, Xiaoli Li, Jie Zheng. Bioinform., 2013

347 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/38963150e4b3623f5ba01711eaf163cc8f4f3113

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jianyang Zeng
2. Min Wu
3. Jian Peng
4. Peilin Zhao
5. Chunyan Miao
6. Ruth Nussinov
7. Peter Csermely
8. Yanqing Zhang

In Molecular Biology

1. Jianyang Zeng
2. Min Wu
3. Jian Peng
4. Peilin Zhao
5. Chunyan Miao
6. Ruth Nussinov
7. Rui Kuang
8. Peter Csermely

Citations in the corpus

• Predicting drug-target interactions using restricted Boltzmann machines (2013, methodology): “…latent features of DTI profiles and have recently become a popular tool for DTI prediction and drug repositioning (Bleakley and Yamanishi, 2009; Chen et al ., 2012; Cheng et al ., 2012; Mei et al ., 2012; van Laarhoven et al ., 2011; Xia et al ., 2010; Xie et al ., 2012; Yamanishi et al ., 2008).”
• Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, positive): “…successfully used to predict and experimentally verify novel drug actions (Keiser et al., 2007, 2009; Ma'ayan et al., 2007; Yildirim et al., 2007; Hert et al., 2008; Yamanishi et al., 2008, 2010; Bleakley & Yamanishi, 2009; van Laarhoven et al., 2011; Nacher & Schwartz, 2012; Mei et al., 2013).”
• Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “The paper 75 also applied a supervised bipartite model to predict the probability of each drug – target interaction based on the known drug targets as labels and the target – target interactions as features, where the bipartite model was augmented with additional training samples from the…”
• Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “In this paper, we compared the proposed NRLMF method with the following state-of-the-art methods, namely, NetLapRLS [13], KBMF2K [24], BLM-NII [17], WNN-GIP [15], and CMF [27], by testing their prediction capabilities under the above three settings.”
• A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, methodology): “We compared DTINet with four state-of-the-art methods of DTI prediction methods, including BLMNII [39], NetLapRLS [76], HNM [70] and CMF [79].”
• Toward more realistic drug–target interaction predictions (2014, neutral): “However, in the context of paired input problems, such as prediction of protein–protein or drug–target interactions, one should in practice consider separately the settings where the training and test sets share common drugs or proteins [8, 14–16].”
• Pairwise input neural network for target-ligand interaction prediction (2014, methodology): “1) Performance evaluation: In order to evaluate the performances of PINN, we used compare PINN with other two representative approaches by 5-fold cross-validation: BLMNII [12] and CS-PD [10].”