M
Min Wu
Researcher at Institute for Infocomm Research Singapore
Publications - 129
Citations - 4210
Min Wu is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 23, co-authored 118 publications receiving 2483 citations. Previous affiliations of Min Wu include Nanyang Technological University & Agency for Science, Technology and Research.
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Computational approaches for detecting protein complexes from protein interaction networks: a survey
TL;DR: The state-of-the-art techniques for computational detection of protein complexes are reviewed, some promising research directions in this field are discussed, and experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes.
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A core-attachment based method to detect protein complexes in PPI networks
TL;DR: A novel core-attachment based method (COACH) which detects protein complexes in two stages and includes attachments into these cores to form biologically meaningful structures, which shows that COACH performs significantly better than the state-of-the-art techniques.
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Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.
TL;DR: The proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively.
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Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization
TL;DR: Two matrix factorization methods that use graph regularization in order to learn low-dimensional non-linear manifolds are proposed and developed, which achieved better results than three other state-of-the-art methods in most cases.
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Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
TL;DR: An attention-based deep learning framework is proposed for machine's RUL prediction that is able to learn the importance of features and time steps, and assign larger weights to more important ones, and the proposed approach outperforms the state-of-the-arts.