In this research, we propose the Motif-based Mass Spectrum prediction Network (MoMS-Net), a GNN-based architecture to predict the mass spectra pattern utilizing the structural motif information of the molecule. MoMS-Net considers both a molecule and its substructures as a graph form, which ...
graphtensorflowmotifmetagraphgraph-convolutional-networksgraph-neural-networks UpdatedApr 29, 2021 C++ Motiflets timeseriestime-seriespatternmotifpattern-recognitionunsupervised-learningpattern-discoverymotif-discoverytime-series-analysismotifsdataseries UpdatedFeb 28, 2025 ...
Traditional social link prediction models primarily concentrate on the adjacency features of the network, overlooking the rich high-order structural information within. Therefore, the study of effective extraction and encoding of these high-order feature
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas. Recent works have exposed that GNN is vulnerable to the backdoor attack, i.e., models trained with maliciously crafted training samples are easily fooled by patched samples. Most of the...
identification of an unknown protein as a member of a COG immediately suggests its probable function. As new complete genomes are being constantly added to the COG database, it is likely to become an extremely effective tool for protein function prediction. Similarity searching against the COG data...
Deciphering cis-regulatory elements or de novo motif-finding in genomes still remains elusive although much algorithmic effort has been expended. The Markov chain Monte Carlo (MCMC) method such as Gibbs motif samplers has been widely employed to solve th
Neural network for graphs: A contextual constructive approach. IEEE Trans Neural Netw. 2009; 20(3):498–511. Article Google Scholar Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem ...
Auto uning of price prediction models for high-frequency trading via reinforcement learning 2022, Pattern Recognition Citation Excerpt : In the above trading framework, if we have a machine learning model library [22], changes in these model library will have a quite significant impact on real pro...
The data set has positives (TFs with known NMs) and negatives (TFs to which randomly chosen NMs are assigned) with equal proportions. This data set is used to train the SVM classifiers. The classifiers are evaluated through the LOOCV approach to estimate their prediction errors. In the ...
Our work employs a TBTH architecture, which combines the RNAErnie basic block with a hybrid neural network inspired by ref. 46. This hybrid neural network acts as the interaction prediction head, sequentially incorporating several components: a convolutional neural network, a bidirectional long short...