Zhixia Teng

TL;DR: The results show that the proposed CFRP-based prediction model achieves better performances than the others in term of the evaluation metrics, and the complex features generated by CFRP are beneficial for building a powerful predicting model of ncRNA-protein interaction.

TL;DR: Wang et al. as discussed by the authors used bidirectional LSTM (EBLSTM) to extract subsequences by sliding a 3-mer window along the DNA sequence as features.

TL;DR: The results suggest that the proposed support vector machine- (SVM-) based classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs.

TL;DR: A novel optimization framework to detect complexes from protein-protein interaction (PPI) network, named PLSMC, which can match known complexes with a higher accuracy than other methods and has high functional homogeneity.

TL;DR: Li et al. as mentioned in this paper used a multifeature encoding scheme consisting of Kmer and EIIP to describe the DNA sequences and developed a stacked ensemble model, in which four machine learning algorithms, namely, BayesNet, NaiveBayes, LibSVM and Voted Perceptron, were utilized to implement an ensemble of base classifiers that produce intermediate results as input of the metaclassifier, Logistic.