Q
Qin-Lai Huang
Researcher at University of Electronic Science and Technology of China
Publications - 7
Citations - 164
Qin-Lai Huang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 4 publications receiving 27 citations.
Papers
More filters
Journal ArticleDOI
iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.
Dan Zhang,Hua-Dong Chen,Hasan Zulfiqar,Shi-Shi Yuan,Qin-Lai Huang,Zhao-Yue Zhang,Ke-Jun Deng +6 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel predicting framework for identifying bioluminescent proteins based on eXtreme gradient boosting algorithm (XGBoost) and using sequence-derived features.
Journal ArticleDOI
Identification of cyclin protein using gradient boost decision tree algorithm.
TL;DR: In this paper, a gradient boost decision tree (GBDT) classifier was trained on the optimal features to identify cyclins with an accuracy of 93.06% and AUC value of 0.971.
Journal ArticleDOI
iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based algorithm to identify whether an unknown sequence region would be potential hypersensitive site (DHS), which showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs.
Journal ArticleDOI
Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique
TL;DR: In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii.
Journal ArticleDOI
Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli.
Hasan Zulfiqar,Lourdes Peña-Castillo,Zi-Jie Sun,Qin-Lai Huang,Shi-Shi Yuan,Hao Lv,Fu-Ying Dao,Hao Lin,Yan-Wen Li +8 more
TL;DR: Wang et al. as discussed by the authors developed a deep learning-based model to predict 4mC sites in the Escherichia coli genome, where DNA sequences were encoded by word embedding technique 'word2vec'.