Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (11): 3238-3245.doi: 10.13229/j.cnki.jdxbgxb.20220007
Previous Articles Next Articles
Feng-feng ZHOU1(),Zhen-wei YAN2
CLC Number:
1 | 王莹. 基于机器学习的神经肽前体及其剪切位点的预测[D]. 成都: 电子科技大学生物科学与技术学院, 2021. |
Wang Ying. Prediction of neuropeptide precursor and its cleavage site based on machine learning[D]. Chengdu: School of Life Science and Technology, University of Electronic Science and Technology of China, 2021. | |
2 | Bin Y N, Zhang W, Tang W D, et al. Prediction of neuropeptides from sequence information using ensemble classifier and hybrid features[J]. Journal of Proteome Research, 2020, 19(9): 3732-3740. |
3 | Hayakawa E, Watanabe H, Menschaert G, et al. A combined strategy of neuropeptide prediction and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone Nematostella vectensis[J]. PLoS ONE, 2019, 14(9): 0215185. |
4 | Manayalan B, Basith S, Shin T H, et al. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation[J]. Bioinformatics, 2019, 35(16): 2757-2765. |
5 | Akbar S, Hayat M, Iqbal M, et al. iACP-GAEnsC: evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space[J]. Artificial Intelligence in Medicine, 2017, 79: 62-70. |
6 | Karsenty S, Rappoport N, Ofer D, et al. NeuroPID: a classifier of neuropeptide precursors[J]. Nucleic Acids Research, 2014, 42(1): 182-186. |
7 | Kang J J, Fang Y W, Yap P C, et al. NeuroPP: a tool for the prediction of neuropeptide precursors based on optimal sequence composition[J]. Interdisciplinary Sciences-Computational Life Sciences, 2019, 11(1): 108-114. |
8 | Agrawal P, Kumar S, Singh A, et al. NeuroPIpred: a tool to predict, design and scan insect neuropeptides[J]. Scientific Reports, 2019, 9(1): 20195129. |
9 | Cheng N, Li M L, Zhao L, et al. Comparison and integration of computational methods for deleterious synonymous mutation prediction[J]. Briefings in Bioinformatics, 2020, 21(3): 970-981. |
10 | Wang Y, Wang M X, Yin S W, et al. NeuroPep: a comprehensive resource of neuropeptides[J]. Database-the Journal of Biological Databases and Curation, 2015, 2015: 25931458. |
11 | Matallana-Surget S, Chang R L, Chan A. Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage[J]. The EMBO Journal, 2020, 39(23): 33073387. |
12 | Petrilli P. Classification of protein sequences by their dipeptide composition[J]. Computer Application in the Biosciences, 1993, 9(2): 205-209. |
13 | Kawashima S, Pokarowski P, Pokarowska M, et al. AAindex: amino acid index database, progress report 2008[J]. Nucleic Acids Research, 2008, 36: 202-205. |
14 | Ken N, Yasushi K, Tatsuo O. Classification of proteins into groups based on amino acid composition and other characters. II. grouping into four types[J]. Journal of Biochemistry, 1983, 94(3): 997-1007. |
15 | Lee T Y, Lin Z Q, Hsieh S J, et al. Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences[J]. Bioinformatics, 2011, 27(13): 1780-1787. |
16 | Yang L W, Gao H, Wu K Y, et al. Identification of cancerlectins by using cascade linear discriminant analysis and optimal g-gap tripeptide composition[J]. Current Bioinformatics, 2020, 15(6): 528-537. |
17 | Xia J F, Zhao X M, Huang D S. Predicting protein-protein interactions from protein sequences using meta predictor[J]. Amino Acids, 2010, 39(5): 1595-1599. |
18 | 施启军, 潘峰, 龙福海, 等. 特征选择方法研究综述[J]. 微电子学与计算机, 2022, 39(3): 1-8. |
Shi Qi-jun, Pan Feng, Long Fu-hai, et al. Summary of research on feature selection methods[J]. Microelectronics & Computer, 2022, 39(3): 1-8. | |
19 | 吴璐. 基于SVM-RFE特征选择的规则提取方法[J]. 微型电脑应用, 2021, 37(9): 150-154. |
Wu Lu. Rule extraction method based on SVM-RFE feature selection[J]. Microcomputer Application, 2021, 37(9): 150-154. | |
20 | 赵若宇. Lasso及其相关优化模型在临床预测中的应用[D]. 大连: 大连理工大学数学科学学院, 2021. |
Zhao Ruo-yu, Lasso and its related optimization models in clinical prediction[D]. Dalian: School of Mathematical Science, Dalian University of Technology, 2021. | |
21 | 张保华, 黄文倩, 李江波, 等. 基于I-RELIEF和SVM的畸形马铃薯在线分选[J]. 吉林大学学报: 工学版, 2014, 44(6): 1811-1817. |
Zhang Bao-hua, Huang Wen-qian, Li Jiang-bo,et al. Online sorting of irregular potatoes based on I-RELIEF and SVM method[J]. Journal of Jilin University(Engineering and Technology Edition), 2014,44(6): 1811-1817. | |
22 | 杨玉玲. 特征选择与集成方法的研究及应用[D]. 兰州: 兰州大学数学与统计学院, 2021. |
Yang Yu-ling. Research on feature selection and integration method and it's applications[D]. Lanzhou: School of mathematics and statistics, Lanzhou University, 2021. | |
23 | 曲铭. 基于集成学习特征选择的新闻流行度预测研究[D]. 济南: 山东大学中泰证券金融研究院, 2021. |
Qu Ming. Research on news popularity prediction based on ensemble learning feature selection[D]. Jinan: Zhongtai Securities Finance Research Institute, Shandong University, 2021. | |
24 | 王斌, 何丙辉, 林娜, 等. 基于随机森林特征选择的茶园遥感提取[J]. 吉林大学学报: 工学版, 2022, 52(7): 1719-1732. |
Wang Bin, He Bing-hui, Lin Na, et al. Tea plantation remote sensing extraction based on random forest[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(7): 1719-1732. | |
25 | Tang J J, Liang J, Han C Y, et al. Crash injury severity analysis using a two-layer Stacking framework[J]. Accident Analysis and Prevention, 2019, 122: 226-238. |
[1] | Ya-hui ZHAO,Fei-yu LI,Rong-yi CUI,Guo-zhe JIN,Zhen-guo ZHANG,De LI,Xiao-feng JIN. Korean⁃Chinese translation quality estimation based on cross⁃lingual pretraining model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2371-2379. |
[2] | Qing-tian GENG,Zhi LIU,Qing-liang LI,Fan-hua YU,Xiao-ning LI. Prediction of soil moisture based on a deep learning model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2430-2436. |
[3] | Shan XUE,Ya-liang ZHANG,Qiong-ying LYU,Guo-hua CAO. Anti⁃unmanned aerial vehicle system object detection algorithm under complex background [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 891-901. |
[4] | Heng-yan PAN,Wen-hui ZHANG,Ting-ting LIANG,Zhi-peng PENG,Wei GAO,Yong-gang WANG. Inducement analysis of taxi drivers' traffic accidents based on MIMIC and machine learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 457-467. |
[5] | Qing-tian GENG,Yang ZHAO,Qing-liang LI,Fan-hua YU,Xiao-ning LI. Integrated LSTM and ARIMA method based on attention model for soil temperature [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 2973-2981. |
[6] | Hui GUO,Jie-di FU,Zhen-dong LI,Yan YAN,Xiao LI. SVM parameters and feature selection optimization based on improved whale algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 2952-2963. |
[7] | Bing ZHU,Zi-wei LI,Qi LI. Building segmentation method of remote sensing image based on improved SegNet [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 248-254. |
[8] | Jun-jie WANG,Yuan-jun NONG,Li-te ZHANG,Pei-chen ZHAI. Visual relationship detection method based on construction scene [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 226-233. |
[9] | Gui-he QIN,Jun-feng HUANG,Ming-hui SUN. Text input based on two⁃handed keyboard in virtual environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1881-1888. |
[10] | Tian BAI,Ming-wei XU,Si-ming LIU,Ji-an ZHANG,Zhe WANG. Dispute focus identification of pleading text based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1872-1880. |
[11] | Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871. |
[12] | Pei-ze LI,Shi-shun ZHAO,Xiao-hui WENG,Xin-mei JIANG,Hong-bo CUI,Jian-lei QIAO,Zhi-yong CHANG. A new method for rapid detection of pesticide residues based on multi⁃sensor optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1951-1956. |
[13] | Feng-feng ZHOU,Hai-yang ZHU. SEE: sense EEG⁃based emotion algorithm via three⁃step feature selection strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1834-1841. |
[14] | Bin WANG,Bing-hui HE,Na LIN,Wei WANG,Tian-yang LI. Tea plantation remote sensing extraction based on random forest feature selection [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1719-1732. |
[15] | Sheng-sheng WANG,Lin-yan JIANG,Yong-bo YANG. Transfer learning of medical image segmentation based on optimal transport feature selection [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1626-1638. |
|