Статті в журналах з теми "Protein Representation Learning"
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Kim, Paul T., Robin Winter, and Djork-Arné Clevert. "Unsupervised Representation Learning for Proteochemometric Modeling." International Journal of Molecular Sciences 22, no. 23 (November 28, 2021): 12882. http://dx.doi.org/10.3390/ijms222312882.
Повний текст джерелаHeinzinger, Michael, Christian Dallago, and Burkhard Rost. "Protein matchmaking through representation learning." Cell Systems 12, no. 10 (October 2021): 948–50. http://dx.doi.org/10.1016/j.cels.2021.09.007.
Повний текст джерелаFasoulis, Romanos, Georgios Paliouras, and Lydia E. Kavraki. "Graph representation learning for structural proteomics." Emerging Topics in Life Sciences 5, no. 6 (October 19, 2021): 789–802. http://dx.doi.org/10.1042/etls20210225.
Повний текст джерелаRives, Alexander, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, et al. "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences." Proceedings of the National Academy of Sciences 118, no. 15 (April 5, 2021): e2016239118. http://dx.doi.org/10.1073/pnas.2016239118.
Повний текст джерелаWarikoo, Neha, Yung-Chun Chang, and Shang-Pin Ma. "Gradient Boosting over Linguistic-Pattern-Structured Trees for Learning Protein–Protein Interaction in the Biomedical Literature." Applied Sciences 12, no. 20 (October 11, 2022): 10199. http://dx.doi.org/10.3390/app122010199.
Повний текст джерелаChornozhuk, S. "The New Geometric “State-Action” Space Representation for Q-Learning Algorithm for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 3 (October 27, 2020): 59–73. http://dx.doi.org/10.34229/2707-451x.20.3.6.
Повний текст джерелаYao, Yu, Xiuquan Du, Yanyu Diao, and Huaixu Zhu. "An integration of deep learning with feature embedding for protein–protein interaction prediction." PeerJ 7 (June 17, 2019): e7126. http://dx.doi.org/10.7717/peerj.7126.
Повний текст джерелаGarruss, Alexander S., Katherine M. Collins, and George M. Church. "Deep representation learning improves prediction of LacI-mediated transcriptional repression." Proceedings of the National Academy of Sciences 118, no. 27 (June 29, 2021): e2022838118. http://dx.doi.org/10.1073/pnas.2022838118.
Повний текст джерелаRahman, Julia, Nazrul Islam Mondal, Khaled Ben Islam, and Al Mehedi Hasan. "Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction." Journal of Integrative Bioinformatics 13, no. 1 (March 1, 2016): 23–33. http://dx.doi.org/10.1515/jib-2016-288.
Повний текст джерелаJin, Chen, Zhuangwei Shi, Chuanze Kang, Ken Lin, and Han Zhang. "TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction." International Journal of Molecular Sciences 23, no. 2 (January 16, 2022): 972. http://dx.doi.org/10.3390/ijms23020972.
Повний текст джерелаLöchel, Hannah F., Dominic Eger, Theodor Sperlea, and Dominik Heider. "Deep learning on chaos game representation for proteins." Bioinformatics 36, no. 1 (June 21, 2019): 272–79. http://dx.doi.org/10.1093/bioinformatics/btz493.
Повний текст джерелаLi, Yan, Yu-Ren Zhang, Ping Zhang, Dong-Xu Li, and Tian-Long Xiao. "Protein–Protein Interactions Prediction Base on Multiple Information Fusion via Graph Representation Learning." Journal of Biomaterials and Tissue Engineering 12, no. 4 (April 1, 2022): 807–12. http://dx.doi.org/10.1166/jbt.2022.2953.
Повний текст джерелаOrasch, Oliver, Noah Weber, Michael Müller, Amir Amanzadi, Chiara Gasbarri, and Christopher Trummer. "Protein–Protein Interaction Prediction for Targeted Protein Degradation." International Journal of Molecular Sciences 23, no. 13 (June 24, 2022): 7033. http://dx.doi.org/10.3390/ijms23137033.
Повний текст джерелаKabir, Anowarul, and Amarda Shehu. "GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction." Biomolecules 12, no. 11 (November 18, 2022): 1709. http://dx.doi.org/10.3390/biom12111709.
Повний текст джерелаCruz-Barbosa, Raúl, Erik-German Ramos-Pérez, and Jesús Giraldo. "Representation Learning for Class C G Protein-Coupled Receptors Classification." Molecules 23, no. 3 (March 19, 2018): 690. http://dx.doi.org/10.3390/molecules23030690.
Повний текст джерелаAlley, Ethan C., Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M. Church. "Unified rational protein engineering with sequence-based deep representation learning." Nature Methods 16, no. 12 (October 21, 2019): 1315–22. http://dx.doi.org/10.1038/s41592-019-0598-1.
Повний текст джерелаDíaz-Eufracio, Bárbara I., and José L. Medina-Franco. "Machine Learning Models to Predict Protein–Protein Interaction Inhibitors." Molecules 27, no. 22 (November 17, 2022): 7986. http://dx.doi.org/10.3390/molecules27227986.
Повний текст джерелаBussey, Thomas J., and MaryKay Orgill. "Biochemistry instructors’ use of intentions for student learning to evaluate and select external representations of protein translation." Chemistry Education Research and Practice 20, no. 4 (2019): 787–803. http://dx.doi.org/10.1039/c9rp00025a.
Повний текст джерелаTsubaki, Masashi, Masashi Shimbo, and Yuji Matsumoto. "Protein Fold Recognition with Representation Learning and Long Short-Term Memory." IPSJ Transactions on Bioinformatics 10 (2017): 2–8. http://dx.doi.org/10.2197/ipsjtbio.10.2.
Повний текст джерелаLi, Bo, Lijun Cai, Bo Liao, Xiangzheng Fu, Pingping Bing, and Jialiang Yang. "Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features." Molecules 24, no. 5 (March 6, 2019): 919. http://dx.doi.org/10.3390/molecules24050919.
Повний текст джерелаCretin, Gabriel, Tatiana Galochkina, Alexandre G. de Brevern, and Jean-Christophe Gelly. "PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction." International Journal of Molecular Sciences 22, no. 16 (August 17, 2021): 8831. http://dx.doi.org/10.3390/ijms22168831.
Повний текст джерелаYan, Zichao, William L. Hamilton, and Mathieu Blanchette. "Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions." Bioinformatics 36, Supplement_1 (July 1, 2020): i276—i284. http://dx.doi.org/10.1093/bioinformatics/btaa456.
Повний текст джерелаXia, Chunqiu, Shi-Hao Feng, Ying Xia, Xiaoyong Pan, and Hong-Bin Shen. "Fast protein structure comparison through effective representation learning with contrastive graph neural networks." PLOS Computational Biology 18, no. 3 (March 24, 2022): e1009986. http://dx.doi.org/10.1371/journal.pcbi.1009986.
Повний текст джерелаDai, Bowen, and Chris Bailey-Kellogg. "Protein interaction interface region prediction by geometric deep learning." Bioinformatics 37, no. 17 (March 8, 2021): 2580–88. http://dx.doi.org/10.1093/bioinformatics/btab154.
Повний текст джерелаvan den Bent, Irene, Stavros Makrodimitris, and Marcel Reinders. "The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction." Evolutionary Bioinformatics 17 (January 2021): 117693432110626. http://dx.doi.org/10.1177/11769343211062608.
Повний текст джерелаLiu, Xianggen, Yunan Luo, Pengyong Li, Sen Song, and Jian Peng. "Deep geometric representations for modeling effects of mutations on protein-protein binding affinity." PLOS Computational Biology 17, no. 8 (August 4, 2021): e1009284. http://dx.doi.org/10.1371/journal.pcbi.1009284.
Повний текст джерелаXie, Ziwei, and Jinbo Xu. "Deep graph learning of inter-protein contacts." Bioinformatics 38, no. 4 (November 10, 2021): 947–53. http://dx.doi.org/10.1093/bioinformatics/btab761.
Повний текст джерелаWang, Xian-Fang, Peng Gao, Yi-Feng Liu, Hong-Fei Li, and Fan Lu. "Predicting Thermophilic Proteins by Machine Learning." Current Bioinformatics 15, no. 5 (October 14, 2020): 493–502. http://dx.doi.org/10.2174/1574893615666200207094357.
Повний текст джерелаZhang, Haiping, Konda Mani Saravanan, Jinzhi Lin, Linbu Liao, Justin Tze-Yang Ng, Jiaxiu Zhou, and Yanjie Wei. "DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation." PeerJ 8 (April 6, 2020): e8864. http://dx.doi.org/10.7717/peerj.8864.
Повний текст джерелаLiu, Xiang, Huitao Feng, Jie Wu, and Kelin Xia. "Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction." PLOS Computational Biology 18, no. 4 (April 6, 2022): e1009943. http://dx.doi.org/10.1371/journal.pcbi.1009943.
Повний текст джерелаJing, Xiaoyang, Qimin Dong, Ruqian Lu, and Qiwen Dong. "Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications." Current Bioinformatics 14, no. 3 (March 7, 2019): 178–89. http://dx.doi.org/10.2174/1574893613666181109130430.
Повний текст джерелаGokcan, Hatice, and Olexandr Isayev. "Prediction of protein pKa with representation learning." Chemical Science 13, no. 8 (2022): 2462–74. http://dx.doi.org/10.1039/d1sc05610g.
Повний текст джерелаSun, Miao, Dong Si, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Renzhi Cao, and John Smith. "TopQA: a topological representation for single-model protein quality assessment with machine learning." International Journal of Computational Biology and Drug Design 13, no. 1 (2020): 144. http://dx.doi.org/10.1504/ijcbdd.2020.10026784.
Повний текст джерелаSmith, John, Matthew Conover, Natalie Stephenson, Jesse Eickholt, Dong Si, Miao Sun, and Renzhi Cao. "TopQA: a topological representation for single-model protein quality assessment with machine learning." International Journal of Computational Biology and Drug Design 13, no. 1 (2020): 144. http://dx.doi.org/10.1504/ijcbdd.2020.105095.
Повний текст джерелаBramer, David, and Guo-Wei Wei. "Atom-specific persistent homology and its application to protein flexibility analysis." Computational and Mathematical Biophysics 8, no. 1 (February 17, 2020): 1–35. http://dx.doi.org/10.1515/cmb-2020-0001.
Повний текст джерелаWiercioch, Magdalena. "Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction." International Journal of Molecular Sciences 20, no. 9 (May 2, 2019): 2175. http://dx.doi.org/10.3390/ijms20092175.
Повний текст джерелаWang, Yanbin, Zhu-Hong You, Shan Yang, Xiao Li, Tong-Hai Jiang, and Xi Zhou. "A High Efficient Biological Language Model for Predicting Protein–Protein Interactions." Cells 8, no. 2 (February 3, 2019): 122. http://dx.doi.org/10.3390/cells8020122.
Повний текст джерелаZhang, Ting-He, and Shao-Wu Zhang. "Advances in the Prediction of Protein Subcellular Locations with Machine Learning." Current Bioinformatics 14, no. 5 (June 28, 2019): 406–21. http://dx.doi.org/10.2174/1574893614666181217145156.
Повний текст джерелаBae, Haelee, and Hojung Nam. "GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity." Biomedicines 11, no. 1 (December 27, 2022): 67. http://dx.doi.org/10.3390/biomedicines11010067.
Повний текст джерелаRassinoux, A. M. "Knowledge Representation and Management." Yearbook of Medical Informatics 19, no. 01 (August 2010): 64–67. http://dx.doi.org/10.1055/s-0038-1638691.
Повний текст джерелаNido, Gonzalo S., Ludovica Bachschmid-Romano, Ugo Bastolla, and Alberto Pascual-García. "Learning structural bioinformatics and evolution with a snake puzzle." PeerJ Computer Science 2 (December 5, 2016): e100. http://dx.doi.org/10.7717/peerj-cs.100.
Повний текст джерелаQu, Kaiyang, Leyi Wei, and Quan Zou. "A Review of DNA-binding Proteins Prediction Methods." Current Bioinformatics 14, no. 3 (March 7, 2019): 246–54. http://dx.doi.org/10.2174/1574893614666181212102030.
Повний текст джерелаJin, Yuan, Jiarui Lu, Runhan Shi, and Yang Yang. "EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction." Biomolecules 11, no. 12 (November 29, 2021): 1783. http://dx.doi.org/10.3390/biom11121783.
Повний текст джерелаTubiana, Jérôme, Simona Cocco, and Rémi Monasson. "Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins." Neural Computation 31, no. 8 (August 2019): 1671–717. http://dx.doi.org/10.1162/neco_a_01210.
Повний текст джерелаWei, Lesong, Xiucai Ye, Tetsuya Sakurai, Zengchao Mu, and Leyi Wei. "ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning." Bioinformatics 38, no. 6 (January 6, 2022): 1514–24. http://dx.doi.org/10.1093/bioinformatics/btac006.
Повний текст джерелаWang, Duolin, Yanchun Liang, and Dong Xu. "Capsule network for protein post-translational modification site prediction." Bioinformatics 35, no. 14 (December 6, 2018): 2386–94. http://dx.doi.org/10.1093/bioinformatics/bty977.
Повний текст джерелаXu, Chang, Limin Jiang, Zehua Zhang, Xuyao Yu, Renhai Chen, and Junhai Xu. "An Integrated Prediction Method for Identifying Protein-Protein Interactions." Current Proteomics 17, no. 4 (June 29, 2020): 271–86. http://dx.doi.org/10.2174/1570164616666190306152318.
Повний текст джерелаNair B.J, Bipin, and Lijo Joy. "A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i1.9.9752.
Повний текст джерелаWang, Huiqing, Juan Wang, Zhipeng Feng, Ying Li, and Hong Zhao. "PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability." International Journal of Molecular Sciences 23, no. 20 (October 16, 2022): 12385. http://dx.doi.org/10.3390/ijms232012385.
Повний текст джерелаNguyen, Trinh‐Trung‐Duong, Nguyen‐Quoc‐Khanh Le, Quang‐Thai Ho, Dinh‐Van Phan, and Yu‐Yen Ou. "Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain." Molecular Informatics 39, no. 10 (July 16, 2020): 2000033. http://dx.doi.org/10.1002/minf.202000033.
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