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Статті в журналах з теми "Protein Representation Learning"
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 джерелаДисертації з теми "Protein Representation Learning"
Tubiana, Jérôme. "Restricted Boltzmann machines : from compositional representations to protein sequence analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
Повний текст джерелаRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
(6326255), Stefan M. Irby. "Evaluation of a Novel Biochemistry Course-Based Undergraduate Research Experience (CURE)." Thesis, 2019.
Знайти повний текст джерелаCourse-based Undergraduate Research Experiences (CUREs) have been described in a range of educational contexts. Although various learning objectives, termed anticipated learning outcomes (ALOs) in this project, have been proposed, processes for identifying them may not be rigorous or well-documented, which can lead to inappropriate assessment and speculation about what students actually learn from CUREs. Additionally, evaluation of CUREs has primarily relied on student and instructor perception data rather than more reliable measures of learning.This dissertation investigated a novel biochemistry laboratory curriculum for a Course-based Undergraduate Research Experience (CURE) known as the Biochemistry Authentic Scientific Inquiry Lab (BASIL). Students participating in this CURE use a combination of computational and biochemical wet-lab techniques to elucidate the function of proteins of known structure but unknown function. The goal of the project was to evaluate the efficacy of the BASIL CURE curriculum for developing students’ research abilities across implementations. Towards achieving this goal, we addressed the following four research questions (RQs): RQ1) How can ALOs be rigorously identified for the BASIL CURE; RQ2) How can the identified ALOs be used to develop a matrix that characterizes the BASIL CURE; RQ3) What are students’ perceptions of their knowledge, confidence and competence regarding their abilities to perform the top-rated ALOs for this CURE; RQ4) What are appropriate assessments for student achievement of the identified ALOs and what is the nature of student learning, and related difficulties, developed by students during the BASIL CURE? To address these RQs, this project focused on the development and use of qualitative and quantitative methods guided by constructivism and situated cognition theoretical frameworks. Data was collected using a range of instruments including, content analysis, Qualtrics surveys, open-ended questions and interviews, in order to identify ALOs and to determine student learning for the BASIL CURE. Analysis of the qualitative data was through inductive coding guided by the concept-reasoning-mode (CRM) model and the assessment triangle, while analysis of quantitative data was done by using standard statistical techniques (e.g. conducting a parried t-test and effect size). The results led to the development of a novel method for identifying ALOs, namely a process for identifying course-based undergraduate research abilities (PICURA; RQ1; Irby, Pelaez, & Anderson 2018b). Application of PICURA to the BASIL CURE resulted in the identification and rating by instructors of a wide range of ALOs, termed course-based undergraduate research abilities (CURAs), which were formulated into a matrix (RQs 2; Irby, Pelaez, & Anderson, 2018a,). The matrix was, in turn, used to characterize the BASIL CURE and to inform the design of student assessments aimed at evaluating student development of the identified CURAs (RQs 4; Irby, Pelaez, & Anderson, 2018a). Preliminary findings from implementation of the open-ended assessments in a small case study of students, revealed a range of student competencies for selected top-rated CURAs as well as evidence for student difficulties (RQ4). In this way we were able to confirm that students are developing some of the ALOs as actual learning outcomes which we term VLOs or verified learning outcomes. In addition, a participant perception indicator (PPI) survey was used to gauge students’ perceptions of their gains in knowledge, experience, and confidence during the BASIL CURE and, therefore, to inform which CURAs should be specifically targeted for assessment in specific BASIL implementations (RQ3;). These results indicate that, across implementations of the CURE, students perceived significant gains with large effect sizes in their knowledge, experience, and confidence for items on the PPI survey (RQ3;). In our view, the results of this dissertation will make important contributions to the CURE literature, as well as to the biochemistry education and assessment literature in general. More specifically, it will significantly improve understanding of the nature of student learning from CUREs and how to identify ALOs and design assessments that reveal what students actually learn from such CUREs - an area where there has been a dearth of available knowledge in the past. The outcomes of this dissertation could also help instructors and administrators identify and align assessments with the actual features of a CURE (or courses in general), use the identified CURAs to ensure the material fits departmental or university needs, and evaluate the benefits of students participating in these innovative curricula. Future research will focus on expanding the development and validation of assessments so that practitioners can better evaluate the efficacy of their CUREs for developing the research competencies of their undergraduate students and continue to render improvements to their curricula.
Книги з теми "Protein Representation Learning"
Lv, Zhibin, Hong Wenjing, and Xue Xu, eds. Feature Representation and Learning Methods With Applications in Protein Secondary Structure. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-555-8.
Повний текст джерелаFaflik, David. Urban Formalism. Fordham University Press, 2020. http://dx.doi.org/10.5422/fordham/9780823288045.001.0001.
Повний текст джерелаЧастини книг з теми "Protein Representation Learning"
Dawn, Sucheta, and Monidipa Das. "Graph Representation Learning for Protein Classification." In Artificial Intelligence Technologies for Computational Biology, 1–28. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003246688-1.
Повний текст джерелаRahman, Taseef, Yuanqi Du, and Amarda Shehu. "Graph Representation Learning for Protein Conformation Sampling." In Computational Advances in Bio and Medical Sciences, 16–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17531-2_2.
Повний текст джерелаQuadrini, Michela, Sebastian Daberdaku, and Carlo Ferrari. "Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites." In Machine Learning, Optimization, and Data Science, 409–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64580-9_34.
Повний текст джерелаZhou, Peixuan, Yijia Zhang, Fei Chen, Kuo Pang, and Mingyu Lu. "Heterogeneous PPI Network Representation Learning for Protein Complex Identification." In Bioinformatics Research and Applications, 217–28. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23198-8_20.
Повний текст джерелаCantoni, Virginio, Alessio Ferone, Ozlem Ozbudak, and Alfredo Petrosino. "Protein Structural Blocks Representation and Search through Unsupervised NN." In Artificial Neural Networks and Machine Learning – ICANN 2012, 515–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_64.
Повний текст джерелаMa, Wenzheng, Wenzheng Bao, Yi Cao, Bin Yang, and Yuehui Chen. "Prediction of Protein-Protein Interaction Based on Deep Learning Feature Representation and Random Forest." In Intelligent Computing Theories and Application, 654–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84532-2_59.
Повний текст джерелаHaj Mohamed, Hela, Samir Belaid, and Wady Naanaa. "RingNet: Geometric Deep Representation Learning for 3D Multi-domain Protein Shape Retrieval." In Computational Collective Intelligence, 135–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16014-1_12.
Повний текст джерелаNanni, Luca. "Computational Inference of DNA Folding Principles: From Data Management to Machine Learning." In Special Topics in Information Technology, 79–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_7.
Повний текст джерелаOrhobor, Oghenejokpeme I., Joseph French, Larisa N. Soldatova, and Ross D. King. "Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology." In Discovery Science, 374–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_25.
Повний текст джерелаFeinstein, Joseph, Wentao Shi, J. Ramanujam, and Michal Brylinski. "Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications." In Methods in Molecular Biology, 299–312. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1209-5_17.
Повний текст джерелаТези доповідей конференцій з теми "Protein Representation Learning"
Zhang, Da, and Mansur R. Kabuka. "Multimodal Deep Representation Learning for Protein-Protein Interaction Networks." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621366.
Повний текст джерелаXia, Tian, Bo Hui, and Wei-Shinn Ku. "APIP: Attention-based Protein Representation Learning for Protein-Ligand Interface Prediction." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020490.
Повний текст джерелаXia, Tian, and Wei-Shinn Ku. "Geometric Graph Representation Learning on Protein Structure Prediction." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467323.
Повний текст джерелаXu, Bo, Kun Li, Xiaoxia Liu, Delong Liu, Yijia Zhang, Hongfei Lin, Zhihao Yang, Jian Wang, and Feng Xia. "Protein Complexes Detection Based on Global Network Representation Learning." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621541.
Повний текст джерелаQuan, Zhe, Yan Guo, Xuan Lin, Zhi-Jie Wang, and Xiangxiang Zeng. "GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983267.
Повний текст джерелаZhou, Peixuan, Yijia Zhang, Fei Chen, Mingyu Lu, Wen Qu, Hongfei Lin, and Xiaoxia Liu. "Contrastive Self-Supervised Representation Learning for Protein Complexes Identification." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995094.
Повний текст джерелаZhijian, Lyu, Jiang Shaohua, Liang Yigao, and Gao Min. "GDGRU-DTA: Predicting Drug-Target Binding Affinity based on GNN and Double GRU." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120703.
Повний текст джерелаWang, Zhizheng, Yuanyuan Sun, Yawen Guan, Yibin Zhang, Liang Yang, Kan Xu, Yijia Zhang, and Hongfei Lin. "A Weak Supervised Learning Method for Essential Protein Detection Based on STRING Database and Learning Representation." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621469.
Повний текст джерелаArango-Rodriguez, J. D., A. F. Cardona-Escobar, J. A. Jaramillo-Garzon, and J. C. Arroyave-Ospina. "Machine learning based protein-protein interaction prediction using physical-chemical representations." In 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA). IEEE, 2016. http://dx.doi.org/10.1109/stsiva.2016.7743304.
Повний текст джерелаAlam, Fardina Fathmiul, Taseef Rahman, and Amarda Shehu. "Learning Reduced Latent Representations of Protein Structure Data." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307339.3343866.
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