• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 87 matches

• 2020
• 
  Meng Liang, Junlan Nie:
  Prediction of Enzyme Function Based on a Structure Relation Network. IEEE Access 8: 132360-132366 (2020)
• 
  Liming Qiu, Xiaoqin Zou:
  Scoring functions for protein-RNA complex structure prediction: advances, applications, and future directions. Commun. Inf. Syst. 20(1): 1-22 (2020)
• 
  Xingqian Chen, Shuangbao Song, Junkai Ji, Zheng Tang, Yuki Todo:
  Incorporating a multiobjective knowledge-based energy function into differential evolution for protein structure prediction. Inf. Sci. 540: 69-88 (2020)
• 
  Titas De:
  Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens. CoRR abs/2004.01029 (2020)
• 
  Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluç, Dmitriy Morozov, Katherine A. Yelick:
  PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction. CoRR abs/2010.16027 (2020)
• 2019
• 
  Chao Wang, Yi Wei, Haicang Zhang, Lupeng Kong, Shiwei Sun, Wei-Mou Zheng, Dongbo Bu:
  Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling. BMC Bioinform. 20-S(3): 99-108 (2019)
• 
  Wei Zheng, Chengxin Zhang, Eric W. Bell, Yang Zhang:
  I-TASSER gateway: A protein structure and function prediction server powered by XSEDE. Future Gener. Comput. Syst. 99: 73-85 (2019)
• 
  Xinxiang Wang, Sheng-You Huang:
  Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction. J. Chem. Inf. Model. 59(6): 3080-3090 (2019)
• 
  Amaninder Singh Gill, Joshua D. Summers, Chiradeep Sen:
  Impact of Chaining Method and Level of Completion on Accuracy of Function Structure-Based Market Price Prediction Models. J. Comput. Inf. Sci. Eng. 19(4) (2019)
• 2018
• 
  Shuangbao Song, Junkai Ji, Xingqian Chen, Shangce Gao, Zheng Tang, Yuki Todo:
  Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction. Appl. Soft Comput. 72: 539-551 (2018)
• 
  Noor AlHuda Ali A. H. Saeed, Israa Hussein Hamzah, Alaa Naseer Mohammed Ali, Abdulwahab Ali Abuderman:
  Prediction of single nucleotide polymorphisms (SNPs) in apolipoprotein E gene and their possible associations with a deleterious effect on the structure and functional properties: an in silico approach. Netw. Model. Anal. Health Informatics Bioinform. 7(1): 16 (2018)
• 
  Christian Klos, Daniel Miner, Jochen Triesch:
  Bridging structure and function: A model of sequence learning and prediction in primary visual cortex. PLoS Comput. Biol. 14(6) (2018)
• 2017
• 
  Amaninder Singh Gill, Joshua D. Summers, Cameron J. Turner:
  Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value. Artif. Intell. Eng. Des. Anal. Manuf. 31(4): 550-566 (2017)
• 
  Chengxin Zhang, Peter L. Freddolino, Yang Zhang:
  COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Res. 45(Webserver-Issue): W291-W299 (2017)
• 
  Gregorio Kappaun Rocha, Jaqueline S. Angelo, Karina B. Santos, Fábio L. Custódio, Laurent Emmanuel Dardenne, Helio J. C. Barbosa:
  Using an aggregation tree to arrange energy function terms for protein structure prediction. CIBCB 2017: 1-7
• 
  Sandhya Parasnath Dubey, N. Gopalakrishna Kini, S. Balaji, M. Sathish Kumar:
  Protein structure prediction on 2D square HP lattice with revised fitness function. ICACCI 2017: 1732-1736
• 
  Or Litany, Tal Remez, Emanuele Rodolà, Alexander M. Bronstein, Michael M. Bronstein:
  Deep Functional Maps: Structured Prediction for Dense Shape Correspondence. ICCV 2017: 5660-5668
• 
  Alessio Martino, Enrico Maiorino, Alessandro Giuliani, Mauro Giampieri, Antonello Rizzi:
  Supervised Approaches for Function Prediction of Proteins Contact Networks from Topological Structure Information. SCIA (1) 2017: 285-296
• 
  Or Litany, Tal Remez, Emanuele Rodolà, Alexander M. Bronstein, Michael M. Bronstein:
  Deep Functional Maps: Structured Prediction for Dense Shape Correspondence. CoRR abs/1704.08686 (2017)
• 2016
• 
  Alvin Farrel, Jonathan Murphy, Jun-tao Guo:
  Structure-based prediction of transcription factor binding specificity using an integrative energy function. Bioinform. 32(12): 306-313 (2016)
• 
  Md. Tamjidul Hoque, Yuedong Yang, Avdesh Mishra, Yaoqi Zhou:
  sDFIRE: Sequence-specific statistical energy function for protein structure prediction by decoy selections. J. Comput. Chem. 37(12): 1119-1124 (2016)
• 
  R. Shamima, S. Suresh:
  Prediction of membrane protein structures using a Projection based Meta-cognitive Radial Basis Function Network. IJCNN 2016: 1229-1235
• 
  Ivan Escobar, Nicolas Hidalgo, Mario Inostroza-Ponta, Mauricio Marín, Erika Rosas, Márcio Dorn:
  Evaluation of a combined energy fitness function for a distributed memetic algorithm to tackle the 3D protein structure prediction problem. SCCC 2016: 1-10
• 2015
• 
  Albert J. Kooistra, Rob Leurs, Iwan J. P. de Esch, Chris de Graaf:
  Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A β-Adrenoceptor Case Study. J. Chem. Inf. Model. 55(5): 1045-1061 (2015)
• 
  Jianyi Yang, Yang Zhang:
  I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res. 43(Webserver-Issue): W174-W181 (2015)
• 
  Navid Nourani-Vatani, Roberto Javier López-Sastre, Stefan B. Williams:
  Structured Output Prediction with Hierarchical Loss Functions for Seafloor Imagery Taxonomic Categorization. IbPRIA 2015: 173-183
• 
  Hossein Hajimirsadeghi, Greg Mori:
  Learning Ensembles of Potential Functions for Structured Prediction with Latent Variables. ICCV 2015: 4059-4067
• 2014
• 
  Yuxiang Jiang, Wyatt T. Clark, Iddo Friedberg, Predrag Radivojac:
  The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective. Bioinform. 30(17): 609-616 (2014)
• 
  Anshuman Dixit, Gennady Verkhivker:
  Structure-Functional Prediction and Analysis of Cancer Mutation Effects in Protein Kinases. Comput. Math. Methods Medicine 2014: 653487:1-653487:24 (2014)
• 
  Huda Amin Maghawry, Mostafa G. M. Mostafa, Tarek F. Gharib:
  A New Protein Structure Representation for Efficient Protein Function Prediction. J. Comput. Biol. 21(12): 936-946 (2014)

skipping 57 more matches