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Publication search results
found 9 matches
- 2024
- Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola:
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. CoRR abs/2402.04997 (2024) - 2019
- Hongjie Wu, Ru Yang, Qiming Fu, Jianping Chen, Weizhong Lu, Haiou Li:
Research on predicting 2D-HP protein folding using reinforcement learning with full state space. BMC Bioinform. 20-S(25): 685 (2019) - 2018
- Nasrin Akhter, Jing Lei, Wanli Qiao, Amarda Shehu:
Reconstructing and Decomposing Protein Energy Landscapes to Organize Structure Spaces and Reveal Biologically-active States. BIBM 2018: 56-60 - 2015
- Berat Dogan, Tamer Ölmez:
A novel state space representation for the solution of 2D-HP protein folding problem using reinforcement learning methods. Appl. Soft Comput. 26: 213-223 (2015) - 2010
- Brian S. Olson, Kevin Molloy, Amarda Shehu:
Enhancing Sampling of the Conformational Space Near the Protein Native State. BIONETICS 2010: 249-263 - Osamu Hirose, Kentaro Shimizu:
A Self-organizing State Space Approach to Inferring Time-Varying Causalities between Regulatory Proteins. ITBAM 2010: 158-171 - 2009
- Christos Lampros, Costas Papaloukas, Costas P. Exarchos, Dimitrios I. Fotiadis, Dimitrios G. Tsalikakis:
Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model. Comput. Biol. Medicine 39(10): 907-914 (2009) - Dietmar Seipel, Jörg Schultz:
Searching in Protein State Space. INAP 2009: 205-222 - 2007
- Christos Lampros, Costas Papaloukas, Themis P. Exarchos, Yorgos Goletsis, Dimitrios I. Fotiadis:
Sequence-based protein structure prediction using a reduced state-space hidden Markov model. Comput. Biol. Medicine 37(9): 1211-1224 (2007)
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