These measures can effectively process sequential and pairwise features to predict the contact probability between residues and improve the prediction accuracy. Protein Contact Map Prediction Based on ResNet and DenseNet, School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China, School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China, Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Solna 17121, Sweden, School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China,,, J. Friedman, T. Hastie, and R. Tibshirani, “Sparse inverse covariance estimation with the graphical lasso,”, S. Seemayer, M. Gruber, and J. Söding, “CCMpred - fast and precise prediction of protein residue–residue contacts from correlated mutations,”, L. Kaján, T. A. Hopf, M. Kalaš, D. S. Marks, and B. Rost, “FreeContact: fast and free software for protein contact prediction from residue co-evolution,”, H. Kamisetty, S. Ovchinnikov, and D. Baker, “Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era,”, D. T. Jones, D. W. A. Buchan, D. Cozzetto, and M. Pontil, “PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments,”, M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Support vector machines,”, J. Cheng and P. Baldi, “Improved residue contact prediction using support vector machines and a large feature set,”, J. Yang, Q. Y. Jin, B. Zhang, and H. B. Shen, “R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter,”, P. Baldi and G. Pollastri, “The principled design of large-scale recursive neural network architectures–dag-rnns and the protein structure prediction problem,”, G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,”, J. Cheng and P. Baldi, “Three-stage prediction of protein -sheets by neural networks, alignments and graph algorithms,”, P. Di Lena, K. Nagata, and P. Baldi, “Deep architectures for protein contact map prediction,”, D. Xiong, J. Zeng, and H. Gong, “A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy,”, A. N. Tegge, Z. Wang, J. Eickholt, and J. Cheng, “NNcon: improved protein contact map prediction using 2d-recursive neural networks,”, D. T. Jones, T. Singh, T. Kosciolek, and S. Tetchner, “MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins,”, S. Wang, S. Sun, Z. Li, R. Zhang, and J. Xu, “Accurate de novo prediction of protein contact map by ultra-deep learning model,”, B. Adhikari, J. Hou, and J. Cheng, “DNCON2: improved protein contact prediction using two-level deep convolutional neural networks,”, D. T. Jones and S. M. Kandathil, “High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features,”, J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, and A. Tramontano, “Critical assessment of methods of protein structure prediction (CASP) - round XII,”, A. Kryshtafovych, T. Schwede, M. Topf, K. Fidelis, and J. Moult, “Critical assessment of methods of protein structure prediction (CASP) - round XIII,”, M. Michel, D. Menéndez Hurtado, and A. Elofsson, “PconsC4: fast, accurate and hassle-free contact predictions,”, K. He, X. Zhang, S. Ren, and J. Quality comparison (measured by TMscore)…, Fig 5. With = protein sequence length and = feature dimension, PSSM is represented by a two-dimensional matrix of , the secondary structure is represented by a two-dimensional matrix of , the solvent accessibility is denoted by a two-dimensional matrix of , and the PSFM is represented by a two-dimensional matrix of (Figure 2). Experimental determination of protein structure is time-consuming and expensive; therefore, accurate protein structure prediction can play a vital role in understanding protein function.


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