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Protein structure
Results: 1992

#	Item
51	Microsoft Word - Masters project 2.docx Add to Reading List Source URL: www.csnd.nl Language: English - Date: 2014-09-05 05:48:46 Protein structure Structural biology TGF beta receptors Transcription factors R-SMAD SMAD Transmembrane receptors Transforming growth factor beta Mothers against decapentaplegic homolog 2 ACVR1B Activin and inhibin TGF beta receptor 1
52	world wide Protein Data Bank Advisory Committee (wwPDBAC) Report of October 27th 2006 Meeting Tokyo, Japan Chair: Stephen K. Burley (RCSB) PDB Site Representatives: Wayne A. Hendrickson (RCSB, excused), Neil Isaacs (MSD) Add to Reading List Source URL: cdn.wwpdb.org Language: English - Date: 2016-07-21 15:06:04 Biological databases Chemistry Software Academia Worldwide Protein Data Bank Protein Data Bank Helen M. Berman RasMol PDB Stephen K. Burley Structure validation
53	Local Packing Density Is the Main Structural Determinant of the Rate of Protein Sequence Evolution at Site Level Add to Reading List Source URL: www.ncbi.nlm.nih.gov Language: English Biology Bioinformatics Protein structure Computational phylogenetics Nucleic acids Solvents Nucleic acid sequence Substitution matrix Sequence alignment Protein domain STING Solvent exposure
54	Table of Contents Use Cases (Target Problems)...........................................................................................................................1 Family alignment: identify homologues, generate fa Add to Reading List Source URL: informatics.nescent.org Language: English - Date: 2016-04-13 13:52:16 Biology Bioinformatics Computational phylogenetics Genetics Population genetics Molecular biology Protein structure Sequence alignment Single-nucleotide polymorphism BLAST Protein superfamily Conserved sequence
55	Genome Informatics 11: 151–Clustering and Averaging of Images in Single-Particle Analysis Add to Reading List Source URL: www.jsbi.org Language: English - Date: 2001-02-21 03:45:32 Cluster analysis Statistics Image processing Microscopes Protein structure Single particle analysis Data analysis Hierarchical clustering Determining the number of clusters in a data set Computational statistics K-means clustering Transmission electron microscopy
56	We present new ideas which attempt to explain why Deep Learning works, taking lessons from Theoretical Chemistry, and integrating ideas from Protein Folding, Renormalization Group, and Quantum Chemistry. We address the i Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-06-23 15:50:48 Physics Theoretical physics Mathematical physics Quantum field theory Renormalization group Academia Statistical mechanics Protein structure Renormalization Deep learning Protein folding Energy landscape
57	Modeling Protein Interaction Networks: From Structure to Function Alessandro Flammini, School of Informatics, IUB Add to Reading List Source URL: vw.slis.indiana.edu - Date: 2005-02-10 17:34:28
58	About the exercises background information on request on topics not, or insufficiently covered during main lectures questions about the lectures, or exercises Add to Reading List Source URL: wwwuser.gwdg.de Language: English - Date: 2009-11-24 02:44:24 Physics Condensed matter physics Protein structure Nuclear magnetic resonance Biophysics Electron
59	Genome Informatics 15(2): 181–Predicting Protein Secondary Structure by a Support Vector Machine Based on a New Coding Scheme Add to Reading List Source URL: www.jsbi.org Language: English - Date: 2004-12-21 03:19:05 Biology Protein structure Structural biology Chemistry Bioinformatics Protein methods Helices Proteomics Protein secondary structure PSIPRED Protein structure prediction Alpha helix
60	322 Genome Informatics 13: 322–Prediction of Protein-Protein Interaction Sites Using Support Vector Machines Add to Reading List Source URL: www.jsbi.org Language: English - Date: 2002-12-19 23:03:36 Biology Bioinformatics Chemistry Protein structure Biochemistry Proteomics Proteinprotein interaction Systems biology Protein secondary structure ProteinDNA interaction site predictor Residue depth

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