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structur (32)
predict (25)
use (18)
fold (16)
base (14)

Stem protein$ (all stems)

101 papers:

ICMLICML-2015-PachecoS #approach #pseudo
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach (JP, EBS), pp. 2200–2208.
DACDAC-2014-XueQBYT #analysis #framework #manycore #scalability
Disease Diagnosis-on-a-Chip: Large Scale Networks-on-Chip based Multicore Platform for Protein Folding Analysis (YX, ZQ, PB, FY, CYT), p. 6.
ICMLICML-c1-2014-ZhouT #generative #network #predict #probability
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.
ICMLICML-c2-2014-McGibbonRSKP #comprehension #markov #modelling
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models (RM, BR, MS, GK, VSP), pp. 1197–1205.
ICPRICPR-2014-GienTCL #fuzzy #learning #multi #predict
Dual Fuzzy Hypergraph Regularized Multi-label Learning for Protein Subcellular Location Prediction (JG, YYT, CLPC, YL), pp. 512–516.
ICPRICPR-2014-OkadaFWP #analysis #random
Microenvironment-Based Protein Function Analysis by Random Forest (KO, LF, MW, DP), pp. 3138–3143.
MLDMMLDM-2014-WangLK #concurrent #data mining #mining
Applications of Concurrent Sequential Patterns in Protein Data Mining (CW, JL, MK), pp. 243–257.
SIGMODSIGMOD-2013-LapeineHHG #data analysis #interactive #mobile #query
Mobile interaction and query optimizationin a protein-ligand data analysis system (ML, KGH, EH, NMG), pp. 1291–1292.
KDDKDD-2013-TabeiKKY #constraints #scalability #similarity
Succinct interval-splitting tree for scalable similarity search of compound-protein pairs with property constraints (YT, AK, MK, YY), pp. 176–184.
SACSAC-2013-NardelliTB #behaviour
Cross-lattice behavior of general ACO folding for proteins in the HP model (MN, LT, AB), pp. 1320–1327.
ICPRICPR-2012-CantoniOFP #analysis
Structural analysis of protein Secondary Structure by GHT (VC, OO, AF, AP), pp. 1767–1770.
ICPRICPR-2012-SuryantoJF #3d #image #multi #similarity #visualisation
Protein structure similarity based on multi-view images generated from 3D molecular visualization (CHS, SJ, KF), pp. 3447–3451.
KDDKDD-2012-YuDRZY #classification #multi #predict
Transductive multi-label ensemble classification for protein function prediction (GXY, CD, HR, GZ, ZY), pp. 1077–1085.
KDIRKDIR-2012-BressoGDNS #3d #concept analysis #learning #relational
Formal Concept Analysis for the Interpretation of Relational Learning Applied on 3D Protein-binding Sites (EB, RG, MDD, AN, MST), pp. 111–120.
MLDMMLDM-2012-SinghCS #approach #hybrid #performance #recognition #using
A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines (LS, GC, DS), pp. 660–668.
SACSAC-2012-MenorPB #kernel #predict #probability #using
Probabilistic prediction of protein phosphorylation sites using kernel machines (MM, GP, KB), pp. 1393–1398.
ICMLICML-2011-PengHMU #set
Convex Max-Product over Compact Sets for Protein Folding (JP, TH, DAM, RU), pp. 729–736.
KDIRKDIR-2011-BicegoFBCM #grid #protocol
An Innovative Protocol for Comparing Protein Binding Sites via Atomic Grid Maps (MB, ADF, PB, AC, VM), pp. 413–422.
SACSAC-2011-ChamorroDA #predict
Evolutionary computation for the prediction of secondary protein structures (AEMC, FD, JSAR), pp. 1082–1087.
SACSAC-2011-TranCS #permutation #predict
Prediction of permuted super-secondary structures in β-barrel proteins (VDT, PC, JMS), pp. 110–111.
ICPRICPR-2010-SakarKSG #clustering #feature model #predict
Prediction of Protein Sub-nuclear Location by Clustering mRMR Ensemble Feature Selection (COS, OK, HS, FG), pp. 2572–2575.
KDIRKDIR-2010-CamposMO #random #recognition #using
Recognition of Gene/Protein Names using Conditional Random Fields (DC, SM, JLO), pp. 275–280.
KDIRKDIR-2010-Molokov #combinator #identification
Application of Combinatorial Methods to Protein Identification in Peptide Mass Fingerprinting (LM), pp. 307–313.
SACSAC-2010-AminBJ #framework #interactive #performance
A cytoscape based framework for efficient sub-graph isomorphic protein-protein interaction motif lookup (MSA, AB, HMJ), pp. 1572–1576.
HPDCHPDC-2010-HigdonLK #functional #modelling #sequence #similarity
Modeling sequence and function similarity between proteins for protein functional annotation (RH, BL, EK), pp. 499–502.
ICLPICLP-J-2010-PaluDFP #assembly
CLP-based protein fragment assembly (ADP, AD, FF, EP), pp. 709–724.
ICMLICML-2009-KamisettyL #approach #assessment #quality
A Bayesian approach to protein model quality assessment (HK, CJL), pp. 481–488.
KDIRKDIR-2009-PaquetV #2d #3d #multi #product line #realtime
Finding Protein Family Similarities in Real Time through Multiple 3D and 2D Representations, Indexing and Exhaustive Searching (EP, HLV), pp. 127–133.
SIGIRSIGIR-2009-YangGH #identification #information retrieval #problem
Protein identification as an information retrieval problem (YY, SG, AH), pp. 752–753.
SACSAC-2009-CorderoVB #clustering #framework
A new protein motif extraction framework based on constrained co-clustering (FC, AV, MB), pp. 776–781.
SACSAC-2009-RekapalliHZ #identification #named #scalability
HSP-HMMER: a tool for protein domain identification on a large scale (BR, CH, IBZ), pp. 766–770.
CASECASE-2008-GeorgievA
Two-stage robotic crystal mounting of protein crystals for X-ray data collection (AG, PKA), pp. 1019–1024.
CASECASE-2008-RussoMCK #automation #modelling #petri net
Petri net modeling and automated system control of protein crystallization experimentation in drug discovery (MFR, SM, MHC, HK), pp. 906–911.
VLDBVLDB-2008-PaquetV #database #named #perspective
Capri/MR: exploring protein databases from a structural and physicochemical point of view (EP, HLV), pp. 1504–1507.
ICMLICML-2008-AllauzenMT #kernel #predict #sequence
Sequence kernels for predicting protein essentiality (CA, MM, AT), pp. 9–16.
ICPRICPR-2008-KuksaHP #detection #kernel #performance
Fast protein homology and fold detection with sparse spatial sample kernels (PPK, PHH, VP), pp. 1–4.
SACSAC-2008-AntonyBHP #approach #biology #interactive #mining #using #validation
Computational approach to biological validation of protein-protein interactions discovered using literature mining (AA, SB, SH, MJP), pp. 1302–1306.
ICMLICML-2007-BhattacharyaBC #classification #kernel
Structural alignment based kernels for protein structure classification (SB, CB, NRC), pp. 73–80.
KDDKDD-2007-PandeySGGK #case study #interactive #network #predict
Association analysis-based transformations for protein interaction networks: a function prediction case study (GP, MS, RG, TG, VK), pp. 540–549.
MLDMMLDM-2007-Kertesz-FarkasKP #classification #equivalence #learning
Equivalence Learning in Protein Classification (AKF, AK, SP), pp. 824–837.
DLTDLT-2006-PaunP
P Systems with Proteins on Membranes and Membrane Division (AP, BP), pp. 292–303.
CIKMCIKM-2006-MarsoloPR #query #using
Structure-based querying of proteins using wavelets (KM, SP, KR), pp. 24–33.
ICMLICML-2006-EngelhardtJB #predict #visual notation
A graphical model for predicting protein molecular function (BEE, MIJ, SEB), pp. 297–304.
ICPRICPR-v1-2006-WenGL #clustering #detection #markov #monte carlo
Markov Chain Monte Carlo Data Association for Merge and Split Detection in Tracking Protein Clusters (QW, JG, KLP), pp. 1030–1033.
ICPRICPR-v3-2006-BaldacciGLR #approach #clustering
A Template-Matching Approach for Protein Surface Clustering (LB, MG, AL, SR), pp. 340–343.
ICPRICPR-v3-2006-BouchaffraT #markov #recognition #using
Protein Fold Recognition using a Structural Hidden Markov Model (DB, JT), pp. 186–189.
KDDKDD-2006-ChenHLN #interactive #named #network
NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs (JC, WH, MLL, SKN), pp. 106–115.
SACSAC-2006-ChenPSW
An integrated computational proteomics method to extract protein targets for Fanconi Anemia studies (JYC, SLP, CS, MW), pp. 173–179.
SACSAC-2006-ChongL #using
An extension of dead end elimination for protein side-chain conformation using merge-decoupling (KFC, HWL), pp. 195–199.
SACSAC-2006-CraigL #classification #learning #using
Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.
SACSAC-2006-MerschmannP #approach #classification
A Bayesian approach for protein classification (LHdCM, AP), pp. 200–201.
SACSAC-2006-PaluPHL #3d #approach #constraints #logic programming #scalability
A constraint logic programming approach to 3D structure determination of large protein complexes (ADP, EP, JH, YL), pp. 131–136.
SACSAC-2006-WuH #interactive #mining #network
Mining and analyzing the topological structure of protein-protein interaction networks (DDW, XH), pp. 185–189.
SIGMODSIGMOD-2005-MurphyF #database #image #research
Research issues in protein location image databases (RFM, CF), pp. 966–967.
CIAACIAA-J-2004-HanMZ05 #approach #automaton #database #sequence
An automata approach to match gapped sequence tags against protein database (YH, BM, KZ), pp. 487–497.
ICMLICML-2005-IeWNL #adaptation #multi #recognition #using
Multi-class protein fold recognition using adaptive codes (EI, JW, WSN, CSL), pp. 329–336.
ICMLICML-2005-LiuXC #graph #predict #using
Predicting protein folds with structural repeats using a chain graph model (YL, EPX, JGC), pp. 513–520.
ICMLICML-2005-WangS #classification #kernel
New kernels for protein structural motif discovery and function classification (CW, SDS), pp. 940–947.
MLDMMLDM-2005-KurganH #approach #feature model #predict #sequence
Prediction of Secondary Protein Structure Content from Primary Sequence Alone — A Feature Selection Based Approach (LAK, LH), pp. 334–345.
PPDPPPDP-2005-PaluDP #heuristic #optimisation #parallel #predict
Heuristics, optimizations, and parallelism for protein structure prediction in CLP(FD) (ADP, AD, EP), pp. 230–241.
SACSAC-2005-DalkilicS #analysis #design #evaluation
Design and evaluation of CATPA: curation and alignment tool for protein analysis (MMD, AS), pp. 190–194.
SACSAC-2005-DavidssonHS #predict #sequence
Comparing approaches to predict transmembrane domains in protein sequences (PD, JH, KS), pp. 185–189.
SACSAC-2005-HuP #3d #complexity #parametricity
Volume fractal dimensionality: a useful parameter for measuring the complexity of 3D protein spatial structures (MH, QP), pp. 172–176.
SACSAC-2005-PlakeHL #interactive #optimisation #syntax
Optimizing syntax patterns for discovering protein-protein interactions (CP, JH, UL), pp. 195–201.
HPDCHPDC-2005-GrothMFWZM #empirical #using
Recording and using provenance in a protein compressibility experiment (PTG, SM, WF, SCW, KPZ, LM), pp. 201–208.
CIAACIAA-2004-HanMZ #approach #automaton #database #sequence
An Automata Approach to Match Gapped Sequence Tags Against Protein Database (YH, BM, KZ), pp. 167–177.
ICALPICALP-2004-FuW #algorithm
A 2O(n1-(1/d)log n) Time Algorithm for d-Dimensional Protein Folding in the HP-Model (BF, WW), pp. 630–644.
CIKMCIKM-2004-AghiliAA #geometry #using
Protein structure alignment using geometrical features (SAA, DA, AEA), pp. 148–149.
ICMLICML-2004-ChuGW #predict #visual notation
A graphical model for protein secondary structure prediction (WC, ZG, DLW).
ICPRICPR-v2-2004-ColleP #predict #process
Relaxation Labeling Processes for Protein Secondary Structure Prediction (GC, MP), pp. 355–358.
ICPRICPR-v2-2004-VijayaMS #classification #clustering #performance #sequence
An Efficient Technique for Protein Sequence Clustering and Classification (PAV, MNM, DKS), pp. 447–450.
SIGIRSIGIR-2004-LiuCKG #predict
Context sensitive vocabulary and its application in protein secondary structure prediction (YL, JGC, JKS, VG), pp. 538–539.
SACSAC-2004-JiangWM #network #thread
Protein threading with residue-environment matching by artificial neural networks (NJ, WXW, IM), pp. 209–210.
SACSAC-2004-WeisserK #identification #metric #sequence #statistics #using
Identification of fundamental building blocks in protein sequences using statistical association measures (DKW, JKS), pp. 154–161.
ICLPICLP-2004-PaluDF #simulation
Protein Folding Simulation in CCP (ADP, AD, FF), pp. 452–453.
ICEISICEIS-v2-2003-VogiatzisFP #information management #predict
The Protein Structure Prediction Module of the Prot-Grid Information System (DV, DF, GAP), pp. 372–378.
KDDKDD-2003-SheCWEGB #predict
Frequent-subsequence-based prediction of outer membrane proteins (RS, FC, KW, ME, JLG, FSLB), pp. 436–445.
SACSAC-2003-CanWWS #3d #java #named #performance #using #visualisation
FPV: Fast Protein Visualization Using Java 3D (TC, YW, YFW, JS), pp. 88–95.
SACSAC-2003-NgZT #approach #interactive
Integrative Approach for Computationally Inferring Protein Domain Interactions (SKN, ZZ, SHT), pp. 115–121.
SACSAC-2003-VulloF #approach #predict #recursion
A Recursive Connectionist Approach for Predicting Disulfide Connectivity in Proteins (AV, PF), pp. 67–71.
ICLPICLP-2003-BackofenW #approach #constraints #modelling #predict
A Constraint-Based Approach to Structure Prediction for Simplified Protein Models That Outperforms Other Existing Methods (RB, SW), pp. 49–71.
VLDBVLDB-2002-HammelP #sequence
Searching on the Secondary Structure of Protein Sequences (LH, JMP), pp. 634–645.
ICPRICPR-v2-2002-TaoIS
Extracting Fractal Features for Analyzing Protein Structure (YT, TRI, JCS), pp. 482–485.
SACSAC-2002-DinakarpandianK #predict #proximity
BlOMIND-protein property prediction by property proximity profiles (DD, VK), pp. 168–172.
MLDMMLDM-2001-MottlDSKM #classification #pattern matching #pattern recognition #recognition
Featureless Pattern Recognition in an Imaginary Hilbert Space and Its Application to Protein Fold Classification (VM, SD, OS, CAK, IBM), pp. 322–336.
SACSAC-2001-DeermanLP #algorithm #predict #problem #search-based
Linkage-learning genetic algorithm application to the protein structure prediction problem (KRD, GBL, RP), pp. 333–339.
SACSAC-2001-Grigoriev #identification
Computational structural genomics: identifying protein targets for structural studies (IVG), pp. 41–45.
HPDCHPDC-2001-NatrajanFHGWCB #case study #experience #grid #using
Studying Protein Folding on the Grid: Experiences Using CHARMM on NPACI Resources under Legion (AN, ADF, MAH, ASG, NWD, MC, CLBI), pp. 14–21.
KDDKDD-2000-KingKCD #data mining #functional #mining #predict #sequence #using
Genome scale prediction of protein functional class from sequence using data mining (RDK, AK, AC, LD), pp. 384–389.
KDDKDD-2000-WangMSW #biology #case study #classification #data mining #mining #network #sequence
Application of neural networks to biological data mining: a case study in protein sequence classification (JTLW, QM, DS, CHW), pp. 305–309.
ICALPICALP-1999-Clote #markov
Protein Folding, the Levinthal Paradox and Rapidly Mixing Markov Chains (PC), pp. 240–249.
SIGMODSIGMOD-1998-ChenWZ #query
A Protein Patent Query System Powered By Kleisli (JC, LW, LZ), pp. 593–595.
STOCSTOC-1998-CrescenziGPPY #complexity #on the
On the Complexity of Protein Folding (Extended Abstract) (PC, DG, CHP, AP, MY), pp. 597–603.
SACSAC-1996-RamamurthyYG #generative #on the #parallel #scalability
On the scalability of parallel triplet generation for protein crystallography (SR, BWY, CG), pp. 344–352.
STOCSTOC-1995-HartI #performance
Fast protein folding in the hydrophobic-hydrophilic model within three-eights of optimal (Extended Abstract) (WEH, SI), pp. 157–168.
ICMLICML-1995-CrawH #network #refinement
Protein Folding: Symbolic Refinement Competes with Neural Networks (SC, PH), pp. 133–141.
KDDKDD-1995-TsumotoT95a #automation #component #functional #representation #sequence #set
Automated Discovery of Functional Components of Proteins from Amino-Acid Sequences Based on Rough Sets and Change of Representation (ST, HT), pp. 318–324.
ICMLICML-1994-AbeM #predict #probability
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars (NA, HM), pp. 3–11.
CADECADE-1994-BaumgartnerF94a #interface #named #proving
PROTEIN: A PROver with a Theory Extension INterface (PB, UF), pp. 769–773.
SEKESEKE-1992-SubramaniamTHRR #design #information management #prototype #type system
Knowledge Engineering for Protein Structure and Motifs: Design of a Prototype System (SS, DKT, KH, HR, LAR), pp. 420–435.
ICLPICLP-1986-RawlingsTNFS86 #prolog #using
Using Prolog to Represent and Reason about Protein Structure (CJR, WRT, JN, JF, MJES), pp. 536–543.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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