21.4.6 Extraction and Analysis of Proteins

Chapter Contents (Back)
Protein.
See also Electron Microscope Images and Sensors.
See also Cryo-Electron Microscopy.

Thompson, R.[Richard],
A measure of shared information in classes of patterns,
PR(12), No. 6, 1980, pp. 369-379.
Elsevier DOI 0309
Insight into the patterns of variation in the amino acid sequences of proteins. BibRef

Guigo, R., Smith, T.F.,
Inferring correlation between database queries: analysis of protein sequence patterns,
PAMI(15), No. 10, October 1993, pp. 1030-1041.
IEEE DOI 0401
BibRef

Leherte, L., Glasgow, J., Baxter, K., Steeg, E., and Fortier, S.,
Analysis of Three-Dimensional Protein Images,
JAIR(7), 1997, pp. 125-159.
HTML Version. BibRef 9700

Zaki, M.J., Jin, S.[Shan], Bystroff, C.,
Mining residue contacts in proteins using local structure predictions,
SMC-B(33), No. 5, October 2003, pp. 789-801.
IEEE Abstract. 0310
BibRef

Yang, Z.R.[Zheng Rong],
Orthogonal kernel Machine for the prediction of functional sites in proteins,
SMC-B(35), No. 1, February 2005, pp. 100-106.
IEEE Abstract. 0501
BibRef

Zhang, G.Z.[Guang-Zheng], Huang, D.S., Quan, Z.H.,
Combining a binary input encoding scheme with RBFNN for globulin protein inter-residue contact map prediction,
PRL(26), No. 10, 15 July 2005, pp. 1543-1553.
Elsevier DOI 0506
BibRef

Zhang, G.Z.[Guang-Zheng], Huang, D.S., Zhu, Y.P., Li, Y.X.,
Improving protein secondary structure prediction by using the residue conformational classes,
PRL(26), No. 15, November 2005, pp. 2346-2352.
Elsevier DOI 0510
BibRef

Zhao, T., Velliste, M., Boland, M.V., Murphy, R.F.,
Object Type Recognition for Automated Analysis of Protein Subcellular Location,
IP(14), No. 9, September 2005, pp. 1351-1359.
IEEE DOI 0508
BibRef

Zacharakis, G., Ripoll, J., Weissleder, R., Ntziachristos, V.,
Fluorescent protein tomography scanner for small animal imaging,
MedImg(24), No. 7, July 2005, pp. 878-885.
IEEE DOI 0508
BibRef

Lozano, M.A., Escolano, F.,
Protein classification by matching and clustering surface graphs,
PR(39), No. 4, April 2006, pp. 539-551.
Elsevier DOI Protein classification; Graph matching; Energy minimization; Graph clustering; EM algorithms 0604
BibRef

Kim, J.K.[Jong Kyoung], Raghava, G.P.S., Bang, S.Y.[Sung-Yang], Choi, S.J.[Seung-Jin],
Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine,
PRL(27), No. 9, July 2006, pp. 996-1001.
Elsevier DOI 0605
BibRef

Kim, J.K.[Jong Kyoung], Bang, S.Y.[Sung-Yang], Choi, S.J.[Seung-Jin],
Sequence-driven features for prediction of subcellular localization of proteins,
PR(39), No. 12, December 2006, pp. 2301-2311.
Elsevier DOI 0609
Protein sequence feature extraction; Subcellular localization prediction; Support vector machine BibRef

Vijaya, P.A., Murty, M.N.[M. Narasimha], Subramanian, D.K.,
Efficient bottom-up hybrid hierarchical clustering techniques for protein sequence classification,
PR(39), No. 12, December 2006, pp. 2344-2355.
Elsevier DOI 0609
BibRef
Earlier:
An efficient technique for protein sequence clustering and classification,
ICPR04(II: 447-450).
IEEE DOI 0409
Hybrid clustering; Hierarchical structure; Protein sequences; Median strings/sequences; Prototypes; Feature selection; Classification accuracy BibRef

Khoja, R.[Rahim], Marolia, M.[Mehul], Acharya, T.[Tinku], Chakrabarti, C.[Chaitali],
A coprocessor architecture for fast protein structure prediction,
PR(39), No. 12, December 2006, pp. 2494-2505.
Elsevier DOI 0609
Protein structure prediction; PSIPRED; PSI BLAST; Neural network; VLSI architecture BibRef

Plötz, T.[Thomas], Fink, G.A.[Gernot A.],
Pattern Recognition methods for advanced stochastic protein sequence analysis using HMMs,
PR(39), No. 12, December 2006, pp. 2267-2280.
Elsevier DOI 0609
BibRef
Earlier:
Feature Extraction for Improved Profile HMM Based Biological Sequence Analysis,
ICPR04(II: 315-318).
IEEE DOI 0409
DNA sequencing. Protein sequence analysis; Probabilistic protein family modeling; HMM BibRef

Bergkvist, A.[Anders], Damaschke, P.[Peter],
Fast algorithms for finding disjoint subsequences with extremal densities,
PR(39), No. 12, December 2006, pp. 2281-2292.
Elsevier DOI 0609
Holes in data; Range prediction; Protein torsion angle; Protein structure prediction; Dynamic programming; Selection algorithms; Time complexity BibRef

Huang, D.S.[De-Shuang], Zhao, X.M.[Xing-Ming], Huang, G.B.[Guang-Bin], Cheung, Y.M.[Yiu-Ming],
Classifying protein sequences using hydropathy blocks,
PR(39), No. 12, December 2006, pp. 2293-2300.
Elsevier DOI 0609
Hydropathy blocks; Protein sequence classification; Support vector machine BibRef

Kurgan, L.A.[Lukasz A.], Homaeian, L.[Leila],
Prediction of structural classes for protein sequences and domains--Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy,
PR(39), No. 12, December 2006, pp. 2323-2343.
Elsevier DOI 0609
Protein structural class; SCOP; Machine learning; Homology; Prediction; Secondary protein structure BibRef

Chen, C.Y.[Chien-Yu], Chung, W.C.[Wen-Chin], Su, C.T.[Chung-Tsai],
Exploiting homogeneity in protein sequence clusters for construction of protein family hierarchies,
PR(39), No. 12, December 2006, pp. 2356-2369.
Elsevier DOI 0609
Protein sequence clustering; Family analysis; Hierarchical algorithm BibRef

Baldacci, L., Golfarelli, M., Lumini, A., Rizzi, S.,
Clustering techniques for protein surfaces,
PR(39), No. 12, December 2006, pp. 2370-2382.
Elsevier DOI 0609
BibRef
Earlier:
A Template-Matching Approach for Protein Surface Clustering,
ICPR06(III: 340-343).
IEEE DOI 0609
Clustering; Region growing; Template matching; Protein surface BibRef

Mundra, P.[Piyushkumar], Kumar, M.[Madhan], Kumar, K.K.[K. Krishna], Jayaraman, V.K.[Valadi K.], Kulkarni, B.D.[Bhaskar D.],
Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM,
PRL(28), No. 13, 1 October 2007, pp. 1610-1615.
Elsevier DOI 0709
Nuclear protein; Subnuclear localization; Multiclass SVM; Factor solution score; PSSM BibRef

Karnik, S.[Shreyas], Mitra, J.[Joydeep], Singh, A.[Arunima], Kulkarni, B.D., Sundarajan, V., Jayaraman, V.K.,
Identification of N-Glycosylation Sites with Sequence and Structural Features Employing Random Forests,
PReMI09(146-151).
Springer DOI 0912
BibRef

Karnik, S.[Shreyas], Prasad, A.[Ajay], Diwevedi, A.[Alok], Sundararajan, V., Jayaraman, V.K.,
Identification of Defensins Employing Recurrence Quantification Analysis and Random Forest Classifiers,
PReMI09(152-157).
Springer DOI 0912
BibRef

McLaren, G.[Gina], Ellis, R.[Robert], Douglass, J.W.[James W.], Riding, T.J.[Thomas J.], Ring, J.E.[James E.],
Automated detection of objects in a biological sample,
US_Patent7,177,454, Feb 13, 2007
WWW Link. Proteins. BibRef 0702

Li, Y.L.[Yun-Lei], de Ridder, D.[Dick], Duin, R.P.W.[Robert P.W.], Reinders, M.J.T.[Marcel J.T.],
Integration of prior knowledge of measurement noise in kernel density classification,
PR(41), No. 1, January 2008, pp. 320-330.
Elsevier DOI 0710
Prior knowledge; Measurement noise; Kernel method; Parzen; Protein complex; mRNA co-expression coefficient BibRef

Tsai, C.Y.[Chieh-Yuan], Chiu, C.C.[Chuang-Cheng],
An efficient conserved region detection method for multiple protein sequences using principal component analysis and wavelet transform,
PRL(29), No. 5, 1 April 2008, pp. 616-628.
Elsevier DOI 0802
Protein sequence analysis; Conserved region detection; Principal component analysis; Wavelet transform BibRef

Savvides, A.[Alexios], Promponas, V.J.[Vasilis J.], Fokianos, K.[Konstantinos],
Clustering of biological time series by cepstral coefficients based distances,
PR(41), No. 7, July 2008, pp. 2398-2412.
Elsevier DOI 0804
Exponential model; Likelihood; Distance measures; Spectral analysis; Periodogram; Data mining; Protein sequence analysis BibRef

Ding, Y.S.[Yong-Sheng], Zhang, T.L.[Tong-Liang],
Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: An approach with immune genetic algorithm-based ensemble classifier,
PRL(29), No. 13, 1 October 2008, pp. 1887-1892.
Elsevier DOI 0804
Apoptosis protein subcellular location; Pseudo amino acid composition; Approximate entropy; Ensemble classifier; Fuzzy K-nearest neighbor classifier BibRef

Buske, F.A.[Fabian A.], Maetschke, S.[Stefan], Boden, M.[Mikael],
It's about time: Signal recognition in staged models of protein translocation,
PR(42), No. 4, April 2009, pp. 567-574.
Elsevier DOI 0812
Bioinformatics; Machine learning; Protein secretory pathway; Signal peptide; Conditional random field; Amino acid sequence BibRef

von Wegner, F., Schurmann, S., Fink, R.H.A., Vogel, M., Friedrich, O.,
Motor Protein Function in Skeletal Muscle: A Multiple Scale Approach to Contractility,
MedImg(28), No. 10, October 2009, pp. 1632-1642.
IEEE DOI 0910
BibRef

Barash, E., Dinn, S., Sevinsky, C., Ginty, F.,
Multiplexed Analysis of Proteins in Tissue Using Multispectral Fluorescence Imaging,
MedImg(29), No. 8, August 2010, pp. 1457-1462.
IEEE DOI 1008
BibRef

Sabarinathan, R., Banerjee, N.[Nirjhar], Balakrishnan, N., Sekar, K.,
An algorithm to find distant repeats in a pair of protein sequences,
PRL(31), No. 14, 15 October 2010, pp. 2161-2169.
Elsevier DOI 1003
Distant repeats; Protein sequences; PAM matrix BibRef

Diniz, M.C.[Michely C.], Pacheco, A.C.L.[Ana Carolina L.], Girao, K.T.[Karen T.], Araujo, F.F.[Fabiana F.], Walter, C.A.[Cezar A.], Oliveira, D.M.[Diana M.],
The tetratricopeptide repeats (TPR)-like superfamily of proteins in Leishmania spp., as revealed by multi-relational data mining,
PRL(31), No. 14, 15 October 2010, pp. 2178-2189.
Elsevier DOI 1003
Multi-relational data mining; Hidden Markov models; Viterbi algorithm; Tetratricopeptide repeat motif; Leishmania proteins BibRef

Wang, H.Y.[Hai-Ying], Zheng, H.[Huiru], Browne, F.[Fiona], Glass, D.H.[David H.], Azuaje, F.[Francisco],
Integration of Gene Ontology-based similarities for supporting analysis of protein-protein interaction networks,
PRL(31), No. 14, 15 October 2010, pp. 2073-2082.
Elsevier DOI 1003
Gene ontology; Protein interaction networks; Bayesian networks; Classification BibRef

Jancura, P.[Pavol], Marchiori, E.[Elena],
Dividing protein interaction networks for modular network comparative analysis,
PRL(31), No. 14, 15 October 2010, pp. 2083-2096.
Elsevier DOI 1003
Protein interaction network division; Modular network alignment; Conserved protein complexes BibRef

Anand, A.[Ashish], Pugalenthi, G.[Ganesan], Fogel, G.B.[Gary B.], Suganthan, P.N.,
Identification and analysis of transcription factor family-specific features derived from DNA and protein information,
PRL(31), No. 14, 15 October 2010, pp. 2097-2102.
Elsevier DOI 1003
Transcription factor; TF family-specific features; TF-TFBS interaction; Multi-class classification; TFBS; Feature selection BibRef

Horst, J.A.[Jeremy A.], Samudrala, R.[Ram],
A protein sequence meta-functional signature for calcium binding residue prediction,
PRL(31), No. 14, 15 October 2010, pp. 2103-2112.
Elsevier DOI 1003
Protein sequence analysis; Protein function prediction; Calcium; Protein binding site; Functional signature BibRef

Jain, B.J.[Brijnesh J.], Obermayer, K.[Klaus],
Graph quantization,
CVIU(115), No. 7, July 2011, pp. 946-961.
Elsevier DOI 1106
BibRef
And:
Generalized Learning Graph Quantization,
GbRPR11(122-131).
Springer DOI 1105
BibRef
And:
Maximum Likelihood for Gaussians on Graphs,
GbRPR11(62-71).
Springer DOI 1105
BibRef
Earlier:
Consistent Estimator of Median and Mean Graph,
ICPR10(1032-1035).
IEEE DOI 1008
BibRef
Earlier:
Algorithms for the Sample Mean of Graphs,
CAIP09(351-359).
Springer DOI 0909
For protein analysis. Structure mining. Quantization of graphs; Graph matching; Orbifolds; Consistent estimators; Clustering; k-means; Competitive learning BibRef

Jain, B.[Brijnesh],
Margin Perceptrons for Graphs,
ICPR14(3851-3856)
IEEE DOI 1412
BibRef
And:
Flip-Flop Sublinear Models for Graphs,
SSSPR14(93-102).
Springer DOI 1408
BibRef
And:
Mixtures of Radial Densities for Clustering Graphs,
CAIP13(110-119).
Springer DOI 1308
BibRef

Pietrosemoli, N., Lopez, D., Segura-Cabrera, A., Pazos, F.,
Computational Prediction of Important Regions in Protein Sequences,
SPMag(29), No. 3, 2012, pp. 143-147.
IEEE DOI 1210
Life Sciences. BibRef

Lei, Y.K.[Ying-Ke], You, Z.H.[Zhu-Hong], Dong, T.[Tianbao], Jiang, Y.X.[Yun-Xiao], Yang, J.A.[Jun-An],
Increasing reliability of protein interactome by fast manifold embedding,
PRL(34), No. 4, 1 March 2013, pp. 372-379.
Elsevier DOI 1302
Protein-protein interaction (PPI); Manifold embedding; Fast isometric feature mapping (Fast ISOMAP); False positive (FP) BibRef

Cantoni, V., Ferone, A., Ozbudak, O., Petrosino, A.,
Protein motifs retrieval by SS terns occurrences,
PRL(34), No. 5, 1 April 2013, pp. 559-563.
Elsevier DOI 1303
Protein motif retrieval; Secondary Structures; Similarity search; Generalized Hough Transform; Pattern recognition; Structural biology BibRef

Dou, H.[Hang], Baker, M.L.[Matthew L.], Ju, T.[Tao],
Graph-based deformable matching of 3D line with application in protein fitting,
VC(31), No. 6-8, June 2015, pp. 967-977.
WWW Link. 1506
BibRef
And: Erratum: VC(31), No. 11, November 2015, pp. 1567.
WWW Link. 1512
BibRef

Mallek, S.[Sabrine], Boukhris, I.[Imen], Elouedi, Z.[Zied],
Community detection for graph-based similarity: Application to protein binding pockets classification,
PRL(62), No. 1, 2015, pp. 49-54.
Elsevier DOI 1507
Graph-based similarity BibRef

Suryanto, C.H.[Chendra Hadi], Fukui, K.[Kazuhiro], Hino, H.[Hideitsu],
Protein Fold Classification Using Large Margin Combination of Distance Metrics,
IEICE(E99-D), No. 3, March 2016, pp. 714-723.
WWW Link. 1604
BibRef
Earlier: A1, A3, A2:
Combination of Multiple Distance Measures for Protein Fold Classification,
ACPR13(440-445)
IEEE DOI 1408
biology computing BibRef

Punjani, A.[Ali], Brubaker, M.A.[Marcus A.], Fleet, D.J.[David J.],
Building Proteins in a Day: Efficient 3D Molecular Structure Estimation with Electron Cryomicroscopy,
PAMI(39), No. 4, April 2017, pp. 706-718.
IEEE DOI 1703
BibRef
Earlier: A2, A1, A3:
Building proteins in a day: Efficient 3D molecular reconstruction,
CVPR15(3099-3108)
IEEE DOI 1510
Computational modeling BibRef

Sun, D.[Dengdi], Liang, H.D.[Hua-Dong], Ge, M.L.[Mei-Ling], Ding, Z.L.[Zhuan-Lian], Cai, W.T.[Wan-Ting], Luo, B.[Bin],
Protein functional annotation refinement based on graph regularized l1-norm PCA,
PRL(87), No. 1, 2017, pp. 212-221.
Elsevier DOI 1703
Graph regularization BibRef

Barnett, A.[Alex], Greengard, L.[Leslie], Pataki, A.[Andras], Spivak, M.[Marina],
Rapid Solution of the Cryo-EM Reconstruction Problem by Frequency Marching,
SIIMS(10), No. 3, 2017, pp. 1170-1195.
DOI Link 1710
Determining the three-dimensional (3D) structure of proteins. BibRef

Europe's new X-ray laser delivers results: Scientists used the EuXFEL to reveal the structures of the tiniest proteins,
Spectrum(55), No. 11, November 2018, pp. 10-11.
IEEE DOI 1811
[News] BibRef

Sit, A.[Atilla], Shin, W.H.[Woong-Hee], Kihara, D.[Daisuke],
Three-dimensional Krawtchouk descriptors for protein local surface shape comparison,
PR(93), 2019, pp. 534-545.
Elsevier DOI 1906
3D image retrieval, Local image comparison, Region of interest, Discrete orthogonal functions, Krawtchouk polynomials, Structure-based function prediction BibRef

Mensi, A.[Antonella], Bicego, M.[Manuele], Lovato, P.[Pietro], Loog, M.[Marco], Tax, D.M.J.[David M.J.],
A dissimilarity-based multiple instance learning approach for protein remote homology detection,
PRL(128), 2019, pp. 231-236.
Elsevier DOI 1912
BibRef
Earlier:
Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning,
SSSPR18(119-129).
Springer DOI 1810
Protein Remote Homology Detection, Multiple-instance learning, Dissimilarity representation BibRef

Vascon, S.[Sebastiano], Frasca, M.[Marco], Tripodi, R.[Rocco], Valentini, G.[Giorgio], Pelillo, M.[Marcello],
Protein function prediction as a graph-transduction game,
PRL(134), 2020, pp. 96-105.
Elsevier DOI 2005
BibRef

Pan, X.Y.[Xiao-Yong], Shen, H.B.[Hong-Bin],
Scoring disease-microRNA associations by integrating disease hierarchy into graph convolutional networks,
PR(105), 2020, pp. 107385.
Elsevier DOI 2006
microRNAs, Protein coding genes, Interaction network, Graph convolutional network, Disease hierarchy BibRef

Cheng, J.Y.[Jin-Yong], Liu, Y.H.[Yi-Hui], Ma, Y.M.[Yu-Ming],
Protein secondary structure prediction based on integration of CNN and LSTM model,
JVCIR(71), 2020, pp. 102844.
Elsevier DOI 2009
Protein secondary structure prediction, Convolution neural networks, Long short-term memory, Softmax, Random forest BibRef

Ye, X.B.[Xian-Bin], Guan, Q.L.[Quan-Long], Luo, W.Q.[Wei-Qi], Fang, L.D.[Liang-Da], Lai, Z.R.[Zhao-Rong], Wang, J.[Jun],
Molecular substructure graph attention network for molecular property identification in drug discovery,
PR(128), 2022, pp. 108659.
Elsevier DOI 2205
Molecular substructure, Graph attention, Molecular property identification BibRef

Santander-Jiménez, S.[Sergio], Vega-Rodríguez, M.A.[Miguel A.], Sousa, L.[Leonel],
Inter-Algorithm Multiobjective Cooperation for Phylogenetic Reconstruction on Amino Acid Data,
Cyber(52), No. 5, May 2022, pp. 3577-3591.
IEEE DOI 2206
Optimization, Phylogeny, Amino acids, Evolutionary computation, Proteins, Bioinformatics, multiobjective optimization BibRef

Elnaggar, A.[Ahmed], Heinzinger, M.[Michael], Dallago, C.[Christian], Rehawi, G.[Ghalia], Wang, Y.[Yu], Jones, L.[Llion], Gibbs, T.[Tom], Feher, T.[Tamas], Angerer, C.[Christoph], Steinegger, M.[Martin], Bhowmik, D.[Debsindhu], Rost, B.[Burkhard],
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning,
PAMI(44), No. 10, October 2022, pp. 7112-7127.
IEEE DOI 2209
Proteins, Training, Amino acids, Task analysis, Databases, Computational modeling, Computational biology, deep learning BibRef

Paquet, E.[Eric], Viktor, H.L.[Herna L.], Madi, K.[Kamel], Wu, J.Z.[Jun-Zheng],
Deformable Protein Shape Classification Based on Deep Learning, and the Fractional Fokker-Planck and Kähler-Dirac Equations,
PAMI(45), No. 1, January 2023, pp. 391-407.
IEEE DOI 2212
Shape, Proteins, Neural networks, Kernel, Deep learning, Manifolds, Classification, fractional, Fokker-Planck, Dirac-Kähler, pyramidal neural network BibRef

Wang, Y.F.[Yi-Fei], Wang, X.[Xue], Chen, C.[Cheng], Gao, H.L.[Hong-Li], Salhi, A.[Adil], Gao, X.[Xin], Yu, B.[Bin],
RPI-CapsuleGAN: Predicting RNA-protein interactions through an interpretable generative adversarial capsule network,
PR(141), 2023, pp. 109626.
Elsevier DOI 2306
RNA-protein interactions, elastic net, multi-information fusion, convolutional block attention module BibRef

Wang, W.[Wei], Zhang, G.W.[Gao-Wei], Han, H.Y.[Hong-Yong], Zhang, C.[Chi],
Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation,
IP(32), 2023, pp. 3980-3993.
IEEE DOI 2307
Noise measurement, Knowledge transfer, Task analysis, Pollution measurement, Data mining, Proteins, correntropy BibRef

Abniki, A.[Ahmad], Beigy, H.[Hamid],
Learning Hidden Graphs From Samples,
PAMI(45), No. 10, October 2023, pp. 11993-12003.
IEEE DOI 2310
Molecular structure. BibRef

Geva, A.S.[Adi Shasha], Shkolnisky, Y.[Yoel],
A Common Lines Approach for Ab Initio Modeling of Molecules with Tetrahedral and Octahedral Symmetry,
SIIMS(16), No. 4, 2023, pp. 1978-2014.
DOI Link 2312
BibRef

Kim, M.[Myeongseop], Kim, S.J.[Sung-Jun], Lee, D.[Dabin], Jang, H.K.[Hyo-Keun], Park, S.H.[Sang-Hoon], Kim, Y.[Yejin], Kim, J.[Jaesoon], Youn, S.H.[Seok-Hyun], Joo, H.[Huitae], Son, S.H.[Seung-Hyun], Lee, S.H.[Sang-Heon],
Spatiotemporal Protein Variations Based on VIIRS-Derived Regional Protein Algorithm in the Northern East China Sea,
RS(16), No. 5, 2024, pp. 829.
DOI Link 2403
BibRef

Yao, Q.M.[Quan-Ming], Shen, Z.Q.[Zhen-Qian], Wang, Y.Q.[Ya-Qing], Dou, D.[Dejing],
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction,
PAMI(46), No. 8, August 2024, pp. 5413-5429.
IEEE DOI 2407
Task analysis, Training, Predictive models, Machine learning, Adaptation models, Drugs, Graph neural networks, Few-Shot learning, transfer learning BibRef

Yang, Z.[Ziduo], Zhong, W.[Weihe], Lv, Q.J.[Qiu-Jie], Dong, T.J.[Tie-Jun], Chen, G.X.[Guan-Xing], Chen, C.Y.C.[Calvin Yu-Chian],
Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures,
PAMI(46), No. 12, December 2024, pp. 8191-8208.
IEEE DOI 2411
Proteins, Programmable logic arrays, Predictive models, Graph neural networks, Data models, Convolution, structure-based virtual screening BibRef

Martinez-Sanchez, A.[Antonio], Lamm, L.[Lorenz], Jasnin, M.[Marion], Phelippeau, H.[Harold],
Simulating the Cellular Context in Synthetic Datasets for Cryo-Electron Tomography,
MedImg(43), No. 11, November 2024, pp. 3742-3754.
IEEE DOI 2411
Organizations, Synthetic data, Proteins, Biomembranes, Geometry, Tomography, Cryo-electron tomography, deep learning, synthetic data generation BibRef

Kamiya, S.[Satoshi], Toida, K.[Keisuke], Tsunoyama, T.A.[Taka-Aki], Hotta, K.[Kazuhiro],
Tracking Correction Method for Rapid and Random Protein Molecules Movement,
ACCV24(II: 449-465).
Springer DOI 2412
BibRef

Carbone, A.[Alessandra], Decelle, A.[Aurélien], Rosset, L.[Lorenzo], Seoane, B.[Beatriz],
Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics,
PAMI(47), No. 2, February 2025, pp. 1309-1316.
IEEE DOI 2501
Training, Computational modeling, Data models, Accuracy, RNA, Biological system modeling, Synthetic data, Generators, Taxonomy, sequence modeling BibRef

Gong, X.[Xu], Liu, M.[Maotao], Liu, Q.[Qun], Guo, Y.[Yike], Wang, G.[Guoyin],
MDFCL: Multimodal data fusion-based graph contrastive learning framework for molecular property prediction,
PR(163), 2025, pp. 111463.
Elsevier DOI 2503
Molecular property prediction, Graph representation, Multimodal data fusion, Graph contrastive learning BibRef

Quan, R.J.[Rui-Jie], Wang, W.G.[Wen-Guan], Ma, F.[Fan], Fan, H.[Hehe], Yang, Y.[Yi],
Clustering for Protein Representation Learning,
CVPR24(319-329)
IEEE DOI 2410
Proteins, Representation learning, Enzymes, Ontologies, Amino acids, Protein Representation Learning, Clustering BibRef

Peng, Z.Y.[Zeng-Yan], Chen, S.L.[Shiang-Liang], Hsu, M.H.[Miao-Hsin], Chang, D.M.[Dong-Meau], Chen, C.C.[Chun-Chi],
MLP Kernel-Based to Predict the Optimal Conditions of Transglutaminase on Protein Polymerization,
IoTDesign24(666-670)
IEEE DOI 2404
Proteins, Dairy products, Estimation, Artificial neural networks, Numerical simulation, Electrophoresis, Fats BibRef

Chen, W.J.[Wei-Jie], Wang, X.[Xinyan], Wang, Y.H.[Yu-Hang],
FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures,
CVPR23(19776-19785)
IEEE DOI 2309
BibRef

Ding, L.F.[Long-Fei], Zhao, M.B.[Meng-Biao], Yin, F.[Fei], Zeng, S.L.[Shui-Ling], Liu, C.L.[Cheng-Lin],
A Large-Scale Database for Chemical Structure Recognition and Preliminary Evaluation,
ICPR22(1464-1470)
IEEE DOI 2212
Image recognition, Databases, Annotations, Biological system modeling, Benchmark testing, Image-to-Markup BibRef

Liu, Z.[Ziyi], Wang, Z.[Zengmao], Du, B.[Bo],
Multi-marginal Contrastive Learning for Multilabel Subcellular Protein Localization,
CVPR22(20594-20603)
IEEE DOI 2210
Proteins, Location awareness, Training, Deep learning, Computational modeling, Medical, retrieval BibRef

Uddin, M.R.[Mostofa Rafid], Howe, G.[Gregory], Zeng, X.R.[Xiang-Rui], Xu, M.[Min],
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations,
CVPR22(20614-20623)
IEEE DOI 2210
Proteins, Protein engineering, Visualization, Image analysis, Shape, Semantics, Medical, biological and cell microscopy, Representation learning BibRef

Ghahremani, P.[Parmida], Marino, J.[Joseph], Dodds, R.[Ricardo], Nadeem, S.[Saad],
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides,
CVPR22(21367-21373)
IEEE DOI 2210
Proteins, Multiplexing, Visualization, Pathology, Graphics processing units, Glass BibRef

Mkhayar, K.[Khaoula], Daoui, O.[Ossama], Elkhattabi, S.[Souad], Chtita, S.[Samir], Elkhalabi, R.[Rachida],
In silico molecular investigations of derived cyclohexane-1,3-dione compounds as potential inhibitors of protein tyrosine kinase C-met: 2D QSAR, molecular docking and ADMET,
ISCV22(1-8)
IEEE DOI 2208
Proteins, Drugs, In vivo, Inhibitors, Biological system modeling, Linear regression, Lung cancer, QSAR, ADMET, Molecular Docking, C-met BibRef

Zhong, E.D.[Ellen D.], Lerer, A.[Adam], Davis, J.H.[Joseph H.], Berger, B.[Bonnie],
CryoDRGN2: Ab initio neural reconstruction of 3D protein structures from real cryo-EM images,
ICCV21(4046-4055)
IEEE DOI 2203
Proteins, Adaptation models, Computational modeling, Reconstruction algorithms, Task analysis, Medical, biological, Vision applications and systems BibRef

Ratul, M.A.R.[Md Aminur Rab], Elahi, M.T.[Maryam Tavakol], Mozaffari, M.H.[M. Hamed], Lee, W.[WonSook],
PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure,
DICTA20(1-3)
IEEE DOI 2201
Deep learning, Digital images, Computer architecture, Feature extraction, Protein sequence, Functional analysis, Skip Connection BibRef

Chung, S.C.[Szu-Chi], Hung, C.Y.[Cheng-Yu], Siao, H.L.[Huei-Lun], Wu, H.Y.[Hung-Yi], Chang, W.H.[Wei-Hau], Tu, I.P.[I-Ping],
Cryo-Ralib: A Modular Library for Accelerating Alignment in CRYO-EM,
ICIP21(225-229)
IEEE DOI 2201
Proteins, Image analysis, Pandemics, Graphics processing units, Data visualization, Data science, Computational biology, cryo-EM, multiple reference alignment BibRef

de Oliveira, G.B.[Gabriel Bianchin], Pedrini, H.[Helio], Dias, Z.[Zanoni],
MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction,
CAIP21(I:175-184).
Springer DOI 2112
BibRef

Sverrisson, F.[Freyr], Feydy, J.[Jean], Correia, B.E.[Bruno E.], Bronstein, M.M.[Michael M.],
Fast end-to-end learning on protein surfaces,
CVPR21(15267-15276)
IEEE DOI 2111
Proteins, Deep learning, Drugs, Computational modeling, Atomic layer deposition BibRef

Kishan, K.C., Cui, F.[Feng], Haake, A.R.[Anne R.], Li, R.[Rui],
Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction,
ICPR21(7126-7133)
IEEE DOI 2105
Proteins, Recurrent neural networks, Biological system modeling, Computational modeling, Predictive models, Logic gates, Amino acids BibRef

Golkov, V.[Vladimir], Skwark, M.J.[Marcin J.], Mirchev, A.[Atanas], Dikov, G.[Georgi], Geanes, A.R.[Alexander R.], Mendenhall, J.[Jeffrey], Meiler, J.[Jens], Cremers, D.[Daniel], c
3D Deep Learning for Biological Function Prediction from Physical Fields,
3DV20(928-937)
IEEE DOI 2102
Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure. Proteins, Atomic measurements, Electrostatics, Electric potential, Amino acids, Compounds, drug discovery BibRef

Arnal, R.D., Metz, M., Morgan, A.J., Chapman, H.N., Millane, R.P.,
Ab initio phasing using diffraction data from different crystal forms,
IVCNZ19(1-6)
IEEE DOI 2004
ab initio calculations, biology computing, crystal structure, crystallography, iterative methods, molecular biophysics, proteins, phase problem BibRef

Bahi, M., Batouche, M.,
Deep semi-supervised learning for DTI prediction using large datasets and H2O-spark platform,
ISCV18(1-7)
IEEE DOI 1807
Big Data, drugs, genomics, learning (artificial intelligence), medical computing, molecular biophysics, neural nets, proteins, Stacked Autoencoders BibRef

Sriwastava, B.K.[Brijesh Kumar], Basu, S.[Subhadip], Maulik, U.[Ujjwal],
A Quasi-Clique Mining Algorithm for Analysis of the Human Protein-Protein Interaction Network,
PReMI17(411-417).
Springer DOI 1711
BibRef

Bankapur, S.[Sanjay], Patil, N.[Nagamma],
Efficient and Effective Multiple Protein Sequence Alignment Model Using Dynamic Progressive Approach with Novel Look Back Ahead Scoring System,
PReMI17(397-404).
Springer DOI 1711
BibRef

Li, H., Zhao, Y.M.[Yu-Ming], Bai, J., Zhang, J., Yang, J.,
Comparative study in complex network: Node degree and topological potential,
ICIVC17(928-932)
IEEE DOI 1708
Annealing, Indexes, Proteins, comparative study, complex network, node degree, topological, potential BibRef

Martino, A.[Alessio], Maiorino, E.[Enrico], Giuliani, A.[Alessandro], Giampieri, M.[Mauro], Rizzi, A.[Antonello],
Supervised Approaches for Function Prediction of Proteins Contact Networks from Topological Structure Information,
SCIA17(I: 285-296).
Springer DOI 1706
BibRef

Hossain, M.J.,
Keynote speaker: An experimental and computational framework to build a dynamic protein atlas for human cell division,
IVPR17(1-1)
IEEE DOI 1704
Biographies;Embryo;Engineering profession;Europe;Proteins;Software BibRef

Shweta, Ekbal, A., Saha, S., Bhattacharyya, P.,
A deep learning architecture for protein-protein Interaction Article identification,
ICPR16(3128-3133)
IEEE DOI 1705
Convolution, Feature extraction, Neural networks, Protein engineering, Proteins, Support vector machines, Convolutional Neural Network (CNN), Protein Protein Interaction (PPI), Word, Embedding BibRef