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[Preprints] [Journal Articles (161)] [Conference Papers and Tutorials (23)] [Abstracts and Posters (147)] [Book Chapters (8)] [Theses (14)]

The citations to these papers in Google Scholar, the papers listed in PubMed, and the PDFs of the papers in ResearchGate.

Preprints

Soltanikazemi, E., Quadir, F., Roy, R.S., Cheng, J. (2021). Distance-based Reconstruction of Protein Quaternary Structures from Inter-Chain Contacts. bioRxiv.

Highsmith M.R., Cheng, J. (2021). TAPIOCA: Topological Attention and Predictive Inference of Chromatin Arrangement Using Epigenetic Features. bioRxiv.

Journal Articles

    167. Highsmith, M., Cheng, J. Four-Dimensional Chromosome Structure Prediction. International Journal of Molecular Sciences. Accepted.

    166. Kryshtafovych, A., et al. Modeling SARS-CoV2 proteins in the CASP-commons experiment. Proteins, accepted, 2021.

    165. Lensink, M. F., Brysbaert, G., Mauri, T., Nadzirin, N., Velankar, S., Chaleil, R. A., ... & Wodak, S. J. Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment. Proteins: Structure, Function, and Bioinformatics.

    164. Douglas, R. N., Yang, H., Zhang, B., Chen, C., Han, F., Cheng, J., & Birchler, J. A. (2021). De novo centromere formation on chromosome fragments with an inactive centromere in maize (Zea mays). Chromosome Research, 1-13

    163. Quadir, F., Roy, R.S., Soltanikazemi, E., Cheng, J. DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-Chain Contact Prediction and Distance-Based Modelling. Frontiers in Molecular Biosciences, Accepted.

    162. Liu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J. (2021). Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14. Proteins.

    161. Wu, T., Liu, J., Guo, Z., Hou, J., & Cheng, J. (2021). MULTICOM2: an open-source protein structure prediction system powered by deep learning and distance prediction. Scientific Reports.

    160. Nicolas Blavet el al., Sequence of the supernumerary B chromosome of maize provides insight into its drive mechanism and evolution, PNAS, 2021.

    159. Quadir, F., Roy, R., Halfmann, R., Cheng, J. DNCON2_Inter: Predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning. Scientific Reports, accepted, 2021.

    158. Chen, X., Liu, J., Guo, Z., Wu, T., Hou, J., & Cheng, J. Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14. Scientific Reports, 2021.

    157. Guo, Z, Wu, T., Liu, J., Hou, J., & Cheng, J. Improving deep learning-based protein distance prediction in CASP14. Bioinformatics, 2021.

    156. M. Highsmith, J. Cheng. VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data. Scientific Reports, accepted, 2021.

    155. H. Yang, X. Shi, C. Chen, J. Hou, T. Ji, J. Cheng, J.A. Birchler. Predominantly inverse modulation of gene expression in genomically unbalanced disomic haploid maize. Plant Cell, accepted, 2021.

    154. X. Shi, H. Yang, C. Chen, J. Hou, K.M. Hanson, P.S. Albert, T. Ji, J. Cheng, J.A. Birchler. Genomic imbalance determines positive and negative modulation of gene expression in diploid maize. Plant Cell, accepted, 2021.

    153. Chen, C., Wu, T., Guo, Z., Cheng, J. (2021). Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction. Proteins, accepted. [At Authorea]. (The first paper of applying attention networks to protein structure prediction and explaining protein folding information learned by AI predictions)

    152. M. Necci et al. Critical Assessment of Protein Intrinsic Disorder Prediction. Nature Methods, accepted.

    151. T. Wu, Z. Guo, J. Hou, J. Cheng. DeepDist: real-value inter-residue distance prediction with deep residual convolutional network. BMC Bioinformatics, accepted.

    150. C. Chen, J. Hou, X. Shi, H. Yang, J.A. Birchler, J. Cheng. Prediction of transcription factor binding site across cell-types using attention-based deep neural networks. BMC Bioinformatics, accepted.

    149. Adil Al-Azzawi, Anes Ouadou, Ye Duan, and Jianlin Cheng. Auto3DCryoMap: An Automated Particle Alignment Approach for 3D cryo-EM Density Map Reconstruction . BMC Bioinformatics, accepted.

    148. Lawson, C. L., Kryshtafovych, A., Adams, P. D., Afonine, P., Baker, M. L., Barad, B. A., ..., Chiu, W. Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution. Nature Methods, accepted, 2020.

    147. C. Chen, J. Hou, X. Shi, H. Yang, J.A. Birchler, and J. Cheng. GNET2: An R package for constructing gene regulatory networks from transcriptomic data. Bioinformatics, 2020.

    146. A. Al-Azzawi, A. Ouadou, M. Highsmith, Y. Duan, J.J. Tanner, and J. Cheng. DeepCryoPicker: Fully Automated Deep Neural Network for Single Protein Particle Picking in cryo-EM. BMC Bioinformatics, 2020.

    145. Grunz-Borgmann, E. A., Nicholas, L. A., Spagnoli, S., Trzeciakowski, J. P., Valliyodan, B., Hou, J., Li, J, Cheng, J, ... Parrish, A. R. The renoprotective effects of soy protein in the aging kidney. Medical Research Archives, 8(3), 2020.

    144. Yang, B., Li, R., Liu, P. N., Geng, X., Mooney, B. P., Chen, C., ... Sun, G. Y. Quantitative proteomics reveals docosahexaenoic acid-mediated neuroprotective effects in lipopolysaccharide-stimulated microglial cells. Journal of Proteome Research, 2020.

    143. M. Alfarhood, J. Cheng. CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles. IEEE Access, 2020.

    142. Guo, Z., Hou, J., Cheng, J. DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures. Proteins: Structure, Function, and Bioinformatics. 2020.

    141. Chen C, Hou J, Tanner JJ, Cheng J. Bioinformatics methods for mass spectrometry-based proteomics data analysis. International Journal of Molecular Sciences. 21(8):2873, 2020.

    140. Si D, Moritz SA, Pfab J, Hou J, Cao R, Wang L, Wu T, Cheng J. Deep learning to predict protein backbone structure from high-resolution cryo-EM density maps. Scientific Reports. 10(1):1-22i, 2020. (The first paper on end-to-end deep learning prediction of 3D protein structures from cryo-EM density maps)

    139. Oluwadare O, Highsmith M, Turner D, Lieberman Aiden E, Cheng J. GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data. BMC Molecular and Cell Biology. 2020.

    138. Dong Y, Li D, Zhang C, Wu C, Wang H, Xin M, Cheng J, Lin J. Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN . Carbon. 2020

    137. Jasmer KJ, Hou J, Mannino P, Cheng J, Hannink M. Heme oxygenase promotes B‐Raf‐dependent melanosphere formation. Pigment Cell & Melanoma Research. 2020

    136. Johnson AF, Hou J, Yang H, Shi X, Chen C, Islam MS, Ji T, Cheng J, Birchler JA. Magnitude of modulation of gene expression in aneuploid maize depends on the extent of genomic imbalance. Journal of Genetics and Genomics. 2020.

    135. J. Hou, B. Adhikari, J. Tanner, J. Cheng. SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data. Proteins, 2020.

    134. Brown, P., RELISH Consortium, Y. Zhou. Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database, 2019

    133. N. Zhou, Y. Jiang, T.R. Bergquist, A.J. Lee, B.Z. Kacsoh, A.W. Crocker, ... & L. Davis. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology, 2019.

    132. M.F. Lensink, G. Brysbaert, N. Nadzirin, S. Velankar, R.A. Chaleil, T. Gerguri, ... & R. Kong. Blind prediction of homo‐and hetero‐protein complexes: The CASP13‐CAPRI experiment. Proteins: Structure, Function, and Bioinformatics, 2019.

    131. J. Yan, J. Cheng, L. Kurgan, V.N. Uversky. Structural and functional analysis of “non-smelly” proteins. Cell Mol. Life Sci. doi: 10.1007/s00018-019-03292-1, 2019.

    130. A. Al-Azzawi, A. Quadou, J.J. Tanner, J. Cheng. A super-clustering approach for fully automated single particle picking in Cryo-EM. Genes, accepted, 2019.

    129. T. Wu, J. Hou, B. Adhikari, J. Cheng. Analysis of several key factors influencing deep learning-based inter-residue contact prediction. Bioinformatics, accepted.

    128. J. Cheng, M. Choe, A. Elofsson, K. Han, J.Hou, A. Maghrabi, L.J. McGuffin, D. Menéndez-Hurtado, K. Olechnovič, T. Schwede, G. Studer, K. Uziela, Č. Venclovas, B. Wallner. Estimation of model accuracy in CASP13. Proteins, accepted. (Our deep learning method - DeepRank was ranked no. 1 in ranking protein models in terms of loss in CASP13. CASP13 invited contribution)

    127. A. Al-Azzawi, A. Quadou, J.J. Tanner, J. Cheng. AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in cryo-EM images. BMC Bioinformatics, accepted, 2019. [at BMC Bioinformatics]

    126. J. Hou, T. Wu, R. Cao, J. Cheng. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins, accepted. (CASP13 invited paper. The method was among ranked top 3 in CASP13 along with Google DeepMind's AlphaFold. This paper is one of the most cited papers in Proteins published in 2019-2020). [at Proteins]

    125. O. Oluwadare, Max Highsmith, J. Cheng. An overview of methods for reconstructing 3D chromosome and genome structures from Hi-C data. Biological Procedures Online. Accepted. [at BPO]

    124. Y. Dong, C. Wu, C. Zhang, Y. Liu, J. L. Cheng, and J. Lin. Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride. npj Computational Materials, accepted, 2019. [at npj]

    123. J. Hou, X. Shi, C. Chen, Md. Soliman, Adam F. Johnson, Tatsuo Kanno, Bruno Huettel, Ming-Ren Yen, Fei-Man Hsu, Tieming Ji, Paoyang Chen, Marjori Matzke, Antonius J.M. Matzke, Jianlin Cheng, James A. Birchler. Global impacts of chromosomal imbalance on gene expression in Arabidopsis and other taxa. Proceedings of the National Academy of Sciences (PNAS). In press. [at PNAS]

    122. H. Song, M. Chen, C. Chen, J. Cui, C.E. Johnson, J. Cheng, X. Wang, R.H. Swerdlow, R. G. DePalma, W. Xia, Z Gu. Proteomic Analysis and Biochemical Correlates of Mitochondrial Dysfunction following Low-Intensity Primary Blast Exposure. Journal of Neurotrauma, in press. [at PubMed]

    121. C. Olaya, B. Adhikari, G. Raikhy, J. Cheng, and H.R. Pappu. In silico localization of Tospoviridae family-wide conserved residues in nucleocapsid and silencing suppressor proteins of Tomato spotted wilt virus. Virology Journal, in press. [at PubMed]

    120. T. Trieu, O. Oluwadare, J. Cheng. Hierarchical Reconstruction of High-Resolution 3D Models of Human Chromosomes. Scientific Reports, 2019. [at bioRxiv]

    119. T. Trieu, O. Oluwadare, J. Wopata, and J. Cheng. GenomeFlow: A Comprehensive Graphical Tool for Modeling and Analyzing 3D Genome Structure. Bioinformatics, accepted. [at Bioinformatics]

    118. Y. Bian, C. He, J. Hou, J. Cheng, J. Qiu. PairedFB: a full hierarchical Bayesian model for paired RNA-seq data with heterogeneous treatment effects. Bioinformatics, accepted. [at Bioinformatics]

    117. Keasar C, McGuffin LJ, Wallner B, Chopra G, Adhikari B, Bhattacharya D, Blake L, Bortot LO, Cao R, Dhanasekaran BK, Dimas I, Faccioli RA, Faraggi E, Ganzynkowicz R, Ghosh S, Ghosh S, Giełdoń A, Golon L, He Y, Heo L, Hou J, Khan M, Khatib F, Khoury GA, Kieslich C, Kim DE, Krupa P, Lee GR, Li H, Li J, Lipska A, Liwo A, Maghrabi AHA, Mirdita M, Mirzaei S, Mozolewska MA, Onel M, Ovchinnikov S, Shah A, Shah U, Sidi T, Sieradzan AK, Ślusarz M, Ślusarz R, Smadbeck J, Tamamis P, Trieber N, Wirecki T, Yin Y, Zhang Y, Bacardit J, Baranowski M, Chapman N, Cooper S, Defelicibus A, Flatten J, Koepnick B, Popović Z, Zaborowski B, Baker D, Cheng J, Czaplewski C, Delbem ACB, Floudas C, Kloczkowski A, Ołdziej S, Levitt M, Scheraga H, Seok C, Söding J, Vishveshwara S, Xu D; Foldit Players consortium, Crivelli SN. An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Scientific Reports, 8(1):9939, 2018. [at Scientific Reports]

    116. O. Oluwadare; Y. Zhang; J. Cheng. A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data. BMC Genomics, 19:161, 2018. [at BMC Genomics]

    115. B. Adhikari, J. Cheng. CONFOLD2: Improved contact-driven ab initio protein structure modeling. BMC Bioinformatics, accepted, 2018. [at BMC Bioinformatics]

    114. B. Adhikari, J. Hou, J. Cheng. DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks. Bioinformatics, accepted. [at Bioinformatics]

    113. J. Hou, B. Adhikari, J. Cheng. DeepSF: deep convolutional neural network for mapping protein sequences to folds. Bioinformatics, accepted. [at Bioinformatics]

    112. O. Oluwadare, J. Cheng. ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data. BMC Genomics. 18:480, 2017. [at BMC Bioinformatics]

    111. X. Rui, J. Wen, A. Quitadamo, J. Cheng, and X. Shi. A deep auto-encoder model for gene expression prediction. BMC Genomics. 18(S9):845, 2017. [at BMC Genomics]

    110. B. Adhikari, J. Hou, J. Cheng. Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning. Proteins, accepted, 2017. [at Proteins] (Ranked among top contact prediction methods in CASP12. CASP12 invited contribution)

    109. H. Li, J. Hou, B. Adhikari, Q. Lyu, J. Cheng. Deep Learning Methods for Protein Torsion Angle Prediction. BMC Bioinformatics. 18:417, 2017. [at BMC Bioinformatics]

    108. J. Lingyan, Y. Wan, J. C. Anderson, J. Hou, S.M. Soliman, J. Cheng, S.C. Peck. Genetic Dissection of Arabidopsis MAP Kinase Phosphatase 1 (AtMKP1)-dependent PAMP-induced transcription pathways. Journal of Experimental Botany. Accepted, 2017. [at Journal of Experimental Botany]

    107. B. Adhikari, J. Cheng. Improved Protein Structure Reconstruction Using Secondary Structures, Contacts at Higher Distance Thresholds, and Non-Contacts. BMC Bioinformatics. 18(1):380, 2017.[at BMC Bioinformatics]

    106. Cao, Z. Zhong, J. Cheng. SMISS: A Protein Function Prediction Server by Integrating Multiple Sources. International Journal of Computational Intelligence in Bioinformatics and Systems Biology, accepted, 2017. [at IJCIBSB] and [at arxiv]

    105. R. Cao, D. Bhattacharya, J. Hou, J. Cheng. DeepQA: Improving the Estimation of Single Protein Model Quality with Deep Belief Networks. BMC Bioinformatics, accepted, 2016. [at BMC Bioinformatics] (The first paper of applying deep learning to protein model quality assessment)

    104. B. Adhikari, J. Nowotny, D. Bhattacharya, J. Hou, J. Cheng. ConEVA: a Toolbox fr Comprehensive Assessment of Protein Contacts. BMC Bioinformatics, accepted, 2016. [at BMC Bioinformatics]

    103. H. Li, Q. Lyu, J. Cheng. A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning. Journal of Proteomics and Bioinformatics, accepted, 2016. [PDF]

    102. T. Trieu, J. Cheng. 3D Genome Structure Modeling by Lorentzian Objective Function. Nucleic Acids Research, accepted, 2016. [at NAR]

    101. R. Cao, B. Adhikari, D. Bhattacharya, M. Sun, J. Hou, J. Cheng. QAcon: Single Model Quality Assessment Using Protein Structural and Contact Information with Machine Learning Techniques. Bioinformatics, accepted, 2016. [at PubMed]

    100. H. Song, Y. Lu, Z. Qu, V.V. Mossine, M.B. Martin, J. Hou, J. Cui, B.A. Peculis, T.P. Mawhinney, J. Cheng, C.M. Greenlief, K. Fritsche, F.J. Schmidt, R.B. Walter, D.B. Lubahn, G.Y. Sun, Z. Gu. Effects of aged garlic extract and FruArg on gene expression and signaling pathways in lipopolysaccharide-activated microglial cells. Scientific Reports. 6:35323, 2016. [at Scientific Reports]

    99. B. Adhikari, T. Tuan, J. Cheng. Chromosome3D: Reconstructing Three-Dimensional Chromosomal Structures from Hi-C Interaction Frequency Data using Distance Geometry Simulated Annealing. BMC Genomics, 17:886, 2016. [at BMC Genomics]

    98. Jiang Y, Oron TR, Clark WT, Bankapur AR, D'Andrea D, Lepore R, Funk CS, Kahanda I, Verspoor KM, Ben-Hur A, Koo da CE, Penfold-Brown D, Shasha D, Youngs N, Bonneau R, Lin A, Sahraeian SM, Martelli PL, Profiti G, Casadio R, Cao R, Zhong Z, Cheng J, Altenhoff A, Skunca N, Dessimoz C, Dogan T, Hakala K, Kaewphan S, Mehryary F, Salakoski T, Ginter F, Fang H, Smithers B, Oates M, Gough J, Törönen P, Koskinen P, Holm L, Chen CT, Hsu WL, Bryson K, Cozzetto D, Minneci F, Jones DT, Chapman S, Bkc D, Khan IK, Kihara D, Ofer D, Rappoport N, Stern A, Cibrian-Uhalte E, Denny P, Foulger RE, Hieta R, Legge D, Lovering RC, Magrane M, Melidoni AN, Mutowo-Meullenet P, Pichler K, Shypitsyna A, Li B, Zakeri P, ElShal S, Tranchevent LC, Das S, Dawson NL, Lee D, Lees JG, Sillitoe I, Bhat P, Nepusz T, Romero AE, Sasidharan R, Yang H, Paccanaro A, Gillis J, Sedeño-Cortés AE, Pavlidis P, Feng S, Cejuela JM, Goldberg T, Hamp T, Richter L, Salamov A, Gabaldon T, Marcet-Houben M, Supek F, Gong Q, Ning W, Zhou Y, Tian W, Falda M, Fontana P, Lavezzo E, Toppo S, Ferrari C, Giollo M, Piovesan D, Tosatto SC, Del Pozo A, Fernández JM, Maietta P, Valencia A, Tress ML, Benso A, Di Carlo S, Politano G, Savino A, Rehman HU, Re M, Mesiti M, Valentini G, Bargsten JW, van Dijk AD, Gemovic B, Glisic S, Perovic V, Veljkovic V, Veljkovic N, Almeida-E-Silva DC, Vencio RZ, Sharan M, Vogel J, Kansakar L, Zhang S, Vucetic S, Wang Z, Sternberg MJ, Wass MN, Huntley RP, Martin MJ, O'Donovan C, Robinson PN, Moreau Y, Tramontano A, Babbitt PC, Brenner SE, Linial M, Orengo CA, Rost B, Greene CS, Mooney SD, Friedberg I, Radivojac P. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biology. 2016. [at Genome Biology]

    97. D. Bhattacharya, R. Cao, J. Cheng. UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling. Bioinformatics, accepted, 2016. [at Bioinformatics]

    96. W.R. Folk, A. Smith, H. Song, D. Chuang, J. Cheng, Z. Gu, G. Sun. Does concurrent use of some botanicals interfere with treatment of tuberculosis? Neuromolecular Med., accepted, 2016. [at NeuroMolecular Medicine]

    95. B. Gandolfi, S. Alamri, W.G. Darby, B. Adhikari, J.C. Lattimer, R. Malik, C.M. Wade, L.A. Lyons, J. Cheng, J.F. Bateman, P. McIntyre, S.R. Lamande, B. Haase. A novel variant in CAMH is associated with blood type AB in Ragdoll cats. PLoS ONE, accepted, 2016. [at Plos ONE]

    94. J. Li, J. Cheng. A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling. Scientific Reports, accepted, 2016. [at PubMed]

    93. D. Bhattacharya, J. Nowotny, R. Cao, J. Cheng. 3Drefine: An Interactive Web Server for Efficient Protein Structure Refinement. Nucleic Acids Research, web server issue, accepted, 2016. [at NAR website]

    92. R. Cao, J. Cheng. Protein single-model quality assessment by feature-based probability density functions. Scientific Reports, accepted. [at Scientific Reports].

    91. M.F. Lensink et al. Prediction of homo- and hetero-protein complexes by ab-initio and template-based docking: a CASP-CAPRI experiment. Proteins, accepted, 2016. [at PubMed].

    90. D. Bhattacharya, B. Adhikari, J. Li, J. Cheng. FRAGSION: ultra-fast protein fragment library generation by IOHMM sampling. Bioinformatics, accepted, 2016. [at Bioinformatics web site].

    89. S. Cui, T. Ji, J. Li, J. Cheng, J. Qiu. What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment? Statistical Applications in Genetics and Molecular Biology (SAGMB), 15(2):87-105, 2016. [at SAGMB]

    88. J. Nowotny, A. Wells, O. Oluwadare, L. Xu, R. Cao, T. Trieu, C. He, J. Cheng. GMOL: an interactive tool for 3D genome structure visualization. Scientific Reports, accepted, 2016. [at Scientific Reports]

    87. W. Lei, Y. Lu, J. Hou, J. Li, J. Browning, P. Eichen, J. Cheng, D. Lubahn, W. Folk, G. Sun, K. Fritsche. Immunomodulation of innate immune cells by Sutherlandia frutescens: A transcriptomic analyses. FASEB Journal. 29(S1):593.3, 2015. [at FASEB journal]

    86. T. Tuan, J. Cheng. MOGEN: a tool for reconstructing 3D models of genomes from chromosomal conformation capturing data. Bioinformatics, accepted, 2015. doi: 10.1093/bioinformatics/btv754. [at Bioinformatics]

    85. Y. Lu, N. Starkey, W. Li, J. Li, J. Cheng, W. Folk, D. Lubahn. Inhibition of Hedgehog-signaling driven genes in prostate cancer cells by Sutherlandia frutescens extract. PLoS ONE. 10(12):e0145507, 2015. [at PLoS ONE web site]

    84. Y. Lu, J. Li, J. Cheng, D.B. Lubahn. Messenger RNA profile analysis deciphers new Esrrb responsive genes in prostate cancer cells. BMC Molecular Biology. 16(1):21, 2015. [at BMC Molecular Biology]

    83. Y. Lu, J. Li, J. Cheng, D.B. Lubahn. Genes targeted by the Hedgehog-signaling pathway can be regulated by Estrogen related receptor B. BMC Mol Biol.. 16(1):19, 2015. [BMC Molecular Biology].

    82. T. Jo, J. Hou, J. Eickholt, J. Cheng. Improving protein fold recognition by deep learning networks. Scientific Reports. 5:17573, 2015. [Scientific Reports]

    81. J. Nowotny, S. Ahmed, L. Xu, O. Oluwadare, H. Chen, N. Hensley, T. Trieu, R. Cao, J. Cheng. Iterative reconstruction of three-dimensional models of human chromosomes from chromosomal contact data. BMC Bioinformatics, 16(1):338, 2015. [at BMC Bioinformatics].

    80. R. Cao, J. Cheng. Deciphering the association between gene function and spatial gene-gene interactions in 3D human genome conformation. BMC Genomics, 16:880, 2015. [at BMC Genomics].

    79. D. Bhattacharya, J. Cheng. De novo portein conformational sampling using a probabilistic graphical model. Scientific Reports, 5:16332, 2015. [at Scientific Reports]

    78. J. Li, R. Cao, J. Cheng. A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11. BMC Bioinformatics, 16:337, 2015. [at BMC Bioinformatics]

    77. J. Hou, D. Zhu, J. Cheng. An overview of bioinformatics methods for modeling biological pathways in yeast. Briefings in Functional Genomics, accepted. [Briefings in Functional Genomics]

    76. R. Cao, D. Bhattacharya, B. Adhikari, J. Li, J. Cheng. Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11. Proteins, accepted. [at Proteins] (MULTICOM was ranked among the best methods in 2014 CASP11 experiment. CASP11 invited contribution)

    75. R. Cao, J. Cheng. Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks. Methods, accepted. [at Methods].

    74. J. Hou, G. Stacey, J. Cheng. Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods. EURASIP Journal on Bioinformatics and Systems Biology. 1:5, 2015. [at EURASIP]

    73. X. Deng, J. Gumm, S. Karki, J. Eickholt, J. Cheng. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions. Int J Mol Sci. 16(7):15384-15404, 2015. [at IJMS].

    72. J. Li, B. Adhikari, J. Cheng. An Improved Integration of Template-based and Template-Free Protein Structure Modeling Methods and its Assessment in CASP11. Protein Pept. Lett. 22(7):586-93, 2015. [at Protein & Peptide Letters].

    71. B. Adhikari, D. Bhattacharya, R. Cao, J. Cheng. CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding Proteins. 83(8):1436-1439, 2015. [at Proteins].

    70. R. Cao, D. Bhattacharya, B. Adhikari, J. Li, J. Cheng. Large-Scale Model Quality Asessment for Improving Protein Tertiary Structure Prediction. 23rd International Conference on Intelligent Systems for Molecular Biology (ISMB), Bioinformatics. 31(12):i116-i123, 2015. [at Bioinformatics]

    69. J. Li, J. Hou, L. Sun, J.M. Wilkins, Y. Lu, C.E. Niederhuth, B.R. Merideth, T.P. Mawhinney, V. Valeri, C.M. Greenlief, J.C. Walker, W.R. Folk, M. Hannink, D.B. Lubahn, J.A. Birchler, J. Cheng. From Gigabyte to Kilobyte: a Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data. PLoS ONE. 10(4):e0125000, 2015. [at PLoS One website].

    68. H. Zhou, Z. Qu, V. Mossine, D. Nknolise, J. Li, Z. Chen, J. Cheng, M, M. Greenlief, T. Mawhinney, P. Brown, K. Fritsche, M. Hannink, D. Lubahn, G. Sun, Z. Gu. Proteomic Analysis of the Effects of Aged Garlic Extract and its FruArg Component on Lipopolysaccharide-induced Neuroinflammatory Response in Microglial Cells. PLoS ONE. 9(11):e113531, 2014. [at PLoS ONE Website].

    67. Q. Qi, J. Li, J. Cheng. Reconstruction of Metabolic Pathways by Combining Probabilistic Graphical Model-based and Knowledge-based Methods. BMC Proceeding, 8(S6):S5, 2014. [at BMC Proceeding Website].

    66. X. Deng, J. Cheng. Enhancing HMM-Based Protein Profile-Profile Alignment with Structural Features and Evolutionary Coupling Information. BMC Bioinformatics. 15:252, 2014. [at BMC Bioinformatics Website].

    65. M. Spencer, J. Eickholt, J. Cheng. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction. IEEE Transactions on Computational Biology and Bioinformatics. Accepted. [PDF]

    64. T. Jo, J. Cheng. Improving Protein Fold Recognition by Random Forest. BMC Bioinformatics. 15(S11):S14, 2014. [at BMC Bioinformatics website]

    63. Z Qu, F. Meng, R. Bomgarden, R. Viner, J. Li, J. Rogers, J. Cheng, C. Greenlief, J. Cui, D. Lubahn, G. Sun, and Z. Gu. Proteomic Quantification and Site-Mapping of S-Nitrosylated Proteins Using Isobaric iodoTMT Reagents. Journal of Proteome Research. 13(7):3200-11, 2014. [at Journal of Proteome Research]

    62. P. Gong, Z. Madak-Ergogan, J. Li, J. Cheng, C.M. Greenlief, W.G. Helferich, J.A. Katzenellengogen, B.S. Katzenellengogen. Transcriptome analyses reveal gene network regulated by ERalpha and ERbeta that control distinct effects of different botanical estrogens. Nuclear Receptor Signaling. 12:e001, 2014. [at NRS website].

    61. R. Cao, Z. Wang, Y. Wang, J. Cheng. SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines. BMC Bioinformatics, 15:120, 2014. [at BMC Bioinformatics' website].

    60. R. Cao, Z. Wang, J. Cheng. Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment. BMC Structural Biology, 14:13, 2014. [at BMC Structural Biology's website].

    59. G.A. Khoury, A. Liwo, F. Khatib, H. Zhou, G. Chopra, J. Bacardit, L.O. Bortot, R.A. Faccioli, X. Deng, Y. He, P. Krupa, J. Li, M.A. Mozolewska, A.K. Sieradzan, J. Smadbeck, T. Wirecki, S. Cooper, J. Flatten, K. Xu, D. Baker, J. Cheng, A.C.B. Delbem, C.A. Floudas, C. Keasar, M. Levitt, Z. Popovic, H.A. Scheraga, J. Skolnick, S.N. Crivelli, and Foldit Players. WeFold: A Coopetition for Protein Structure Prediction. Proteins, in press. [at Proteins journal].

    58. Z. Qu, F. Meng, H. Zhou, J. Li, Q. Wang, F. Wei, J. Cheng, C.M. Greenlief, D.B. Lubahn, G.Y. Sun, S. Liu, Z. Gu. NitroDIGE Analysis Reveals Inhibition of Protein S-Nitrosylation by Epigallocatechin Gallates in Lipopolysaccharide-stimulated Microglial Cells. Journal of Neuroinflammation, 11:17, 2014. [at Journal of Neuroinflamation].

    57. T. Trieu, J. Cheng. Large-scale reconstruction of 3D structures of human chromosomes from chromosomal contact data. Nucleic Acids Research. 42(7):e52, 2014. [at NAR's website].

    56. X. Deng, J. Li, J. Cheng. Predicting protein model quality from sequence alignments by support vector machines. Journal of Proteomics and Bioinformatics. S9:001, 2013. [PDF], [at Journal's website].

    55. L. Sun, A.F. Johnson, J. Li, A.S. Lambdin, J. Cheng, J.A. Birchler. Differential effect of aneuploidy on the X chromosome and genes with sex-biased expression in Drosophila. Proceeding of National Academy of Sciences (P.N.A.S), USA. 110(41):16514-9, 2013. [at PNAS's web site].

    54. M. Zhu, J. Dahmen, G. Stacey, J. Cheng. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data. BMC Bioinformatics. 14:278, 2013. [at BMC Bioinformatics's web site]. .

    53. J. Eickholt, J. Cheng. A Study and Extension of DNcon: a Method for Protein Residue-Residue Contact Prediction Using Deep Networks. BMC Bioinformatics. 14(Suppl 14):S12, 2013. [at BMC Bioinformatics' web site]

    52. D. Bhattacharya, J. Cheng. i3Drefine Software for Protein 3D Structure Refinement and its Assessment in CASP10. PLoS ONE. 8(7):e69648, 2013. [at PLoS ONE's website]

    51. L. Sun, A.F. Johnson, R.C. Donohue, J. Li, J. Cheng, J.A. Birchler. Dosage Compensation and Inverse Effects in Triple X Metafemales of Drosophila. Proceedings of the National Academy of Sciences (PNAS). 110(18):7383-8, 2013. [at PubMed].

    50. K.H. Taylor, A. Briley, Z. Wang, J. Cheng, H. Shi, C.W. Caldwell. Aberrant Epigenetic Gene Regulation in Lymphoid Malignancies. Seminars in Hematology. 50(1):38-47, 2013. [at Elsevier's website].

    49. J. Eickholt, J. Cheng. DNdisorder: Predicting Protein Disorder Using Boosting and Deep Networks. BMC Bioinformatics. 14:88, 2013. [at BMC Structural Biology's website]

    48. J. Li, X. Deng, J. Eickholt, J. Cheng. Designing and Benchmarking the MULTICOM Protein Structure Prediction System. BMC Structural Biology. 13:2, 2013. [at BMC Structural Biology Website] .

    47. Z. Wang, R. Cao, K. Taylor, A. Briley, C. Caldwell, J. Cheng. The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types. PLoS ONE. 8(3):e58793, 2013 [at PLoS ONE's web site].

    46. L. Sun, H.R. Fernandez, R.C. Donohue, J. Li, J. Cheng, J.A. Birchler. Male-Specific Lethal Complex in Drosophila Counteracts the Effect of Histone Acetylation and Does Not Mediate Dosage Compensation. Proceedings of National Academy of Sciences (P.N.A.S.) USA. 110(9):E808-17, 2013.[at PNAS' website].

    45. P. Radivojac, W. Clark, T.B. Oron, A.M. Schnoes, T. Wittkop, A. Sokolov, K. Graim, C. Funk, K. Verspoor, A. Ben-Hur, G. Pandey, J.M. Yunes, A.S. Talwakar, S. Repo, M.L. Souza, D. Piovesan, R. Casadio, Z. Wang, J. Cheng, H. Fang, J. Gough, P. Koskinen, P. Toronen, J. Nokso-Koivisto, L. Holm, D. Cozzetto, D.W. Buchan, K. Bryson, D.T. Jones, B. Limaye, H. Inamdar, A. Datta, S.K. Manjari, R. Joshi, M. Chitale, D. Kihara, A.M. Lisewski, S. Erdin, E. Venner, O. Lichtarge, R. Rentzsch, H. Yang, A.E. Romero, P. Bhat, A. Paccanaro, T. Hamp, R. Kassner, S. Seemayer, E. Vicedo, C. Schaefer, D. Achten, F. Auer, A. Bohm, T. Braun, M. Hecht, M. Heron, P. Honigschmid, T. Hopf, S. Kaufmann, M. Kiening, D. Krompass, C. Landerer, Y. Mahlich, M. Roos, J. Bjorne, T. Salakoski, A. Wong, H. Shatkay, M.N. Wass, M.J.E. Sternberg, N. Skunca, F. Supek, M. Bosnjak, P. Panov, S. Dzeroski, T. Smuc, Y.A.I. Kourmpetis, A.D.J. van Dijk, C.J.F. ter Braak, Y. Zhou, Q. Gong, X. Dong, W. Tian, M. Falda, P. Fontana, E. Lavezzo, B.D. Camillo, S. Toppo, L. Lan, N. Djuric, Y. Guo, S. Vucetic, A. Bairoch, M. Linial, P.C. Babbitt, S.E. Brenner, C. Orengo, B. Rost, S.D. Mooney, I. Friedberg. A Large-Scale Evaluation of Computational Protein Function Prediction. Nature Methods. 10(13):221-7, 2013. [at Nature Methods' website].

    44. Z. Wang, R. Cao, J. Cheng. Three-Level Prediction of Protein Function by Combining Profile-Sequence Search, Profile-Profile Search, and Domain Co-occurrence Networks. BMC Bioinformatics. 14(Suppl 3):S3, 2013. [at BMC Bioinformatics' website].

    43. D. Bhattacharya, J. Cheng. 3DRefine: Consistent Protein Structure Refinement by Optimizing Hydrogen Bonding Network and Atomic Level Energy Minimization. Proteins, 81(1):119-31, 2013. [at PubMed].

    42. J. Eickholt, J. Cheng. Predicting Protein Residue-Residue Contacts Using Deep Networks and Boosting. Bioinformatics. 28(23):3066-3072, 2012. [at Bioinformatics web site]. This is the first deep learning method on protein contact prediction, ranked no. 1 in the wolrd-wide CASP10 experiment in 2012. For the first time, it demonstrated that deep learning is the best method for protein contact prediction, which inspired the development of deep learning methods in the field of protein structure prediction.

    41. M. Zhu, X. Deng, T. Joshi, D. Xu, G. Stacey, J. Cheng. Reconstructing Differentially Co-expressed Gene Modules and Regulatory Networks of Soybean Cells. BMC Genomics, 13:434, 2012. [at BMC Genomics web site].

    40. J. Cheng, J. Li, Z. Wang, J. Eickholt, X. Deng. The MULTICOM Toolbox for Protein Structure Prediction. BMC Bioinformatics, 13:65, 2012. [at BMC Bioinformatics web site]

    39. J. Cheng, J. Eickholt, Z. Wang, and X. Deng. Recursive Protein Modeling: a Divide and Conquer Strategy for Protein Structure Prediction and its Case Study in CASP9. Journal of Bioinformatics and Computational Biology, vol. 10, no. 3, 2012. [at JBCB journal]. DOI: 10.1142/S0219720012420036. [PDF] [at NIH PMC]

    38. X. Zhang, Z. Wang, X. Zhang, M. Le, J. Sun, D. Xu, J. Cheng, and G. Stacey. Evolutionary Dynamics of Protein Domain Architecture in Plants. BMC Evolutionary Biology, 12:6, 2012. [at BMC Evolutionary Biology web site]

    37. T. Joshi, K. Patil, M.R. Fitzpatrick, L.D. Franklin, Q. Yao, Z. Wang, M. Libault, L. Brechenmacher, B. Valiyodan, X. Wu, J. Cheng, G. Stacey, H. Nguyen, and D. Xu. Soybean Knowledge Base (SoyKB): A Web Resource for Soybean Translational Genomics. BMC Genomics, 13(Suppl 1):S15, 2012. [at BMC Genomics web site]

    36. Z. Wang and J. Cheng. An Iterative Self-Refining and Self-Evaluating Approach for Protein Model Quality Estimation. Protein Science, 21(1):142-151, 2012. [at Protein Science's web site] [PDF]

    35. X. Deng, J. Eickholt, J. Cheng. A Comprehensive Overview of Computational Protein Disorder Prediction Methods. Molecular BioSystems, 8(1):114-121, 2012. [at Molecular BioSystems web site]

    34. J. Eickholt, Z. Wang, J. Cheng. A Conformation Ensemble Approach to Protein Contact Prediction. BMC Structural Biology, 11:38, 2011. [at BMC Structural Biology web site]

    33. X. Deng and J. Cheng. MSACompro: Protein Multiple Sequence Alignment Using Predicted Secondary Structure, Solvent Accessibility, and Residue-Residue Contacts. BMC Bioinformatics. 12:472, 2011. [Open access at BMC Bioinformatics] .

    32. Z. Wang, J. Eickholt, J. Cheng. APOLLO: A Quality Assessment Service for Single and Multiple Protein Models. Bioinformatics. 27(12):1715-1716, 2011. [Open Access at Bioinformatics Website]

    31. K. Tanaka, C. Nguyen, M. Libault, J. Cheng, Gary Stacey. Enzymatic Activity of the Soybean Ecto-Apyrase GS52 is Essential for Stimulation of Nodulation. Plant Physiology. 155(4):1988-98, 2011. [at Plant Physiology's web site].

    30. Z. Wang, X. Zhang, M. Le, D. Xu, G. Stacey, and J. Cheng. A Protein Domain Co-Occurrence Network Approach for Predicting Protein Function and Inferring Species Phylogeny. PLoS ONE. 6(3): e17906, 2011. [at PLoS ONE web site].

    29. J. Eickholt, X. Deng, and J. Cheng. DoBo: Protein Domain Boundary Prediction by Integrating Evolutionary Signals and Machine Learning. BMC Bioinformatics. 12:43, 2011. [at BMC Bioinformatics] .

    28. M. Libault, L. Brechenmacher, J. Cheng, D. Xu, G. Stacey. Root Hair Systems Biology. Trends in Plant Science. 15(11):641-650, 2010. [at Trends' web site].

    27. Z. Wang, J. Eickholt, and J. Cheng. MULTICOM: A Multi-Level Combination Approach to Protein Structure Prediction and its Assessment in CASP8. Bioinformatics. 26(7):882-888, 2010. [at Bioinformatics web site]. The MULTICOM system was ranked among the best methods in template-based modeling, template-free modeling, protein model quality assessment, protein contact map prediction, and protein disorder prediction during CASP9, 2010.

    26. J. Schmutz, S. Cannon, J. Schlueter, J. Ma, T. Mitros, W. Nelson, D. Hyten, Q. Song, J. Thelen, J. Cheng, D. Xu, U. Hellsten, G. May, Y. Yu, T. Sakurai, T. Umezawa, M. Bhattacharyya, D. Sandhu, B. Valliyodan, E. Lindquist, M. Peto, D. Grant, S. Shu, D. Goodstein, K. Barry, M. Futrell-Griggs, J. Du, Z. Tian, L. Zhu, N. Gill, T. Joshi, M. Libault, A. Sethuraman, X. Zhang, S. Shinozaki, H. Nguyen, R. Wing, P. Cregan, J. Specht, J. Grimwood, D. Rokhsar, G. Stacey, R. Shoemaker and S. Jackson. Genome Sequence of the Palaeopolyploid Soybean. Nature. 463:178-83, 2010. [at Nature website].

    25. Z. Wang, M. Libault, T. Joshi, B. Valliyodan, H. Nguyen, D. Xu, G. Stacey, and J. Cheng. SoyDB: A Knowledge Database of Soybean Transcription Factors. BMC Plant Biology. 10:14, 2010 [SoyDB database] [open access at BMC Plant Biology] .

    24. G. Lin, Z. Wang, D. Xu, and J. Cheng. Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines. BMC Bioinformatics, 11(Suppl 3):S1, 2010. [at BMC Bioinformatics website]

    23. X. Deng, J. Eickholt, and J. Cheng. PreDisorder: Ab Initio Sequence-Based Prediction of Protein Disordered Regions. BMC Bioinformatics, 10:436, 2009. [at BMC Bioinformatics website]. Predisorder was ranked among the best protein disorder prediction methods in CASP9, 2010.

    22. J. Cheng, Z. Wang, A.N. Tegge and J. Eickholt. Prediction of Global and Local Quality of CASP8 Models by MULTICOM series. Proteins, vol. 77, pp. 181-184, 2009. [at Proteins web site] (Ranked among the best methods in model quality assessment in CASP8. CASP8 invited contribution)

    21. A.N. Tegge, Z. Wang, J. Eickholt, and J. Cheng. NNcon: Improved Protein Contact Map Prediction Using 2D-Recursive Neural Networks. Nucleic Acids Research , vol. 37, pp. w515-w518, 2009. [at NAR web site]. (NNcon was ranked among the best contact map prediction methods in CASP8)

    20. E.E. Stagner, D.J. Bouvrette, J. Cheng, and E.C. Bryda. The Polycystic Kidney Disease-related Proteins Bicc1 and SamCystin Interact. Biochemical and Biophysical Researh Communications. 383(1):16-21, 2009. [PDF]

    19. Z. Wang, A. N. Tegge, and J. Cheng. Evaluating the Absolute Quality of a Single Protein Model Using Support Vector Machines and Structural Features. Proteins, vol. 75, no. 3, 638-647, 2009. [at Proteins website] (ModelEvaluator was ranked among the best model evaluation methods in CASP8) [CASP8 model quality assessment talk]

    18. J. Cheng. A Multi-Template Combination Algorithm for Protein Comparative Modeling. BMC Structural Biology.8:18, 2008. [Open Access at BMC website] MULTICOM was ranked among the best template-based and template-free structure prediction methods in CASP8. [CASP8 template-based modeling talk]; [CASP8 template_free modeling talk]

    17. J. Dai and J. Cheng. HMMEditor: A Visual Editing Tool for Profile Hidden Markov Model. BMC Genomics. 9(S1):S8, 2008. [Open Access at BMC website]

    16. J. Hecker, J. Yang, and J. Cheng. Protein Disorder Prediction at Multiple Levels of Sensitivity and Specificity. BMC Genomics. 9(S1):S9, 2008. [Open Access at BMC website] PreDisorder was ranked among the best disorder predictors in CASP8. [CASP8 disorder prediction talk]

    15. J. Cheng, A. N. Tegge, and P. Baldi. Machine Learning Methods for Protein Structure Prediction. IEEE Reviews in Biomedical Engineering. 1:41-49, 2008. [PDF]

    14. A. Randall, J. Cheng, M. Sweredoski, and P. Baldi. TMBpro: Secondary Structure, Beta-Contact, and Tertiary Structure Prediction of Transmembrane Beta-Barrel Proteins. Bioinformatics, vol. 24, pp. 513-520, 2008. [Bioinformatics website] [PDF]

    13. J. Cheng. DOMAC: An Accurate, Hybrid Protein Domain Prediction Server. Nucleic Acids Research, vol. 35, pp. w354-w356, 2007. [PDF] [Open Access at NAR website] (DOMAC was ranked among the best domain predictors in CASP8)

    12. J. Cheng and P. Baldi. Improved Residue Contact Prediction Using Support Vector Machines and a Large Feature Set. BMC Bioinformatics. 8:113, 2007. [PDF][Free access at BMC Bioinformatics website] SVMcon is one of the best contact map predictors in CASP7, CASP8 and CASP9.

    11. M. Tress, J. Cheng, P. Baldi, K. Joo, J. Lee, J.H. Seo, J. Lee, D. Baker, D. Chivian, D. Kim, A. Valencia, and I. Ezkurdia. Assessment of Predictions Submitted for the CASP7 Domain Prediction Category. Proteins: Structure, Function and Bioinformatics, vol. 68 (S8):137-151, 2007. [Our method was ranked among the best protien domain prediciton methods in CASP7. CASP7 invited contribution ] [PDF] [Paper at Proteins Website]

    10. L. Larson, M. Zhang, N. Beliakova-Bethell, V. Bilanchone, A. Lamsa, K. Nagashima, R. Najdi, K. Kosaka, V. Kovacevic, A. Lamsa, J. Cheng, P. Baldi, G.W. Hatfield, and S. Sandmeyer. Ty3 Capsid Scanning Mutations Reveal Early and Late Functions of the Amino-Terminal Domain. Journal of Virology, vol. 81, pp. 6957-6972, 2007. [PDF] [Free Access at Journal of Virology website]

    9. J. Cheng and P. Baldi. A Machine Learning Information Retrieval Approach to Protein Fold Recognition. Bioinformatics, vol. 22, no. 12, pp. 1456-1463, 2006. [PDF][Free Access at Bioinformatics website] . FOLDpro and 3Dpro are the No. 2 and No. 3 Servers for High-Accuracy Protein Structure Prediction in the Seventh Edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7). Recommended by Faculty of 1000 Biology.

    8. J. Cheng, M. Sweredoski, and P. Baldi. DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks. Data Mining and Knowledge Discovery, vol. 13, no. 1, pp. 1-10, 2006. [PDF] [DAMI advance online] . FOLDpro-DOMpro is the No. 1 Server for Automated Protein Domain Prediction in CASP7.

    7. S. A. Danziger, S. J. Swamidass, J. Zeng, L. R. Dearth, Q. Lu, J. H. Chen, J. Cheng, V. P. Hoang, H. Saigo, R. Luo, P. Baldi, R. K. Brachmann, and R. H. Lathrop. Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants. IEEE Transactions on Computational Biology and Bioinformatics, vol. 3, no. 2, pp. 114-125, 2006. [PDF]

    6. J. Cheng, A. Randall, and P. Baldi. Prediction of Protein Stability Changes for Single-Site Mutations Using Support Vector Machines. Proteins: Structure, Function, Bioinformatics, vol. 62, no. 4, pp. 1125-1132, 2006. [PDF][PDF at Proteins website]

    5. J. Cheng, H. Saigo, and P. Baldi. Large-Scale Prediction of Disulphide Bridges Using Kernel Methods, Two-Dimensional Recursive Neural Networks, and Weighted Graph Matching.Proteins: Structure, Function, Bioinformatics, vol 62, no. 3, pp. 617-629, 2006. [PDF][PDF at Proteins website]

    4. J. Cheng, M. Sweredoski, and P. Baldi. Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data, Data Mining and Knowledge Discovery, vol. 11, no. 3, pp. 213-222, 2005. [PDF] [PDF at DAMI website] DISpro is No. 2 server in disorder prediction in CASP7 (No. 5 in both human and server predictors).

    3. J. Cheng, A. Randall, M. Sweredoski, and P. Baldi, SCRATCH: a Protein Structure and Structural Feature Prediction Server. Nucleic Acids Research, vol. 33 (web server issue), w72-76, 2005. [PDF][PDF at NAR website]

    2. J. Cheng, L. Scharenbroich, P. Baldi, and E. Mjolsness. Sigmoid: Towards a Generative, Scalable, Software Infrastructure for Pathway Bioinformatics and Systems Biology. IEEE Intelligent Systems, vol. 20, no. 3, pp. 68-75, 2005.[PDF][PDF at IEEE website]

    1. J. Cheng and P. Baldi. Three-Stage Prediction of Protein Beta-Sheets by Neural Networks, Alignments, and Graph Algorithms. Bioinformatics, vol. 21(Suppl 1), pp. i75-84, 2005. (This is the journal version of Conference paper 2) [PDF][Free Access at Bioinformatics website][ISMB Talk]. BETApro is one of the Best Residue Contact Predictors in CASP7 and CASP8.

Conference Papers and Tutorials

    23. Chen, X., & Cheng, J. (2021). DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network. International Conference on Intelligent Biology and Medicine (ICIBM 2021).

    22. M. Highsmith, J. Cheng. An Introduction to Computational Approaches for 3D Genomic Modeling. ACM Conference on Bioinformatics and Computational Biology (ACM-BCB), 2021.

    21. A. Al-Azzawi, A. Ouadou, Y. Duan, and J. Cheng*. Auto3DCryoMap: An Automated Particle Alignment Approach for 3D cryo-EM Density Map Reconstruction. International Conference on Intelligent Biology and Medicine (ICIBM), Virtual, 2020.

    20. X. Chen, N. Akhter, Z. Guo, T. Wu, J. Hou, Shehu, A., & J. Cheng*. Deep Ranking in Template-free Protein Structure Prediction. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 1-10), Virtual, 2020.

    19. M. Alfarhood, J. Cheng*. Collaborative attentive autoencoder for scientific article recommendation. 18th IEEE International Conference on Machine Learning and Applications (ICMLA), Boca Raton, Florida, USA, 2019.

    18. A. Al-Azzawi, A. Quadou, J. Cheng. Super Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM. International Conference on Intelligent Biology and Medicine (ICIBM), Columbus, OH, 2019.

    17 M. Alfarhood, J. Cheng. DeepHCF: A Deep Learning Based Hybrid Collaborative Filtering Approach for Recommendation Systems. The 17th International Conference on Machine Learning and Application, Orlando, FL, USA, 2018.

    16 R. Cao, J. Hou, T. Jo, J. Cheng. Evaluation of protein structural models using random forests. 9th International Conference on Bioinformatics and Computational Biology. Honolulu, HI, USA, 2017.

    15. R. Xie, A. Quitadamo, J. Cheng, X. Shi. A predictive model of gene expression using a deep learning framework. IEEE Internaltional Conference on Bioinformatics and Biomedicine (BIBM). Shenzhen, China, 2016. [PDF]

    14. X. Zhou, J. Cheng. DNpro: a deep learning network approach to predicting protein stability changes induced by single-site mutations . The International Conference on Bioinformatics and Biochemical Engineering, Tokyo, Japan, 2016. [PDF]

    13. R. Cao, D. Bhattacharya, B. Adhikari, J. Li, J. Cheng. Large-Scale Model Quality Asessment for Improving Protein Tertiary Structure Prediction. The 23rd International Conference on Intelligent Systems for Molecular Biology (ISMB), Dublin, Ireland, accepted, 2015. (the same as Journal Article 70). [PDF]

    12. Q. Qi, J. Li, J. Cheng. Reconstruction of Metabolic Pathways by Combining Probabilistic Graphical Model-based and Knowledge-based Methods. The Great Lake Bioinformatics Conference, Cincinatti, OH, 2014. [PDF].

    11. D. Bhattacharya, J. Cheng. Protein structure refinement by iterative fragment exchange. ACM Conference on Bioinformatics and Computational Biology (ACM BCB), Washington DC, 2013. [PDF]

    10. B. Adhikari, D. Bhattacharya, X. Deng, J. Li, J. Cheng. A Contact-Assisted Approach to Protein Structure Prediction and Its Assessments in CASP10. The Workshop on Artificial Intelligence and Robotics Methods in Computational Biology of the 27th AAAI Conference, Bellevue, WA, USA, 2013. [PDF].

    9. J. Chen, J. Cheng, A.K. Dunker. Intrinsically Disordered Proteins - A Tutorial. Pacific Symposium on Biocomputing (PSB), Hawaii, 2012. [PDF] [Tutorial Slides].

    8. J. Chen, J. Cheng, A.K. Dunker. Intrinsically Disordered Proteins: Analysis, Prediction, Simulation, and Biology. Pacific Symposium on Biocomputing (PSB), Hawaii, 2012. [at PSB web site].

    7. T. Joshi, K. Patil, M.R. Fitzpatrick, L.D. Franklin, Q. Yao, Z. Wang, M. Libault, L. Brechenmacher, B. Valiyodan, X. Wu, J. Cheng, G. Stacey, H. Nguyen, and D. Xu. Soybean Knowledge Base (SoyKB): A Web Resource for Soybean Translational Genomics. The 10th Asia Pacific Bioinformatics Conference (APBC), Melbourne, Australia, 2012. [PDF] (same as Journal article 36).

    6. J. Cheng, J. Eickholt, Z. Wang, and X. Deng. Recursive Protein Modeling: a Divide and Conquer Strategy for Protein Structure Prediction and its Case Study in CASP9. Computational Structural Bioinformatics Workshop, Atlanta, Georgia, 2011. [PDF], [Presentation Slides].

    5. G. Lin, Z. Wang, D. Xu, and J. Cheng. Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington D.C., 2009. [PDF]

    4. J. Dai and J. Cheng. HMMVE: A Visual Editor for Profile Hidden Markov Model. International Conference on Bioinformatics and Computational Biology (BIOCOMP), Las Vegas, 2007. (short version of journal article 17)

    3. J. Cheng, Z. Wang, and G. Pollastri. A Neural Network Approach to Ordinal Regression. International Joint Conference on Neural Networks (IJCNN), Hongkong, 2008. [PDF]

    2. J. Cheng and P. Baldi. Three-Stage Prediction of Protein Beta-Sheets by Neural Networks, Alignments, and Graph Algorithms. Proceedings of the 2005 Conference on Intelligent Systems for Molecular Biology (ISMB 2005). Bioinformatics, vol. 21(Suppl 1), pp. i75-84, 2005. [PDF][Free Access at Bioinformatics website][ISMB Talk].

    1. P. Baldi, J. Cheng, and A. Vullo. Large-Scale Prediction of Disulphide Bond Connectivity . Advances in Neural Information Processing Systems 17 (NIPS 2004), L. Saul,Y. Weiss, and L. Bottou editors, MIT press, pp.97-104, Cambridge, MA, 2004. [PDF][Free Access at NIPS website]

Book Chapters

    8. Hou, J., Wu, T., Guo, Z., Quadir, F., Cheng, J. (2020). The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction. In Protein Structure Prediction (pp. 13-26). Humana, New York, NY

    7. Shi X, Chen C, Yang H, Hou J, Ji T, Cheng J, Veitia RA, Birchler JA. The Gene Balance Hypothesis: Epigenetics and Dosage Effects in Plants. In Plant Epigenetics and Epigenomics. pp. 161-171), Humana, New York, NY, 2020.

    6. B. Adhikari, D. Bhattacharya, R. Cao, J. Cheng. Assessing Predicted Contacts for Building Protein Three-Dimensional Models. Methods Mol Biology. 1484:115-126, 2017.

    5. B. Adhikari, J. Cheng. Protein Residue Contacts and Prediction Methods. Methods in Mol Biol. 1415:463-76, 2016.

    4. J. Li, D. Bhattacharya, R. Cao, B. Adhikari, X. Deng, J. Eickholt, J. Cheng. The MULTICOM Protein Tertiary Structure Prediction System. in press.

    3. X. Deng, J. Cheng. MSACompro: Improving Multiple Protein Sequence Alignment by Predicted Structural Features. in press.

    2. B. Compani, T. Su, I. Chang, J. Cheng, K. Shah, T. Whisenant, Y. Dou, A. Bergmann, R. Cheong, L. Bardwell, A. Levchenko, P. Baldi, and E. Mjolsness. A Scalable and Integrative System for Pathway Bioinformatics and Systems Biology. Adv Exp Med Biol. 680:523-534, 2010. [at PubMed].

    1. A. N. Tegge, Z. Wang, and J. Cheng. Integrative Protein Fold Recognition by Alignments and Machine Learning. in Protein Structure Prediction: Method and Algorithms (editors: H. Rangwala and G. Karypis), Wiley, 2009.

Theses

    14. R. Cao. Genome Data Analysis, Protein Structure and Function Predictioin by Machine Learning Techniques. PhD Dissertation. University of Missouri - Columbia, 2016.

    13. D. Bhattacharya. Probabilistic Graphical Models for Protein Structure Prediction. PhD Dissertation. University of Missouri - Columbia, 2016.

    12. Y. Tang. Using Machine Learing Approach to Predict Enzyme Family Classes by Fusing AM-PSE-ACC and PSE-PSSM. Master's Thesis. University of Missouri - Columbia, 2016.

    11. Y. Zhang. EM Algorithm for Reconstructing 3D Structures of Human Chromosomes from Chromosomal Contact Data. Master's Thesis. University of Missouri - Columbia, 2016.

    10. J. Li. Computational Methods for Protein Structure Prediction and Next-Generation Sequencing Data Analysis. PhD Dissertation. University of Missouri - Columbia, 2016.

    9. R. Xie. Gene Expression Prediction Based on Deep Learning. Master’s Thesis, University of Missouri – Columbia, 2015.

    8. D. Bhattacharya. Computational Optimization Algorithms For Protein Structure Refinement. Master's Thesis. University of Missouri - Columbia. [PDF].

    7. S. Ahmed. Iterative Reconstruction of Three-Dimensional Model of Human Genome from Chromosomal Contact Data. Master's Thesis. University of Missouri, Columbia, 2014.

    6. X. Deng. Improved Computational Methods of Protein Sequence Alignment, Model Selection and Tertiary Structure Prediction. PhD Dissertation. University of Missouri, Columbia, 2013.

    5. J. Eickholt. Predicting Protein Residue-Residue Contacts and Disorder. PhD Dissertation. University of Missouri, Columbia, 2013.

    4. Z. Wang. Revealing the Conformation and Properties of Human Genome, Protein Molecules, and Protein Domain Co-Occurrence Network. PhD Dissertation. University of Missouri, Columbia, 2012.

    3. M. Ahmad. MUPRIMER: A Tool for Finding Allele Specific PCR-Primers for Homologous Gene Sequences . Master Thesis. University of Missouri, Columbia, 2009.

    2. J. Cheng. Machine Learning Algorithms for Protein Structure Prediction. PhD Dissertation. University of California Irvine, Irvine, CA, 2006. [PDF]

    1. J. Cheng. A Comparative Study of the Similarity Measures of Text Categorization. Master Thesis. Utah State University, Logan, UT, 2001.