216. Hegde, A., Hajikhani, M., Snyder, J., Cheng, J., Lin, M. Leveraging SERS and Transformer Models for Simultaneous Detection of Multiple Pesticides in Fresh Produce. ACS Applied Materials & Interfaces, accepted.

215. Boadu, F., Cheng, J. Improving protein function prediction by learning and integrating representations of protein sequences and function labels. Bioinformatics Advances, accepted.

214. Morehead, A., N. Giri, J. Liu, and J. Cheng. Geometry-complete diffusion for 3D molecule generation and optimization. Communications Chemistry, accepted.

213. Giri, N., Cheng, J. De Novo Atomic Protein Structure Modeling for CryoEM Density Maps Using 3D Transformer and HMM. Nature Communications, accepted.

212. Boadu, F., Lee, A., Cheng, J. Deep Learning Methods for Protein Function Prediction. Proteomics, accepted.

211. Gyawali, R., Dhakal, A., Wang, L., Cheng, J. CryoSegNet: Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net. Briefings in Bioinformatics, accepted.

210. Lawson, C., Kryshtafovych, A., Pintilie, G., Burley, S., Cerny, J., Chen, V., ... & Chiu, W. (2024). Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge. Nature Methods, accepted.

209. Wang, Y., Cheng, J. HiCDiff: single-cell Hi-C data denoising with diffusion models. Briefings in Bioinformatics, accepted.

208. Chen, X., Liu, J., Park, N., Cheng, J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules, accepted.

207. Giri, N, Wang, L, Cheng, J. (2024). Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures. Scientific Data, accepted.

206. Joshi, K., Katam, T., Hegde, A., Cheng, J., Prather, R. S., Whitworth, K., ... & Rachagani, S. (2024). Pigs: Large Animal Preclinical Cancer Models. World Journal of Oncology, 15(2), 149

205. Korasick, D. A., Buckley, D. P., Palpacelli, A., Cursio, I., Cesaroni, E., Cheng, J., & Tanner, J. J. (2024). Biochemical, structural, and computational analyses of two new clinically identified missense mutations of ALDH7A1. Chemico-Biological Interactions, 110993

204. Hajikhani, M., Hegde, A., Snyder, J., Cheng, J., & Lin, M. (2024). Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach. Journal of Hazardous Materials, 134208.

203. Dhakal, A., Gyawali, R., Wang, L., Cheng, J. (2024) CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs. Bioinofrmatics, accepted.

202. Morehead, A., Cheng, J. (2024). Geometry-Complete Perceptron Networks for 3D Molecular Graphs. Bioinformatics, accepted.

201. Morehead, A., Liu, J., Cheng, J. (2024) Protein Structure Accuracy Estimation using Geometry-Complete Perceptron Networks. Protein Science, accepted.

200. Jian Liu, Zhiye Guo, Tianqi Wu, Raj S. Roy, Farhan Quadir, Chen Chen, Jianlin Cheng. Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15. Communications Biology, accepted.

199. Lensink et al. (2023). Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment. Proteins, accepted.

198. Guo, Z., Liu, J., Wang, Y., Chen, M., Wang, D., Xu, D., Cheng, J. (2023). Diffusion models in bioinformatics and computational biology. Nature Reviews Bioengineering, accepted. [Online Article] and [PDF]

197. Liu, J., Guo, Z., Wu, T., Roy, R.S., Chen, C., Cheng, J. (2023) Improving AlphaFold2-based Protein Tertiary Structure Prediction with MULTICOM in CASP15. Communications Chemistry, accepted.

196. Davidson, R.B., Coletti, M., Gao, M., Piatkowski, B., Sreedasyam, A., Quadir, F., Weston, D.J., Schmutz, J., Cheng, J., Skolnick, J., Parks, J.M., and Sedova, A. (2023) Predicted structural proteome of Sphagnum divinum and proteome-scale annotation. Bioinformatics, accepted.

195. McKay, C.E., Cheng, J., Tanner, J.J. (2023) Crystal structure of domain of unknown function 507 (DUF507) reveals a new protein fold. Scientific Reports, accepted.

194. Knoshaug, E., et al. (2023) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis. Plant Direct, accepted.

193. Morehead, A., Chen, C., Sedova, A., Cheng, J. (2023) DIPS-Plus: The enhanced database of interacting protein structures for interface prediction. Scientific Data, accepted.

192. Wang, Y., Guo, Z., Cheng, J. (2023). Single-cell Hi-C data Enhancement with Deep Residual and Generative Adversarial Networks. Bioinformatics, accepted.

191. Roy, R., Liu, J., Giri, N., Guo, Z., Cheng, J. (2023). Combining Pairwise Structural Similarity and Deep Learning Interface Contact Prediction to Estimate Protein Complex Model Accuracy in CASP15. Proteins, accepted.

190. Dhakal, A., Gyawali, R., Wang, L., & Cheng, J. (2023). A Large Expert-Curated Cryo-EM Image Dataset for Machine Learning Protein Particle Picking. Scientific Data, accepted.

189. Del Conte, A., Bouhraoua, A., Mehdiabadi, M., et al. (2023). CAID Prediction Portal: A Comprehensive Service for Predicting Intrinsic Disorder and Binding Regions in Proteins. Nucleic Acids Research, accepted.

188. Xing, Z., Li, Y., Cortes-Gomez, E., Jiang, X., Gao, S., Pao, A., ... & Yu, Y. E. (2023). Dissection of a Down syndrome-associated trisomy to separate the gene dosage-dependent and-independent effects of an extra chromosome. Human Molecular Genetics, ddad056

187. Wu, T., Guo, Z., Cheng, J. (2023) Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer. Bioinformatics, accepted.

186. Boadu, F., Cao, H., Cheng, J. (2023) Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function. Bioinformatics, accepted.

185. Chen, X., Morehead, A., Liu, J., Cheng, J. (2023) A Gated Graph Transformer for Protein Complex Structure Quality Assessment and its Performance in CASP15. Bioinformatics, accepted.

184. Chen, C., Chen, X., Morehead, A., Wu, T., Cheng, J. (2023) 3D-equivariant graph neural networks for protein model quality assessment. Bioinformatics, accepted.

183 Giri, N., Cheng, J. (2023) Improving protein-ligand interaction modeling with cryo-EM data, templates, and deep learning in 2021 ligand model challenge. Biomolecules, accepted.

182. Giri, N., Roy, R.S., Cheng, J. (2023) Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions. Current Opinion in Structural Biology, accepted.

181. Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2022). Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review. Proceedings of the IEEE.

180. Mattoon, E. M., McHargue, W., Bailey, C. E., Zhang, N., Chen, C., Eckhardt, J., ... & Zhang, R. (2022). High‐throughput Identification of Novel Heat Tolerance Genes via Genome‐wide Pooled Mutant Screens in the Model Green Alga Chlamydomonas reinhardtii. Plant, Cell & Environment.

179. Guo, Z., Liu, J., Skolnick, J., Cheng, J. Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks. Nature Communications, accepted.

178. Yang, H., Shi, X., Chen, C., Hou, J., Ji, T., Cheng, J., Birchler, J. Genomic imbalance modulates transposable element expression in maize. Plant Communications. Accepted.

177. Li, Y., Boadu, F., Highsmith, M. R., Hagen, D. E., Cheng, J., & Rivera, R. M. (2022). Allele-specific aberration of imprinted domain chromosome architecture associates with large offspring syndrome. iScience, 25(5), 104269.

176. Hailong Song, Chen Chen, Brian Kelley, Alexandra Tomasevich, Hyoungjoo Lee, Jean-Pierre Dolle, Jianlin Cheng, Benjamin Garcia, David F. Meaney, Douglas H. Smith. (2022). Traumatic brain injury recapitulates developmental changes of axons. Progress in Neurobiology, in press.

175. Mahmud, S., Guo, Z., Quadir, F., Liu, J., & Cheng, J. (2022). Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps. BMC Bioinformatics, in press.

174. Shi, X., Yang, H., Chen, C., Hou, J., Ji, T., Cheng, J., Birchler, J. Dosage-sensitive miRNAs trigger modulation of gene expression during genomic imbalance in maize. Nature Communication, accepted, 2022

173. Zhang, N. et al. (2022). Systems-wide Analysis Revealed Shared and Unique Responses to Moderate 2 and Acute High Temperatures in the Green Alga Chlamydomonas reinhardtii. Communications Biology, accepted.

172. Cheng, X., Cheng, J. (2022). DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network. BMC Bioinformatics, accepted.

171. Roy, R. S., Quadir, F., Soltanikazemi, E., & Cheng, J. (2022). A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers. Bioinformatics, accepted.

170. Shi, X., Yang, H., Chen, C., Hou, J., Ji, T., Cheng, J., & Birchler, J. A. (2022). Effect of aneuploidy of a nonessential chromosome on gene expression in maize. The Plant Journal.

169. Dhakal, A., McKay, C., Tanner, J.J., Cheng, J. (2021). Artificial Intelligence in the Prediction of Protein-Ligand Interactions: Recent Advances and Future Directions. Briefings in Bioinformatics, accepted.

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

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. (Ranked one of top 10 most cited papers in Proteins journal in 2022-2023).

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]

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] .

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.

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]