Table of Contents

• Integrative sparse Bayesian analysis of high-dimensional multi-platform genomic data in glioblastomaAnindya Bhadra, Veerabhadran Baladandayuthapani. 1-4 [doi]
• Integrative analysis of multi-modal correlated imaging-genomics data in glioblastomaRolando J. Olivares, Arvind Rao, Ganesh Rao, Jeffrey S. Morris, Veerabhadran Baladandayuthapani. 5-8 [doi]
• A structure-based approach to predicting in vitro transcription factor-DNA interactionZhen Gao, Jianhua Ruan. 9 [doi]
• On the modeling of heterogeneity in cancer tissueAnwoy Kumar Mohanty, Aniruddha Datta, Jijayanagaram Venkatraj. 10 [doi]
• Identifying RNAseq-based coding-noncoding co-expression interactions in breast cancerNilanjana Banerjee, Sonia Chothani, Lyndsay Harris, Nevenka Dimitrova. 11-14 [doi]
• An approach for assessing RNA-seq quantification algorithms in replication studiesPo-Yen Wu, John H. Phan, May D. Wang. 15-18 [doi]
• ROBNCA: Robust Network Component Analysis for recovering transcription factor activitiesAmina Noor, Aitzaz Ahmad, Erchin Serpedin, Mohamed N. Nounou, Hazem N. Nounou. 19-22 [doi]
• Characterization of conditions for competing endogenous RNA regulation in GBMYu-Chiao Chiu, Eric Y. Chuang, Tzu-Hung Hsiao, Yidong Chen. 23 [doi]
• NetceRNA: An algorithm for construction of phenotype-specific regulation networks via competing endogenous RNAsMario Flores, Yufei Huang, Yidong Chen. 24-27 [doi]
• A generic model of transcriptional regulatory networks: Application to plants under abiotic stressAlain B. Tchagang, Sieu Phan, Fazel Famili, Youlian Pan, Adrian Cutler, Jitao Zou. 28-31 [doi]
• Compromised intervention policies for phenotype alterationMohammadmahdi R. Yousefi. 32-35 [doi]
• Identifying cancer biomarkers through a network regularized Cox modelYing-Wooi Wan, John Nagorski, Genevera I. Allen, Zhaohui Li, Zhandong Liu. 36-39 [doi]
• SeqBBS: A change-point model based algorithm and R package for searching CNV regions via the ratio of sequencing readsHua Li, Jim Vallandingham, Jie Chen. 40-43 [doi]
• Exploiting the cancer and diabetes metabolic connection for therapeutic purposesOsama A. Arshad, Priyadharshini S. Venkatasubramami, Aniruddha Datta, Jijayanagaram Venkatraj. 44 [doi]
• Deciphering chemically-induced reversible neurotoxicity by reconstructing perturbed pathways from time series microarray gene expression dataYi Yang, Si Li, Andrew S. Maxwell, Natalie D. Barker, Yan Peng, Ying Li, Haoni Li, Xi Wu, Peng Li, Tao Huang, Chenhua Zhang, Nan Wang, Edward J. Perkins, Chaoyang Zhang, Ping Gong. 45-48 [doi]
• Quantifying the inference power of a drug screen for predictive analysisNoah Berlow, Saad Haider, Ranadip Pal, Charles Keller. 49-52 [doi]
• A multivariate random forest based framework for drug sensitivity predictionQian Wan, Ranadip Pal. 53 [doi]
• Drug effect study on proliferation and survival pathways on cell line-based platform: A stochastic hybrid systems approachXiangfang Li, Lijun Qian, Michael L. Bittner, Edward R. Dougherty. 54-57 [doi]
• Prediction of drug-target interactions using popular Collaborative Filtering methodsArezou Koohi. 58-61 [doi]
• Parameter distribution estimation in first order ODETianyi Yang, Nguyen Nguyen, Yufang Jin, Merry Lindsey. 62-65 [doi]
• Optimal Bayesian MMSE estimation of the coefficient of determination for discrete predictionTing Chen, Ulisses Braga-Neto. 66-69 [doi]
• On the optimality of k-means clusteringLori A. Dalton. 70-71 [doi]
• Effect of separate sampling on classification and the minimax criterionMohammad Shahrokh Esfahani, Edward R. Dougherty. 72-73 [doi]
• Inference of genetic regulatory networks with unknown covariance structureBelhassen Bayar, Nidhal Bouaynaya, Roman Shterenberg. 74-77 [doi]
• Active learning for Bayesian network models of biological networks using structure priorsAntti Larjo, Harri Lähdesmaki. 78-81 [doi]
• Phenotypically constrained Boolean network inference with prescribed steady statesXiaoning Qian, Edward R. Dougherty. 82-83 [doi]
• Modeling hypoxia stress response pathwaysRajani Varghese, Sriram Sridharan, Aniruddha Datta, Jijayanagaram Venkatraj. 84 [doi]
• An HMM-based Exome Peak-finding package for RNA epigenome sequencing dataXiaodong Cui, Jia Meng, Manjeet K. Rao, Yidong Chen, Ufei Huang. 85 [doi]
• Supersonic MiBBilal Wajid, Ali Riza Ekti, Amina Noor, Erchin Serpedin, Muhammad Naeem Ayyaz, Hazem N. Nounou, Mohamed N. Nounou. 86-87 [doi]
• Designing experiments for optimal reduction of uncertainty in gene regulatory networksRoozbeh Dehghannasiri, Byung-Jun Yoon, Edward R. Dougherty. 88-89 [doi]
• Optimal neyman-pearson classification under Bayesian uncertainty modelsLori A. Dalton. 90-91 [doi]
• DBComposer: An R package for integrative analysis and management of gene expression microarray dataLingjia Kong, Kaisa-Leena Aho, Kirsi Granberg, Christophe Roos, Reija Autio. 92-93 [doi]
• Boolean model to experimental validation: A preliminary attemptS. Sridharan, A. Datta, J. Venkatraj. 94-95 [doi]
• Bayesian multivariate Poisson model for RNA-seq classificationJason M. Knight, Ivan Ivanov, Edward R. Dougherty. 96-97 [doi]
• Effect of mixing probabilities on the bias of cross-validation under separate samplingAmin Zollanvari, Ulisses Braga-Neto, Edward R. Dougherty. 98-99 [doi]
• Improved branch-and-bound algorithm for U-curve optimizationEsmaeil Atashpaz-Gargari, Ulisses Braga-Neto, Edward R. Dougherty. 100-101 [doi]
• Semi-supervised classification using sparse representation for cancer recurrence predictionYan Cui, Xiaodong Cai, Zhong Jin. 102-105 [doi]