Publications

Book chapters

Selected journal and conference papers (peer reviewed)

For an extended list click here (PubMed list)
 

  1. A.J. Sedgewick, I. Shi, R.M. Donovan, P.V. Benos, “Learning mixed graphical models with separate sparsity parameters and stability-based model selection”BMC Bioinformatics, (2016), 17 Suppl 5:175.  Abstract (PubMed) and pdf   PMID:27294886.  PMCID:PMC4905606
  2. A. Bahreini, K. Levine, L. Santana-Santos, P.V. Benos, P. Wang, C. Andersen, S. Oesterreich, A.V. Lee, “Non-coding single nucleotide variants affecting estrogen receptor binding and activity”Genome Med, (2016), 8:128. Abstract (PubMed) and pdf   PMID:27964748   PMCID:PMC5154163
  3. N. Olave, C.V. Lal, B. Halloran, K. Pandit, A.C. Cuna, O.M. Faye-Petersen, D.R. Kelly, T. Nicola, P.V. Benos, N. Kaminski, N. Ambalavanan, “Regulation of alveolar septation by microRNA-489”Am J Physiol Lung Cell Mol Physiol, (2016), 10:L476-L487.  Abstract (PubMed) and pdf   PMID:26719145 [PubMed]  PMCID:PMC4773841   Editor’s pick
  4. G.F. Cooper, I. Bahar, M.J. Becich, P.V. Benos, J. Berg, J.U. Espino, C. Glymour, R.C. Jacobson, M. Kienholz, A.V. Lee, X. Lu, R.B. Scheines, Center for Causal Discovery team, “The Center for Causal Discovery of biomedical knowledge from Big Data”J Am Med Inform Assoc, (2015), 22:1132-1136.  Abstract (PubMed) and pdf.   PMID: 26138794
  5. U.R. Chandran, S. Luthra, L. Santana-Santos, P. Mao, S.H. Kim, M. Minata, J. Li, P.V. Benos, M. DeWang, B. Hu, S.Y. Cheng, I.B. Nakano, R.W. Sobol, “Gene expression profiling distinguishes proneural glioma cells from mesenchymal glioma cells”Genom Data (2015), 5:333-336.  Abstract (PubMed) and pdf.   PMID: 26251826 [PubMed]  PMCID: PMC4523279
  6. L.C. Villaruz(*), G.T. Huang(*), M. Romkes, J.M. Kirkwood, S.C. Buch, T. Nukui, K.T. Flaherty, S.J. Lee, M.A. Wilson, K.L. Nathanson, P.V. Benos, H.A. Tawbi, “MicroRNA expression profiling predicts clinical outcome of carboplatin/paclitaxel-based therapy in metastatic melanoma treated on the ECOG-ACRIN trial E2603” Clinical Epigenetics, (2015), 7:58. Abstract (PubMed) and pdf   PMID: 26052356 [PubMed]   PMCID:PMC4457092
    (*) equal contribution
  7. G.T. Huang, I. Tsamardinos, V. Raghu, N. Kaminski, P.V. Benos, “T-ReCS: Stable Selection of Dynamically Formed Groups of Features with Application to Prediction of Clinical Outcomes” Pac Symp Biocomput, (2015), 20:431-442. Abstract (PubMed) and pdf   PMID:25592602 [PubMed]   PMCID:PMC4299881
    This article presents a new method for performing feature selection at the cluster level. The method “seeds” the clustered feature selection using a standard feature selection method and then dynamically forms the optimal predictive clusters by ascending a hierarchical tree. The optimal number and size of clusters is determined by two probabilistic criteria. T-ReCS doe snot only improve the stability of the selected features but it is also able to detect more biologically relevant features that are predictive of a target variable, including variables describing survival data.
  8. R.S. Edinger, C. Coronnello, A.J. Bodnar, W.A. Laframboise, P.V. Benos, J. Ho, J.P. Johnson, M.B. Butterworth, “Aldosterone Regulates MicroRNAs in the Cortical Collecting Duct to Alter Sodium Transport” J Am Soc Nephrol, (2014) 25:2445-2457. Abstract (PubMed) and pdf   PMID: 24744440
  9. T.Y. Lu, B. Lin, Y. Li, A. Arora, L. Han, C. Cui, C. Coronnello, Y. Sheng, P.V. Benos, L. Yang, “Overexpression of microRNA-1 promotes cardiomyocyte commitment from human cardiovascular progenitors via suppressing WNT and FGF signaling pathways” J Mol Cell Cardiol, (2013) 63: 146-154. Abstract (PubMed) and pdf    PMID: 23939491
  10. H.U. Osmanbeyoglu, K.N. Lu, S. Oesterreich, R.S. Day, P.V. Benos, C. Coronnello, X. Lu“Estrogen represses gene expression through reconfiguring chromatin structures” Nucleic Acids Res, (2013) 41: 8061-8071. Abstract (PubMed) and pdf    PMID: 23821662
  11. P. Mao, K. Joshi, J. Li, L. Santana-Santos, S. Luthra, U.R. Chandran, P.V. Benos, L. Smith, M. DeWang, B. Hu, S-Y. Cheng, R.W. Sobol, I. Nakano “Mesenchymal glioma stem cells are maintained by activated glycolytic metabolism involving aldehyde dehydrogenase 1A3” Proc Natl Acad Sci USA, (2013) 110: 8644-8649. Abstract (PubMed) and pdf    PMID: 23650391
  12. (Web tool) C. Coronnello, P.V. Benos, “ComiR: Combinatorial microRNA target prediction tool” Nucl Acids Res, (2013) 41: W159-W164. Abstract (PubMed) and pdf    PMID: 23703208
  13. S. Jain, M.G. Kapetanaki, N. Raghavachari, K. Woodhouse, G. Yu, S. Barge, C. Coronnello,P.V. Benos, G.J. Kato, N. Kaminski, M.T. Gladwin, “Expression of regulatory platelet miRNAs in patients with sickle cell disease” PLoS One, (2013) 8:e60932. Abstract (PubMed) and pdf   PMID: 23593351
  14. G.T. Huang, K. Cunningham, P.V. Benos, Chakra Chennubhotla, “Spectral clustering strategies for heterogeneous disease expression data” Pac Symp Biocomput, (2013), 212-223. Abstract (PubMed) and pdf    PMID: 23424126
    This article presents novel method for clustering heterogeneous human disease dataset. ReKS is a recursive spectral clustering method and the optimal number of clusters is calculated from the data.
  15. C. Coronnello, R. Hartmaier, A. Arora, L. Huleihel, K.V. Pandit, A.S. Bais, M. Butterworth, N. Kaminski, G.D. Stormo, S. Oesterreich, P.V. Benos, “Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density” PLoS Comput Biol, (2012), 8:e1002830. Abstract (PubMed) and pdf    PMID: 23284279
    This article presents a new method for modeling microRNA:mRNA interactions. The novelty of the method is two-fold. First it uses microRNA expression combined with the Fermi-Dirac thermodynamic model to better estimate the potential of a target sequence. Second, it combines the target potential of multiple microRNAs to give a composite score on whether a set of microRNAs target a given mRNA. We also showed that ComiR scoring scheme is useful to identify SNPs that affect microRNA targeting in human disease.
  16. J. Milosevic, K. Pandit, M. Magister, E. Rabinovich, D.C. Ellwanger, G. Yu, L.J. Vuga, B. Weksler, P.V. Benos, K.F. Gibson, M. McMillan, M. Kahn, N. Kaminski, “Profibrotic role of miR-154 in pulmonary fibrosis” Am J Respir Cell Mol Biol, (2012), 47:879-887. Abstract (PubMed) and pdf    PMID: 23043088
  17. S. Kadri, V. Hinman, P.V. Benos, “RNA deep sequencing reveals differential microRNA expression during development of sea urchin and sea star” PLoS One, (2011) 6:e29217.Abstract (PubMed) and pdf    PMID: 22216218
    This paper presents the first detailed analysis of miRNA genes expressed during embryogenesis in two echinoderm species :sea urchin and sea star.
  18. (Web tool) G.T. Huang, C. Athanassiou, P.V. Benos, “mirConnX: Condition-specific mRNA-microRNA network integrator” Nucleic Acids Res, (2011) 39 (Suppl 2):W416-W423. Abstract(PubMed) and pdf    PMID: 21558324
    A new web server that combines prior (static) information (sequence and literature-based) with (dynamic) relevance network reconstruction to identify high confidence TF-to-gene and miRNA-to-gene interactions.
  19. A.S. Bais, N. Kaminski, P.V. Benos, “Finding subtypes of transcription factor motif pairs with distinct regulatory roles” Nucleic Acids Res, (2011) 39:e76. Abstract (PubMed) and pdf   PMID: 21486752
    This paper presents the DiSCO algorithm, which implements a new idea to tackle a previously overlooked problem: to identify variants of transcription factor (TF) in vivo binding sites when its specificity is altered by co-factors.
  20. Y. Zhang, D. Handley, A. Bais, T. Kaplan, H. Yu, T. Richards, K. Pandit, P.V. Benos, N. Friedman, O. Eickelberg, N. Kaminski, “High throughput determination of TGFβ1/SMAD3 targets in A549 lung epithelial cells” PLoS ONE, (2011) 6:e20319. Abstract (PubMed) andpdf    PMID: 21625455
  21. K.V. Pandit, D. Corcoran, H. Yousef, M. Yarlagadda, A. Tzouvelekis, K.F. Gibson, K. Konishi, S.A. Yousem, M. Singh, D. Handley, T. Richards, M. Selman, S.C. Watkins, A. Pardo, A. Ben-Yehudah, D. Bouros, O. Eickelberg, P. Ray, P.V. Benos, N. Kaminski, “Inhibition and role of let-7d in idiopathic pulmonary fibrosis.” Am J Resp Crit Care Med, (2010) 182:220-229.Abstract (PubMed) and pdf    PMID: 20395557
    In this article, for the first time, we show that a growth factor (TGF-β) directly controls the expression of a miRNA gene (let-7d), which plays a crucial role in epithylial to mesenchymal transition (EMT) and idiopathic pulmonary fibrosis (IPF).
  22. A.B. Tchagang, K.V. Bui, T. McGinnis, P.V. Benos, “Extracting biologically significant patterns from short time series gene expression data.” BMC Bioinformatics, (2009) 10:255. Abstract(PubMed) and pdf    PMID: 19695084   
  23. D.L. Corcoran1, K.V. Pandit1, B. Gordon, A. Bhattacharjee, N. Kaminski, P.V. Benos“Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data.” PLoS ONE, (2009) 4:e5279. Abstract (PubMed) and pdf    PMID: 19390574
    1 equal contribution,    corresponding authorsRNA Pol II ChIP-chip data on a tiling array surrounding all known microRNA genes are analyzed to identify the characteristics of microRNA promoters. After comparing various SVM-based strategies, we used the best-performing SVM model to predict microRNA Pol II promoters from the ChIP-chip data. We found evidence that microRNA transcripts can be very long (up to 40 kb long) and that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters.
  24. R. Brower-Sinning, D.M. Carter, C.J. Crevar, E. Ghedin, T.M. Ross, P.V. Benos “The role of RNA folding free energy in the evolution of the polymerase genes of the influenza A virus.”Genome Biol, (2009) 10:R18. Abstract (PubMed) and pdf    PMID: 19216739
    In this article, we show for the first time that the RNA free folding energy (FFE) of the polymerase genes of the influenza A virus is under evolutionary pressure and may impose a barrier for the avian-to-human transmition of the virus.
  25. S. Kadri, V. Hinman, P.V. Benos “HHMMiR: Efficient de novo prediction of microRNAs using hierarchical hidden Markov models.” BMC Bioinformatics (Proc APBC), (2009) 10(Suppl 1):S35. Abstract (PubMed) and pdf    PMID: 19208136
    This article presents a hierarchical hidden Markov model that naturally represents the structure of a microRNA stemloop and can efficiently classify the microRNA-containing stemloops in the genome. HHMMiR method does not consider evolutionary conservation for the classification and works efficiently even for distantly related species.
  26. H. Feng, J.L. Taylor, P.V. Benos, R. Newton, K. Waddell, S.B. Lucas, Y. Chang, P.S. Moore “Human transcriptome subtraction by using short sequence tags to search for tumor viruses in conjunctival carcinoma.” J Virol, (2007) 81:11332-11340. Abstract (PubMed) and pdf   PMID: 17686852
  27. Y. Lu, S. Mahony, P.V. Benos, R. Rosenfeld, I. Simon, L.L. Breeden, Z. Bar-Joseph “Combined analysis reveals a core set of cycling genes.” Genome Biol, (2007) 8:R146.    PMID: 17650318 Abstract (PubMed) and pdf
  28. S. Mahony, P.E. Auron and P.V. Benos “Inferring protein-DNA dependencies using motif alignments and mutual information.” Bioinformatics (Proc ISMB), (2007) 23:i297-i304.Abstract (PubMed) and pdf    PMID: 17646310
    Multiple transcription factor protein sequence alignments and the alignments of their corresponding DNA motifs are analyzed using an information theoretic approach to identify amino acid and base positions that co-vary, pointing into possible residue interactions.
  29. S. Mahony, D.L. Corcoran, E. Feingold, P.V. Benos “Regulatory conservation of protein coding and miRNA genes in vertebrates: lessons from the opossum genome.” Genome Biol, (2007) 8:R84. Abstract (PubMed) and pdf    PMID: 17506886   
    This accompanying paper for the publication of the opossum genome (see below) analyzes the promoters of the protein coding genes and the upstream regions of the microRNA genes and identifies similar conservation patterns. Also, a probabilistic (Bayesian) method is developed to help researchers identify the most informative set of species when they use phylogenetic footprinting to identify cis-regulatory elements.
  30. T.S. Mikkelsen(1), …, P.V. Benos(18), …, S. Mahony(37), …, K. Lindblad-Toh(64) “Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences.” Nature, (2007) 447:167-177. Abstract (PubMed) and pdf    PMID: 17495919
    The publication of the opossum genome.
  31. S. Mahony, P.E. Auron, P.V. Benos “DNA Familial Binding Profiles Made Easy: Comparison of Various Motif Alignment and Clustering Strategies.” PLoS Comput Biol, (2007) 3:e61.Abstract (PubMed) and pdf    PMID: 17397256
    Aiming to the development of a strategy for aligning DNA profiles (PSSM models) anumber of column-to-column comparison methods are tested in conjunction with local and global alignment strategies and distance tree building methods. The best performing method is selected to build a new improved set of familial binding profiles (FBPs). FBPs can be used as prior information in motif search algorithms and for identifying the transcription factor that binds to a newly discovered DNA motif (or -at least- the structural family the transcription factor belongs to).
  32. (Web tool) S. Mahony, P.V. Benos “STAMP: a web tool for exploring DNA-binding motif similarities.” Nucleic Acids Res, (2007) 35:W253-258. Abstract (PubMed) and pdf    PMID: 17478497
    The STAMP web tool offers public access to the DNA motif alignment strategies tested in the above paper. It offers the functionality of BLAST and CLUSTALW for DNA motifs.
  33. S. Mahony, P.V. Benos, T.J. Smith, and A. Golden “Self-Organizing Neural Networks to Support the Discovery of DNA-Binding Motifs.” Neural Networks, (2006) 19:950-962. Abstract(PubMed) and pdf    PMID: 16839740
  34. K. Belov, J.E. Deakin, A.T. Papenfuss, M.L. Baker, S.D. Melman, H.V. Siddle, N. Gouin, D.L. Goode, T.J. Sargeant, M.D. Robinson, M.J. Wakefield, S. Mahony, J.G.R. Cross, P.V. Benos, P.B. Samollow, T.P. Speed, J.A.M. Graves, and R.D. Miller “Reconstructing an Ancestral Mammalian Immune Supercomplex from a Marsupial MHC.” PLoS Biology, (2006) 4:e46.Abstract (PubMed) and pdf    PMID: 16435885
  35. K.R. Rogulski, D.E. Cohen, D.L. Corcoran, P.V. Benos and E.V. Prochownik “Deregulation of common genes by c-Myc and its direct target, MT-MC1.” Proc Natl Acad Sci USA, (2005)102:18968-18973. Abstract (PubMed) and pdf    PMID: 16365299
  36. S. Mahony, A. Golden, T.J. Smith and P.V. Benos “Improved detection of DNA motifs using a self-organized clustering of familial binding profiles.” Bioinformatics (Proc ISMB), (2005) 21 Suppl:i283-i291. Abstract (PubMed) and pdf    PMID: 15961468
    In this paper we show that including gneeralized DNA motifs as priors (familial binding profiles) improves the performance of SOMBRERO, a self-organizing map approach for the detection of multiple cis-regulatory sites from a set of unaligned DNA sequences.
  37. D.L. Corcoran, E. Feingold, J. Dominick, M. Wright, J. Harnaha, M. Trucco, N. Giannoukakis and P.V. Benos “Footer: a quantitative comparative genomics method for efficient recognition of cis-regulatory elements.” Genome Res, (2005) 15:840-847. Abstract (PubMed) and pdf   PMID: 15930494
    FOOTER is a phylogenetic footprinting method for discovery of evolutionary conserved cis-regulatory elements. The novelty of the method is that it takes into consideration both the location of the predicted sites in the human-mouse alignment and their relative PSSM score (considered to be indicative of the binding affinity of the transcription factor to the predicted target).
  38. (Web tool) D.L. Corcoran, E. Feingold, and P.V. Benos “FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting.” Nucleic Acids Res, (2005) 33:W442-446. Abstract (PubMed) and pdf    PMID: 15980508
    Web implementation of the FOOTER algorithm described above. Besides the predicted cis-regulatory sites, the web tool provides information on SNPs located in the submitted promoter sequences.
  39. (Web tool) C.T. Workman, Y. Yin, D.L. Corcoran, T. Ideker, G.D. Stormo and P.V. Benos“EnoLOGOS: a versatile web tool for energy normalized sequence logos.” Nucleic Acids Res, (2005) 33:W389-392. Abstract (PubMed) and pdf    PMID: 15980495
    A web tool for plotting DNA logos. enoLOGOS accepts a pleiad of input formats and provides many options for the output LOGO. It can also optionally plot a heat map of the mutual information content between base positions in the DNA motif.
  40. (Web tool) S. Ringquist, C. Pecoraro, C. Gilchrist, A. Styche, W. Rudert, P.V. Benos, and M. Trucco “SOP3v2: web-based selection of oligonucleotide primer trios for genotyping of human and mouse polymorphisms.” Nucleic Acids Res, (2005) 33:W548-552. Abstract(PubMed) and pdf    PMID: 15980532

Some Older Publications (2000 – 2002)

  1. P.V. Benos, A.S. Lapedes and G.D. Stormo, “Probabilistic code for DNA recognition by proteins of the EGR-family”, J. Mol. Biol., (2002) 323:701-727. Abstract (PubMed) and pdf   PMID: 12419259
    In this paper we show that probabilistic modeling of the C2H2 DNA-binding family can be efficient.
  2. P.V. Benos, M.L. Bulyk and G.D. Stormo, “Additivity in protein-DNA interactions: how good an approximation is it?”, Nucleic Acids Res., (2002) 30:4442-445. Abstract (PubMed) and pdf   PMID: 12384591
    In this paper we re-analyze the data from a previous study [Bulyk et al., Nucl Acids Res, 2002] and we assign statistical significance on the improved performance of the di-nucleotide models. We found that in half of the cases, the di-nucleotide models are not statistically significantly better than the mono-nucleotide (additive) models.
  3. P.V. Benos, A.S. Lapedes and G.D. Stormo, “Is there a code for protein-DNA recognition? Probab(ilistical)ly…”, Bioessays, (2002) 24:466-475. (Review article) Abstract (PubMed) andpdf    PMID: 12001270
  4. P.V. Benos||, A.S. Lapedes, D.S. Fields, G.D. Stormo, “SAMIE: Statistical Algorithm for Modeling Interaction Energies”, Pac. Symp. Biocomput. (2001) 6:115-126. Abstract(PubMed) and pdf    PMID: 11262933
    The first presentation of SAMIE, a statistical algorithm for modeling protein-DNA interactions in a quantitative way.
  5. P.V. Benos, M.K. Gatt, L. Murphy, D. Harris, B. Barrel, C. Ferraz, S. Vidal, C. Brun, J. Demaille, E. Cadieu, S. Dreano, S. Gloux, V. Lelaure, S. Mottier, F. Galibert, D. Borkova, B. Minana, F.C. Kafatos, S. Bolshakov, I. Siden-Kiamos, G. Papagiannakis, L. Spanos, C. Louis, E. Madueno, B. de Pablos, J. Modolell, A. Peter, P. Schoettler, M. Werner, F. Mourkioti, N. Beinert, G. Dowe, U. Schaefer, H. Jaeckle, A. Bucheton, D. Callister, L. Campbell, N.S. Henderson, P.J. Mcmillan, C. Salles, E. Tait, P. Valenti, R.D.C. Saunders, A. Billaud, L. Pachter, D.M. Glover and M. Ashburner, “From first base: The sequence of the tip of the X chromosome of D. melanogaster, a comparison of two sequencing strategies”, Genome Res, (2001) 11:710-730. Abstract (PubMed) and pdf (publisher).    PMID: 11337470
    In this paper we compare the efficiency of the clone-by-clone and whole-genome-shotgun sequencing strategies, both in terms of genome assembly accuracy and annotation of the predicted genes.
  6. P.V. Benos, M.K. Gatt, M. Ashburner, L. Murphy, D. Harris, B. Barrel, C. Ferraz, S. Vidal, C. Brun, J. Demailles, E. Cadieu, S. Dreano, S. Gloux, V. Lelaure, S. Mottier, F. Galibert, D. Borkova, B. Minana, F.C. Kafatos, C. Louis, I. Siden-Kiamos, S. Bolshakov, G. Papagiannakis, L. Spanos, S. Cox, E. Madueno, B. de Pablos, J. Modolell, A. Peter, P. Schoettler, M. Werner, F. Mourkioti, N. Beinert, G. Dowe, U. Schaefer, H. Jaeckle, A. Bucheton, D. Callister, L. Campbell, N.S. Henderson, P.J. Mcmillan, C. Salles, E. Tait, P. Valenti, R.D.C. Saunders and D.M. Glover, “From DNA sequence to chromosome organisation: the tip of the X chromosome of D. melanogaster“, Science (2000) 287:2220-2222. Abstract (PubMed) and pdf (publisher).    PMID: 10731137
    This is an accompanying paper for the publication of the Drosophila genome that tries to identify sequence features that might explain the bulbous structure that has been observed at the tip of the X chromosome in the region of the broad complex.
  7. M.D. Adams(1), …, P.V. Benos(43), …, J.C. Venter(195), “The genome sequence ofDrosophila melanogaster“, Science (2000) 287:2185-2195. Abstract (PubMed) and pdf(publisher).    PMID: 10731132
    The publication of the Drosophila genome, the first multicellular eukaryote sequenced using the whole-genome-shotgun sequencing strategy.
  8. S. Brogna*, P.V. Benos*, G. Gasperi, C. Savakis, “The Drosophila alcohol dehydrogenase gene may have evolved independently of the functionally homologous medfly, olive fly and flesh fly genes”, Mol. Biol. Evol., (2001) 18:322-329. Abstract (PubMed) and pdf (publisher).
    PMID: 11230533 (*) equal contributionThe first publication providing evidence that the acceleration observed in the pace of evolution in alcohol dehydrogenase genes in Drosophilids can be due to paralogous evolution.
  9. P. Benos||, N. Tavernarakis, S.Brogna, G. Thireos, C. Savakis, “Acquisition of a potential marker for insect transformation: isolation of a novel alcohol dehydrogenase gene fromBactrocera oleae by functional complementation in yeast”, Mol. Gen. Genet. (2000) 263:90-95. Abstract (PubMed) and pdf (publisher).    PMID: 10732677
    In this paper, we show that the insect alcohol dehydrogenase, a gene very different from its non-insect enzymes, can functionally substitute the yeast proteins. This method can be used to clone effectively the alcohol dehydrogenase genes from any insect. Alcohol dehydrogenase genes are useful transformation markers, but they evlve very fast in insects, thus their cloning is problematic.

 


Book chapters

Integrating Omics Data
(Editors: George Tseng, Debashis Ghosh, Xianghong Jasmine Zhou)
P.V. Benos “microRNAs: target prediction and involvement in gene regulatory networks”
Publisher: Cambridge University Press; 1 edition (September 2015)
ISBN: 978-1107069114 (buy)
book_ComputSysBiol_small Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (1 Vol)
(Editors: Limin Angela Liu, Dongqing Wei and Yixue Li)
A.B. Tchagang, Y. Pan, F. Famili, A.H. Tewfik and P.V. Benos “Biclustering of DNA Microarray Data: Theory, Evaluation, and Applications”
Publisher: Medical Information Science Reference; 1 edition (January 2011)
ISBN: 978-1609604912 (buy)
book_ElementsComputSystBiol_small Elements of Computational Systems Biology
(Editors: Huma Lodhi and Stephen Muggleton)
P.V. Benos, A. Tchagang, “From DNA motifs to gene networks: a review of the physical interaction models”
Publisher: Wiley and Sons; 1 edition (January 2010)
ISBN: 978-0470180938 (buy)
book_Bioinformatics_Systems_Biology_small Bioinformatics for Systems Biology
(Editor: Stephen A. Krawetz)
P.V. Benos, In Silico Discovery of DNA Regulatory Sites and Modules”
Publisher: Humana Press; 1 edition (February 17, 2009)
ISBN: 978-1934115022 (buy)
book_Comparative_Genomics_vol2_small Comparative Genomics: Volume 2 (Methods in Molecular Biology)
(Editor: Nicholas H. Bergman)
P.V. Benos, D.L. Corcoran, E. Feingold, “FOOTER: Web-Based Identification of Evolutionary Conserved DNA cis-regulatory Elements”
Publisher: Humana Press; 1 edition (November 29, 2007)
ISBN: 978-1934115374 (buy)
book_Pyrosequencing_small Pyrosequencing Protocols (Methods in Molecular Biology)
(Editor: Sharon Marsh)
S. Ringquist, C. Pecoraro, Y. Lu, A. Styche, W.A. Rudert, P.V. Benos, M. Trucco, “Web-based primer design software for genome scale SNP mapping by pyrosequencing”
Publisher: Humana Press; 1 edition (November 21, 2006)
ISBN: 978-1588296450 (buy)