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890 results on '"Genetic network"'

51. Reconstructing Gene Regulatory Network with Enhanced Particle Swarm Optimization

Author

Sultana, Rezwana, Showkat, Dilruba, Samiullah, Mohammad, Chowdhury, Ahsan Raja, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Loo, Chu Kiong, editor, Yap, Keem Siah, editor, Wong, Kok Wai, editor, Teoh, Andrew, editor, and Huang, Kaizhu, editor

Published
2014
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55. A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis

Author

Huang, Heng, Yan, Jingwen, Nie, Feiping, Huang, Jin, Cai, Weidong, Saykin, Andrew J., Shen, Li, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mori, Kensaku, editor, Sakuma, Ichiro, editor, Sato, Yoshinobu, editor, Barillot, Christian, editor, and Navab, Nassir, editor

Published
2013
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61. On the Reconstruction of Genetic Network from Partial Microarray Data

Author

Chowdhury, Ahsan Raja, Chetty, Madhu, Vinh, Xuan Nguyen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, Tingwen, editor, Zeng, Zhigang, editor, Li, Chuandong, editor, and Leung, Chi Sing, editor

Published
2012
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62. Genetic Network Design Automation with LOICA.

Author

Vidal G, Vitalis C, Matúte T, Núñez I, Federici F, and Rudge TJ

Subjects
Gene Regulatory Networks, Algorithms, Synthetic Biology methods, Automation, Software, Programming Languages
Abstract

Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic networks in a high-throughput fashion. At the core of these tools is the abstraction of a hierarchy of standardized components. The components' input, output, and interactions must be captured and parametrized from relevant experimental data. Simulations of genetic networks should use those parameters and include the experimental context to be compared with the experimental results.This chapter introduces Logical Operators for Integrated Cell Algorithms (LOICA), a Python package used for designing, modeling, and characterizing genetic networks using a simple object-oriented design abstraction. LOICA represents different biological and experimental components as classes that interact to generate models. These models can be parametrized by direct connection to the Flapjack experimental data management platform to characterize abstracted components with experimental data. The models can be simulated using stochastic simulation algorithms or ordinary differential equations with varying noise levels. The simulated data can be managed and published using Flapjack alongside experimental data for comparison. LOICA genetic network designs can be represented as graphs and plotted as networks for visual inspection and serialized as Python objects or in the Synthetic Biology Open Language (SBOL) format for sharing and use in other designs., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Published
2024
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63. Introduction

Author

Niiranen, Samuli, Ribeiro, Andre, Kacprzyk, Janusz, editor, Jain, Lakhmi C., editor, Niiranen, Samuli, editor, and Ribeiro, Andre, editor

Published
2011
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72. Tissue expression profiles unveil the gene interaction of hepatopancreas, eyestalk, and ovary in the precocious female Chinese mitten crab, Eriocheir sinensis.

Author

Chen, Xiaowen, Wang, Jun, Hou, Xin, Yue, Wucheng, Huang, Shu, and Wang, Chenghui

Subjects
GENE expression, PRECOCIOUS puberty, CHINESE mitten crab, TISSUE analysis, ANIMAL species
Abstract

Background: Sexual precocity is a common biological phenomenon in animal species. A large number of precocity individuals were identified in Chinese mitten crab Eriocheir sinensis, which caused huge economic loss annually. However, the underlying genetic basis of precocity in E. sinensis remains unclear to date. Results: In this study, morphological and histological observation and comparative transcriptome analysis were conducted among different stages of precocious one-year-old and normal two-year-old sexually mature E. sinensis. The expression profiles of the ovary, hepatopancreas, and eyestalk tissues were presented and compared. Genes associated with lipid metabolic process, lipid transport, vitelline membrane formation, vitelline synthesis, and neuropeptide hormone-related genes were upregulated in the ovary, hepatopancreas, and eyestalk of precocious E. sinensis. Our results indicated that the eyestalk was involved in the neuroendocrine system providing neuropeptide hormones that may induce vitellogenesis in the hepatopancreas and further stimulate ovary development. The hepatopancreas is a site for energy storage and vitellogenin synthesis, and it may assist oogenesis through lipid transport in precocious E. sinensis. Conclusion: We provided not only an effective and convenient phenotype measurement method for the identification of potential precocious E. sinensis detection but also valuable genetic resources and novel insights into the molecular mechanism of precocity in E. sinensis. The genetic basis of precocity in E. sinensis is an integrated gene regulatory network of eyestalk, hepatopancreas, and ovary tissues. [ABSTRACT FROM AUTHOR]

Published
2019
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73. Bioelectrical coupling in multicellular domains regulated by gap junctions: A conceptual approach.

Author

Cervera, Javier, Pietak, Alexis, Levin, Michael, and Mafe, Salvador

Subjects
*ELECTROPHYSIOLOGY, *COUPLING reactions (Chemistry), *MEMBRANE potential, *POLARIZATION (Electrochemistry), *ION channels
Abstract

We review the basic concepts involved in bioelectrically-coupled multicellular domains, focusing on the role of membrane potentials ( V mem ). In the first model, single-cell V mem is modulated by two generic polarizing and depolarizing ion channels, while intercellular coupling is implemented via voltage-gated gap junctions. Biochemical and bioelectrical signals are integrated via a feedback loop between V mem and the transcription and translation of a protein forming an ion channel. The effective rate constants depend on the single-cell V mem because these potentials modulate the local concentrations of signaling molecules and ions. This electrochemically-based idealization of the complex biophysical problem suggests that the spatio-temporal map of single-cell potentials can influence downstream patterning processes by means of the voltage-gated gap junction interconnectivity, much as in the case of electronic devices where the control of electric potentials and currents allows the local modulation of the circuitry to achieve full functionality. An alternative theoretical approach, the BioElectrical Tissue Simulation Engine (BETSE), is also presented. The BETSE modeling environment utilizes finite volume techniques to simulate bioelectric states from the perspective of ion concentrations and fluxes. This model has been successfully applied to make predictions and explain experimental observations in a variety of embryonic, regenerative, and oncogenic contexts. [ABSTRACT FROM AUTHOR]

Published
2018
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74. The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target.

Author

Petyuk, Vladislav A, Chang, Rui, Ramirez-Restrepo, Manuel, Beckmann, Noam D, Henrion, Marc Y R, Piehowski, Paul D, Zhu, Kuixi, Wang, Sven, Clarke, Jennifer, Huentelman, Matthew J, Xie, Fang, Andreev, Victor, Engel, Anzhelika, Guettoche, Toumy, Navarro, Loida, De Jager, Philip, Schneider, Julie A, Morris, Christopher M, McKeith, Ian G, and Perry, Robert H

Subjects
*GENETICS of Alzheimer's disease, *GENE expression, *DEMENTIA, *PROTEIN expression, *BRAIN proteins, *CELL lines, *BRAIN research, *GENETICS, *RNA analysis, *RNA metabolism, *PROTEIN metabolism, *PROTEINS, *RESEARCH, *ALZHEIMER'S disease, *NERVOUS system, *RESEARCH methodology, *BRAIN mapping, *EVALUATION research, *MEDICAL cooperation, *COMPARATIVE studies, *GENE expression profiling, *RESEARCH funding, *EPITHELIAL cells, BRAIN metabolism
Abstract

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001. [ABSTRACT FROM AUTHOR]

Published
2018
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75. Metastatic group 3 medulloblastoma is driven by PRUNE1 targeting NME1-TGF-β-OTX2-SNAIL via PTEN inhibition.

Author

Ferrucci, Veronica, de Antonellis, Pasqualino, Pennino, Francesco Paolo, Asadzadeh, Fatemeh, Virgilio, Antonella, Montanaro, Donatella, Galeone, Aldo, Boffa, Iolanda, Pisano, Ida, Scognamiglio, Iolanda, Navas, Luigi, Diana, Donatella, Pedone, Emilia, Gargiulo, Sara, Gramanzini, Matteo, Brunetti, Arturo, Danielson, Laura, Carotenuto, Marianeve, Liguori, Lucia, and Verrico, Antonio

Subjects
*MEDULLOBLASTOMA, *PTEN protein, *TUMOR growth, *XENOGRAFTS, *GENETIC mutation, *ANIMAL experimentation, *BIOCHEMISTRY, *BIOLOGICAL models, *CELL lines, *CELLULAR signal transduction, *GENE expression, *GENES, *GLIOMAS, *PHENOMENOLOGY, *METASTASIS, *MICE, *PHOSPHATASES, *CHEMICAL inhibitors
Abstract

Genetic modifications during development of paediatric groups 3 and 4 medulloblastoma are responsible for their highly metastatic properties and poor patient survival rates. PRUNE1 is highly expressed in metastatic medulloblastoma group 3, which is characterized by TGF-β signalling activation, c-MYC amplification, and OTX2 expression. We describe the process of activation of the PRUNE1 signalling pathway that includes its binding to NME1, TGF-β activation, OTX2 upregulation, SNAIL (SNAI1) upregulation, and PTEN inhibition. The newly identified small molecule pyrimido-pyrimidine derivative AA7.1 enhances PRUNE1 degradation, inhibits this activation network, and augments PTEN expression. Both AA7.1 and a competitive permeable peptide that impairs PRUNE1/NME1 complex formation, impair tumour growth and metastatic dissemination in orthotopic xenograft models with a metastatic medulloblastoma group 3 cell line (D425-Med cells). Using whole exome sequencing technology in metastatic medulloblastoma primary tumour cells, we also define 23 common 'non-synonymous homozygous' deleterious gene variants as part of the protein molecular network of relevance for metastatic processes. This PRUNE1/TGF-β/OTX2/PTEN axis, together with the medulloblastoma-driver mutations, is of relevance for future rational and targeted therapies for metastatic medulloblastoma group 3.10.1093/brain/awy039_video1awy039media15742053534001. [ABSTRACT FROM AUTHOR]

Published
2018
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76. What do the genetic association data say about the high risk of suicide in people with depression? A novel network-based approach to find common molecular basis for depression and suicidal behavior and related therapeutic targets.

Author

Bozorgmehr, Ali, Alizadeh, Fatemeh, Ofogh, Sattar Norouzi, Hamzekalayi, Mohammad Reza Abdollahzadeh, Herati, Sara, Moradkhani, Atefeh, Shahbazi, Ali, and Ghadirivasfi, Mohammad

Subjects
*SUICIDAL behavior, *SUICIDE risk factors, *MENTAL depression, *AFFECTIVE disorders, *TRICYCLIC antidepressants, *ANTIDEPRESSANTS, *SEROTONIN uptake inhibitors, *DATABASES, *GENETIC techniques, *MEMBRANE proteins, *NERVE tissue proteins, *PROTEINS, *SUICIDE, *TUMOR necrosis factors, *SUICIDAL ideation
Abstract

Background: Available sources indicate that the risk of suicide in people with major depression is higher than other psychiatric disorders. Although it seems that these two conditions may have a shared cause in some cases, no studies have been conducted to identify a common basis for them.Methods: In this study, following an extensive review of literature, we found almost all the genes that are involved in major depression and suicidal behavior, and we isolated genes shared between the two conditions. Then, we found all physical or functional interactions within three mentioned gene sets and reconstructed three genetic interactive networks. All networks were analyzed topologically and enriched functionally. Finally, using a drug repurposing approach, we found the main available drugs that interacted with the most central genes shared between suicidal behavior and depression.Results: The results demonstrated that BDNF, SLC6A4, CREB1, and TNF are the most fundamental shared genes; and generally, disordered dopaminergic, serotonergic, and immunologic pathways in neuronal projections are the main shared deficient pathways. In addition, we found two genes, SLC6A4 and SLC6A2, to be the main therapeutic targets, and Serotonin-Norepinephrine Reuptake Inhibitors (SNRI) and Tricyclic Antidepressants (TCA) to be the most effective drugs for individuals with depression at risk for suicide.Conclusions: Our results, in addition to shedding light on the integrated molecular basis of depression-suicide, offer new therapeutic targets for individuals with depression at high risk for suicide and could pave the way for future preclinical and clinical studies. However, integrative systems biology-based studies highly depend on existing data and related databases, as well as the arrival of new experimental data sources in the future, possibly affecting the current results. [ABSTRACT FROM AUTHOR]

Published
2018
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77. Formal Analysis of the Genetic Toggle

Author

Bella, Giampaolo, Liò, Pietro, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Degano, Pierpaolo, editor, and Gorrieri, Roberto, editor

Published
2009
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78. Genetic variations and relationships between deformed wing virus strains infesting honey bees based on structural proteins

Author

Hossam F. Abou-Shaara and Shokry R. Bayoumi

Subjects
Genetics, Phylogenetic tree, Genetic network, Cell Biology, Plant Science, Biology, biology.organism_classification, Biochemistry, Virus, Honey Bees, Specific primers, Deformed wing virus, Statistical analyses, Genetic variation, Animal Science and Zoology, Molecular Biology, Ecology, Evolution, Behavior and Systematics
Abstract

Deformed wing virus (DWV) is considered among the most common pathogens in bee colonies worldwide. This virus is anticipated to contribute in causing the mass collapse of bee colonies and winter colony losses. The sequences and amino acids of proteins for some strains are currently available. So, tools of bioinformatics can be applied to investigate these sequences. The objective of this study was to investigate the genetic relationships between 21 strains of this virus isolated from honey bees in Europe, Asia, USA, New Zealand and Chile based on amino acids and protein sequences. Phylogenetic relationships were studied in combination with genetic network to understand mutations in the common ancestors. The results showed low number of mutations between strains of Asia and those of the USA, New Zealand, Chile and some European countries while high number of mutations was detected between those of Asia and Western European countries. The results showed the occurrence of mutations rapidly in the sequences of this virus with suggestion to its ability to adapt with different environments. Also, sequence and statistical analyses were used to shade more lights on the variations between strains. Additionally, some specific primers were deigned based on the protein sequences of these strains. These primers were tested against the full sequences of all strains and their ability to identify DWV strains was discussed. The current investigation extends the knowledge about this dangerous virus especially the obtained results were based on protein sequences of DWV strains.

Published
2021
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86. Algorithms for Inference, Analysis and Control of Boolean Networks

Author

Akutsu, Tatsuya, Hayashida, Morihiro, Tamura, Takeyuki, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Horimoto, Katsuhisa, editor, Regensburger, Georg, editor, Rosenkranz, Markus, editor, and Yoshida, Hiroshi, editor

Published
2008
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87. Inference on Missing Values in Genetic Networks Using High-Throughput Data

Author

Koukolíková-Nicola, Zdena, Liò, Pietro, Bagnoli, Franco, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Marchiori, Elena, editor, and Moore, Jason H., editor

Published
2008
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91. Modeling Genetic Networks: Comparison of Static and Dynamic Models

Author

Rubio-Escudero, Cristina, Harari, Oscar, Cordón, Oscar, Zwir, Igor, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Marchiori, Elena, editor, Moore, Jason H., editor, and Rajapakse, Jagath C., editor

Published
2007
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94. Asymptotical Lower Limits on Required Number of Examples for Learning Boolean Networks

Author

Abul, Osman, Alhajj, Reda, Polat, Faruk, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Levi, Albert, editor, Savaş, Erkay, editor, Yenigün, Hüsnü, editor, Balcısoy, Selim, editor, and Saygın, Yücel, editor

Published
2006
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95. Qualitative Petri Net Modelling of Genetic Networks

Author

Chaouiya, Claudine, Remy, Elisabeth, Thieffry, Denis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Priami, Corrado, editor, and Plotkin, Gordon, editor

Published
2006
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96. Modelling and Analysing Genetic Networks: From Boolean Networks to Petri Nets

Author

Steggles, L. J., Banks, Richard, Wipat, Anil, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, and Priami, Corrado, editor

Published
2006
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97. Compositional Reachability Analysis of Genetic Networks

Author

Gössler, Gregor, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, and Priami, Corrado, editor

Published
2006
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98. Coupled Random Boolean Network Forming an Artificial Tissue

Author

Villani, M., Serra, R., Ingrami, P., Kauffman, S. A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, El Yacoubi, Samira, editor, Chopard, Bastien, editor, and Bandini, Stefania, editor

Published
2006
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99. A Control Model for Markovian Genetic Regulatory Networks

Author

Ng, Michael K., Zhang, Shu-Qin, Ching, Wai-Ki, Akutsu, Tatsuya, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Priami, Corrado, editor, Hu, Xiaohua, editor, Pan, Yi, editor, and Lin, Tsau Young, editor

Published
2006
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100. Learning Genetic Regulatory Network Connectivity from Time Series Data

Author

Barker, Nathan, Myers, Chris, Kuwahara, Hiroyuki, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ali, Moonis, editor, and Dapoigny, Richard, editor

Published
2006
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