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Hongyao HE, Chun JI, Xiaoyan G, Fangfang L, Jing Z, Lin Z, Pengxiang Z, Zengchun L. Associative gene networks reveal novel candidates important for ADHD and dyslexia comorbidity. BMC Med Genomics 2023; 16:208. [PMID: 37667328 PMCID: PMC10478365 DOI: 10.1186/s12920-023-01502-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/26/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) is commonly associated with developmental dyslexia (DD), which are both prevalent and complicated pediatric neurodevelopmental disorders that have a significant influence on children's learning and development. Clinically, the comorbidity incidence of DD and ADHD is between 25 and 48%. Children with DD and ADHD may have more severe cognitive deficiencies, a poorer level of schooling, and a higher risk of social and emotional management disorders. Furthermore, patients with this comorbidity are frequently treated for a single condition in clinical settings, and the therapeutic outcome is poor. The development of effective treatment approaches against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and treatment. In this study, we developed bioinformatical methodology for the analysis of the comorbidity of these two diseases. As such, the search for candidate genes related to the comorbid conditions of ADHD and DD can help in elucidating the molecular mechanisms underlying the comorbid condition, and can also be useful for genotyping and identifying new drug targets. RESULTS Using the ANDSystem tool, the reconstruction and analysis of gene networks associated with ADHD and dyslexia was carried out. The gene network of ADHD included 599 genes/proteins and 148,978 interactions, while that of dyslexia included 167 genes/proteins and 27,083 interactions. When the ANDSystem and GeneCards data were combined, a total of 213 genes/proteins for ADHD and dyslexia were found. An approach for ranking genes implicated in the comorbid condition of the two diseases was proposed. The approach is based on ten criteria for ranking genes by their importance, including relevance scores of association between disease and genes, standard methods of gene prioritization, as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analyzed genes. Among the top 20 genes with the highest priority DRD2, DRD4, CNTNAP2 and GRIN2B are mentioned in the literature as directly linked with the comorbidity of ADHD and dyslexia. According to the proposed approach, the genes OPRM1, CHRNA4 and SNCA had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the most relevant genes are involved in biological processes related to signal transduction, positive regulation of transcription from RNA polymerase II promoters, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, and neuron migration. CONCLUSIONS The application of methods of reconstruction and analysis of gene networks is a powerful tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance for the comorbid condition of ADHD and dyslexia was employed to predict genes that play key roles in the development of the comorbid condition. The results can be utilized to plan experiments for the identification of novel candidate genes and search for novel pharmacological targets.
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Affiliation(s)
- H E Hongyao
- Medical College of Shihezi University, Shihezi, China
| | - J I Chun
- Medical College of Shihezi University, Shihezi, China
| | - Gao Xiaoyan
- Medical College of Shihezi University, Shihezi, China
| | - Liu Fangfang
- Medical College of Shihezi University, Shihezi, China
| | - Zhang Jing
- Medical College of Shihezi University, Shihezi, China
| | - Zhong Lin
- Medical College of Shihezi University, Shihezi, China
| | - Zuo Pengxiang
- Medical College of Shihezi University, Shihezi, China.
| | - Li Zengchun
- Medical College of Shihezi University, Shihezi, China.
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Morello G, La Cognata V, Guarnaccia M, D'Agata V, Cavallaro S. Cracking the Code of Neuronal Cell Fate. Cells 2023; 12:cells12071057. [PMID: 37048129 PMCID: PMC10093029 DOI: 10.3390/cells12071057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
Transcriptional regulation is fundamental to most biological processes and reverse-engineering programs can be used to decipher the underlying programs. In this review, we describe how genomics is offering a systems biology-based perspective of the intricate and temporally coordinated transcriptional programs that control neuronal apoptosis and survival. In addition to providing a new standpoint in human pathology focused on the regulatory program, cracking the code of neuronal cell fate may offer innovative therapeutic approaches focused on downstream targets and regulatory networks. Similar to computers, where faults often arise from a software bug, neuronal fate may critically depend on its transcription program. Thus, cracking the code of neuronal life or death may help finding a patch for neurodegeneration and cancer.
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Affiliation(s)
- Giovanna Morello
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
| | - Valentina La Cognata
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
| | - Maria Guarnaccia
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
| | - Velia D'Agata
- Section of Human Anatomy and Histology, Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy
| | - Sebastiano Cavallaro
- Institute for Biomedical Research and Innovation, National Research Council (CNR-IRIB), 95126 Catania, Italy
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Ivanisenko TV, Saik OV, Demenkov PS, Ivanisenko NV, Savostianov AN, Ivanisenko VA. ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature. BMC Bioinformatics 2020; 21:228. [PMID: 32921303 PMCID: PMC7488989 DOI: 10.1186/s12859-020-03557-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 05/25/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The rapid growth of scientific literature has rendered the task of finding relevant information one of the critical problems in almost any research. Search engines, like Google Scholar, Web of Knowledge, PubMed, Scopus, and others, are highly effective in document search; however, they do not allow knowledge extraction. In contrast to the search engines, text-mining systems provide extraction of knowledge with representations in the form of semantic networks. Of particular interest are tools performing a full cycle of knowledge management and engineering, including automated retrieval, integration, and representation of knowledge in the form of semantic networks, their visualization, and analysis. STRING, Pathway Studio, MetaCore, and others are well-known examples of such products. Previously, we developed the Associative Network Discovery System (ANDSystem), which also implements such a cycle. However, the drawback of these systems is dependence on the employed ontologies describing the subject area, which limits their functionality in searching information based on user-specified queries. RESULTS The ANDDigest system is a new web-based module of the ANDSystem tool, permitting searching within PubMed by using dictionaries from the ANDSystem tool and sets of user-defined keywords. ANDDigest allows performing the search based on complex queries simultaneously, taking into account many types of objects from the ANDSystem's ontology. The system has a user-friendly interface, providing sorting, visualization, and filtering of the found information, including mapping of mentioned objects in text, linking to external databases, sorting of data by publication date, citations number, journal H-indices, etc. The system provides data on trends for identified entities based on dynamics of interest according to the frequency of their mentions in PubMed by years. CONCLUSIONS The main feature of ANDDigest is its functionality, serving as a specialized search for information about multiple associative relationships of objects from the ANDSystem's ontology vocabularies, taking into account user-specified keywords. The tool can be applied to the interpretation of experimental genetics data, the search for associations between molecular genetics objects, and the preparation of scientific and analytical reviews. It is presently available at https://anddigest.sysbio.ru/ .
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Affiliation(s)
- Timofey V Ivanisenko
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.
- Laboratory of Computer Genomics, Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090, Russia.
- Kurchatov Genomics Center, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.
| | - Olga V Saik
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
| | - Pavel S Demenkov
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- Kurchatov Genomics Center, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090, Russia
| | - Nikita V Ivanisenko
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- Kurchatov Genomics Center, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
| | | | - Vladimir A Ivanisenko
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- Kurchatov Genomics Center, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia
- Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090, Russia
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Ivanisenko VA, Demenkov PS, Ivanisenko TV, Mishchenko EL, Saik OV. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics 2019; 20:34. [PMID: 30717676 PMCID: PMC6362586 DOI: 10.1186/s12859-018-2567-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Consideration of tissue-specific gene expression in reconstruction and analysis of molecular genetic networks is necessary for a proper description of the processes occurring in a specified tissue. Currently, there are a number of computer systems that allow the user to reconstruct molecular-genetic networks using the data automatically extracted from the texts of scientific publications. Examples of such systems are STRING, Pathway Commons, MetaCore and Ingenuity. The MetaCore and Ingenuity systems permit taking into account tissue-specific gene expression during the reconstruction of gene networks. Previously, we developed the ANDSystem tool, which also provides an automated extraction of knowledge from scientific texts and allows the reconstruction of gene networks. The main difference between our system and other tools is in the different types of interactions between objects, which makes the ANDSystem complementary to existing well-known systems. However, previous versions of the ANDSystem did not contain any information on tissue-specific expression. RESULTS A new version of the ANDSystem has been developed. It offers the reconstruction of associative gene networks while taking into account the tissue-specific gene expression. The ANDSystem knowledge base features information on tissue-specific expression for 272 tissues. The system allows the reconstruction of combined gene networks, as well as performing the filtering of genes from such networks using the information on their tissue-specific expression. As an example of the application of such filtering, the gene network of the extrinsic apoptotic signaling pathway was analyzed. It was shown that considering different tissues can lead to changes in gene network structure, including changes in such indicators as betweenness centrality of vertices, clustering coefficient, network centralization, network density, etc. CONCLUSIONS: The consideration of tissue specificity can play an important role in the analysis of gene networks, in particular solving the problem of finding the most significant central genes. Thus, the new version of ANDSystem can be employed for a wide range of tasks related to biomedical studies of individual tissues. It is available at http://www-bionet.sscc.ru/and/cell /.
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Affiliation(s)
- Vladimir A. Ivanisenko
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090 Russia
- Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090 Russia
| | - Pavel S. Demenkov
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090 Russia
- Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090 Russia
| | - Timofey V. Ivanisenko
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090 Russia
- Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090 Russia
| | - Elena L. Mishchenko
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090 Russia
| | - Olga V. Saik
- Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090 Russia
- Novosibirsk State University, st. Pirogova 1, Novosibirsk, 630090 Russia
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Saik OV, Demenkov PS, Ivanisenko TV, Bragina EY, Freidin MB, Goncharova IA, Dosenko VE, Zolotareva OI, Hofestaedt R, Lavrik IN, Rogaev EI, Ivanisenko VA. Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks. BMC Med Genomics 2018; 11:15. [PMID: 29504915 PMCID: PMC6389037 DOI: 10.1186/s12920-018-0331-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. RESULTS Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system. CONCLUSIONS The application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets.
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Affiliation(s)
- Olga V. Saik
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
| | - Pavel S. Demenkov
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
| | - Timofey V. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena Yu Bragina
- Research Institute of Medical Genetics, Tomsk NRMC, Tomsk, Russia
| | - Maxim B. Freidin
- Research Institute of Medical Genetics, Tomsk NRMC, Tomsk, Russia
| | | | | | - Olga I. Zolotareva
- Bielefeld University, International Research Training Group “Computational Methods for the Analysis of the Diversity and Dynamics of Genomes”, Bielefeld, Germany
| | - Ralf Hofestaedt
- Bielefeld University, Technical Faculty, AG Bioinformatics and Medical Informatics, Bielefeld, Germany
| | - Inna N. Lavrik
- Department of Translational Inflammation, Institute of Experimental Internal Medicine, Otto von Guericke University, Magdeburg, Germany
| | - Evgeny I. Rogaev
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
- University of Massachusetts Medical School, Worcester, MA USA
- Department of Genomics and Human Genetics, Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
- Center for Genetics and Genetic Technologies, Faculty of Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Vladimir A. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
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Tiys ES, Ivanisenko TV, Demenkov PS, Ivanisenko VA. FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets. BMC Genomics 2018; 19:76. [PMID: 29504895 PMCID: PMC5836822 DOI: 10.1186/s12864-018-4474-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members. Results We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones. Conclusions FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/. Electronic supplementary material The online version of this article (10.1186/s12864-018-4474-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Evgeny S Tiys
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia. .,Laboratory of Computer Genomics, Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia.
| | - Timofey V Ivanisenko
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia.,Laboratory of Computer Genomics, Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia
| | - Pavel S Demenkov
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia
| | - Vladimir A Ivanisenko
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia
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Kim S, Pick JE, Abera S, Khatri L, Ferreira DDP, Sathler MF, Morison SL, Hofmann F, Ziff EB. Brain region-specific effects of cGMP-dependent kinase II knockout on AMPA receptor trafficking and animal behavior. ACTA ACUST UNITED AC 2016; 23:435-41. [PMID: 27421896 PMCID: PMC4947234 DOI: 10.1101/lm.042960.116] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 05/27/2016] [Indexed: 12/25/2022]
Abstract
Phosphorylation of GluA1, a subunit of AMPA receptors (AMPARs), is critical for AMPAR synaptic trafficking and control of synaptic transmission. cGMP-dependent protein kinase II (cGKII) mediates this phosphorylation, and cGKII knockout (KO) affects GluA1 phosphorylation and alters animal behavior. Notably, GluA1 phosphorylation in the KO hippocampus is increased as a functional compensation for gene deletion, while such compensation is absent in the prefrontal cortex. Thus, there are brain region-specific effects of cGKII KO on AMPAR trafficking, which could affect animal behavior. Here, we show that GluA1 phosphorylation levels differ in various brain regions, and specific behaviors are altered according to region-specific changes in GluA1 phosphorylation. Moreover, we identified distinct regulations of phosphatases in different brain regions, leading to regional heterogeneity of GluA1 phosphorylation in the KO brain. Our work demonstrates region-specific changes in GluA1 phosphorylation in cGKII KO mice and corresponding effects on cognitive performance. We also reveal distinct regulation of phosphatases in different brain region in which region-specific effects of kinase gene KO arise and can selectively alter animal behavior.
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Affiliation(s)
- Seonil Kim
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA
| | - Joseph E Pick
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA
| | - Sinedu Abera
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA
| | - Latika Khatri
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA
| | - Danielle D P Ferreira
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA Department of Pharmacology and Physiology, Fluminense Federal University, Niteroi 24210-130, Brazil
| | - Matheus F Sathler
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA Department of Pharmacology and Physiology, Fluminense Federal University, Niteroi 24210-130, Brazil
| | - Sage L Morison
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA Center for Neural Science, New York University, New York 10012, USA
| | - Franz Hofmann
- Technical University of Munich, Munich 80802, Germany
| | - Edward B Ziff
- Department of Biochemistry and Molecular Pharmacology, New York University Langone Medical Center, New York 10016, New York, USA
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Baranova AV, Orlov YL. The papers presented at 7th Young Scientists School "Systems Biology and Bioinformatics" (SBB'15): Introductory Note. Introduction. BMC Genet 2016; 17 Suppl 1:20. [PMID: 26822407 PMCID: PMC4895277 DOI: 10.1186/s12863-015-0326-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Ancha V Baranova
- Research Center for Medical Genetics RAMS, Moscow, Russian Federation. .,Center for the Study of Chronic Metabolic Diseases, School of System Biology, George Mason University, Fairfax, VA, USA.
| | - Yuriy L Orlov
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Lavrentieva ave., 10, Novosibirsk, 630090, Russian Federation.,Novosibirsk State University, Pirogova, 2, Novosibirsk, 630090, Russian Federation
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