Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314144
M. Elbashir, Jianxin Wang, Fang Wu, Min Li
A β-turn is a secondary protein structure type that plays a significant role in protein folding, stability, and molecular recognition. On average 25% of amino acids in protein structures are located in β-turns. Development of accurate and efficient method for β-turns prediction is very important. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or Neural Networks (NNs), however a method that can yield probabilistic outcome, and has a well-defined extension to the multi-class case will be more valuable in β-turns prediction. Although kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems, however it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper we used KLR to obtain sparse β-turns prediction in short evolution time after speeding it using Nystrom approximation method. Secondary structure information and position specific scoring matrices (PSSMs) are utilized as input features. We achieved Qtotal of 80.4% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent or even better than NNs and SVMs in β-turns prediction. In addition KLR yields probabilistic outcome and has a well-defined extension to multi-class case.
{"title":"Sparse kernel logistic regression for β-turns prediction","authors":"M. Elbashir, Jianxin Wang, Fang Wu, Min Li","doi":"10.1109/ISB.2012.6314144","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314144","url":null,"abstract":"A β-turn is a secondary protein structure type that plays a significant role in protein folding, stability, and molecular recognition. On average 25% of amino acids in protein structures are located in β-turns. Development of accurate and efficient method for β-turns prediction is very important. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or Neural Networks (NNs), however a method that can yield probabilistic outcome, and has a well-defined extension to the multi-class case will be more valuable in β-turns prediction. Although kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems, however it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper we used KLR to obtain sparse β-turns prediction in short evolution time after speeding it using Nystrom approximation method. Secondary structure information and position specific scoring matrices (PSSMs) are utilized as input features. We achieved Qtotal of 80.4% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent or even better than NNs and SVMs in β-turns prediction. In addition KLR yields probabilistic outcome and has a well-defined extension to multi-class case.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120973620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314153
Hua Tan, Fuhai Li, Jaykrishna Singh, Xiaofeng Xia, D. Cridebring, Jian Yang, Ming Zhan, Stephen T. C. Wong, Jiguang Bao, Jinwen Ma
The recent discovery of cancer stem cells (CSCs), or tumor initiating cells (TICs), in a variety of cancers, including breast cancer, provides a key to understand the processes of tumor initiation, progression and recurrence. Here, we present a three-dimensional (3D) multiscale model of the CSC-initiated tumor growth, which takes into account essential microenvironmental (mE) factors (e.g. nutrients, extracellular matrix) and some important biological traits (e.g. angiogenesis, cell apoptosis, and necrosis) and addresses tumor growth from three different levels, i.e. molecular, cellular and tissue levels. At the molecular level, mathematical diffusion-reaction equations are used to understand the dynamics of mE factors. At the cellular level, a cellular automaton is designed to simulate the life cycle and behaviors of individual cells. At the tissue level, a computer graphics method is used to illustrate the geometry of the whole tumor. The simulation study based on the proposed model indicates that the content of CSCs in a tumor mass plays an essential role in driving tumor growth. The simulation also highlights the significance of developing therapeutic agents that can deliver drug molecules into the interior of the tumor, where most of CSCs tend to reside. The simulation study on the breast cancer xenografts reveals that the mouse tumor initiated from a mixed population of human CSCs and other tumor cells show a faster growth rate, while a weaker proliferation and aggressiveness than that initiated from a pure human CSCs population. These simulation results are mostly consistent with our experimental observations. The mathematical model thus provides a new framework for the modeling and simulation studies of CSC-initiated cancer development.
{"title":"A 3-dimentional multiscale model to simulate tumor progression in response to interactions between cancer stem cells and tumor microenvironmental factors","authors":"Hua Tan, Fuhai Li, Jaykrishna Singh, Xiaofeng Xia, D. Cridebring, Jian Yang, Ming Zhan, Stephen T. C. Wong, Jiguang Bao, Jinwen Ma","doi":"10.1109/ISB.2012.6314153","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314153","url":null,"abstract":"The recent discovery of cancer stem cells (CSCs), or tumor initiating cells (TICs), in a variety of cancers, including breast cancer, provides a key to understand the processes of tumor initiation, progression and recurrence. Here, we present a three-dimensional (3D) multiscale model of the CSC-initiated tumor growth, which takes into account essential microenvironmental (mE) factors (e.g. nutrients, extracellular matrix) and some important biological traits (e.g. angiogenesis, cell apoptosis, and necrosis) and addresses tumor growth from three different levels, i.e. molecular, cellular and tissue levels. At the molecular level, mathematical diffusion-reaction equations are used to understand the dynamics of mE factors. At the cellular level, a cellular automaton is designed to simulate the life cycle and behaviors of individual cells. At the tissue level, a computer graphics method is used to illustrate the geometry of the whole tumor. The simulation study based on the proposed model indicates that the content of CSCs in a tumor mass plays an essential role in driving tumor growth. The simulation also highlights the significance of developing therapeutic agents that can deliver drug molecules into the interior of the tumor, where most of CSCs tend to reside. The simulation study on the breast cancer xenografts reveals that the mouse tumor initiated from a mixed population of human CSCs and other tumor cells show a faster growth rate, while a weaker proliferation and aggressiveness than that initiated from a pure human CSCs population. These simulation results are mostly consistent with our experimental observations. The mathematical model thus provides a new framework for the modeling and simulation studies of CSC-initiated cancer development.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314111
X. Gong, Hua Yu, F. Zhao
Identification and analysis of tissue-specific (TS) genes and their regulatory activities play an important role in the understanding of mechanisms of organisms, disease diagnosis and drug design. In this paper, we designed a pipeline for the discovery of promoter motifs for tissue-specific genes. The pipeline consists of three phases: motif searching, motif merging and motif validation. The motif searching phase integrated three algorithms: MEME, AlignACE and Gibbs Sampling. In the second phase, we proposed a motif merging method, which is based on Bayesian probabilistic principles, to reduce redundancies of motifs from the first phase. Lastly, the motif validation phase verified the statistical significance of discovered motifs using a Bayesian Hypothesis Test approach. We performed the analysis on the sequences of promoter regions (-449bp-1000bp) of 4,552 human tissue-specific genes across 82 tissues and 924 housekeeping genes. The distributions of motifs in different promoter regions show that most motifs prefer to be in the proximal region (+500~50bp, -50bp~-500bp) of promoters.
{"title":"A novel pipeline for motif discovery, pruning and validation in promoter sequences of human tissue specific genes","authors":"X. Gong, Hua Yu, F. Zhao","doi":"10.1109/ISB.2012.6314111","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314111","url":null,"abstract":"Identification and analysis of tissue-specific (TS) genes and their regulatory activities play an important role in the understanding of mechanisms of organisms, disease diagnosis and drug design. In this paper, we designed a pipeline for the discovery of promoter motifs for tissue-specific genes. The pipeline consists of three phases: motif searching, motif merging and motif validation. The motif searching phase integrated three algorithms: MEME, AlignACE and Gibbs Sampling. In the second phase, we proposed a motif merging method, which is based on Bayesian probabilistic principles, to reduce redundancies of motifs from the first phase. Lastly, the motif validation phase verified the statistical significance of discovered motifs using a Bayesian Hypothesis Test approach. We performed the analysis on the sequences of promoter regions (-449bp-1000bp) of 4,552 human tissue-specific genes across 82 tissues and 924 housekeeping genes. The distributions of motifs in different promoter regions show that most motifs prefer to be in the proximal region (+500~50bp, -50bp~-500bp) of promoters.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127619193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314157
Fen Kong, Xuying Nan, P. He, Qi Dai, Yu-Hua Yao
A 2-D graphical representation of protein sequences based on two classifications of amino acids is outlined. The method of dividing a long sequence into k segments (SSM) is introduced, so protein graph is divided into k segments, geometrical center of the points for all protein curve segment is given as descriptors of protein sequences. It is not only useful for comparative study of proteins, but also for encoding innate information about the structure of proteins. Finally, a simple example is taken to highlight the behavior of the new descriptor on protein sequences taken from the 12 baculoviruse proteins.
{"title":"A sequence-segmented method applied to the similarity analysis of proteins","authors":"Fen Kong, Xuying Nan, P. He, Qi Dai, Yu-Hua Yao","doi":"10.1109/ISB.2012.6314157","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314157","url":null,"abstract":"A 2-D graphical representation of protein sequences based on two classifications of amino acids is outlined. The method of dividing a long sequence into k segments (SSM) is introduced, so protein graph is divided into k segments, geometrical center of the points for all protein curve segment is given as descriptors of protein sequences. It is not only useful for comparative study of proteins, but also for encoding innate information about the structure of proteins. Finally, a simple example is taken to highlight the behavior of the new descriptor on protein sequences taken from the 12 baculoviruse proteins.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314123
Shuqin Zhang
Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in different kinds of biological networks. Compared to the module identification methods, less research is done on the hierarchical structure of modules. In this paper, we propose a method for constructing the hierarchical modular structure in networks based on the extended random graph model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene co-expression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.
{"title":"Hierarchical modular structure in gene coexpression networks","authors":"Shuqin Zhang","doi":"10.1109/ISB.2012.6314123","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314123","url":null,"abstract":"Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in different kinds of biological networks. Compared to the module identification methods, less research is done on the hierarchical structure of modules. In this paper, we propose a method for constructing the hierarchical modular structure in networks based on the extended random graph model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene co-expression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"65 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115740127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314130
Xi Liu, Peipei Zhou, Ruiqi Wang
Quorum sensing (QS) is a mechanism by which bacteria produce, release, and then detect and respond to biosignals called autoinducers (AIs). There are multiple feedback loops in the QS system of Vibrio harveyi. However, how these feedback loops function to control signal processing remains unclear. In this paper, we present a computational model for switch-like regulation of signal transduction by small regulatory RNA-mediated QS based on intertwined network involving AIs, LuxO, LuxU, Qrr sRNAs, and LuxR. In agreement with experimental observations, the model suggests that different feedbacks play critical roles in the switch-like regulation. Our results reveal that Vibrio harveyi uses multiple feedbacks to precisely control signal transduction.
{"title":"Switch-like regulation of signal transduction by small RNA-mediated quorum sensing","authors":"Xi Liu, Peipei Zhou, Ruiqi Wang","doi":"10.1109/ISB.2012.6314130","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314130","url":null,"abstract":"Quorum sensing (QS) is a mechanism by which bacteria produce, release, and then detect and respond to biosignals called autoinducers (AIs). There are multiple feedback loops in the QS system of Vibrio harveyi. However, how these feedback loops function to control signal processing remains unclear. In this paper, we present a computational model for switch-like regulation of signal transduction by small regulatory RNA-mediated QS based on intertwined network involving AIs, LuxO, LuxU, Qrr sRNAs, and LuxR. In agreement with experimental observations, the model suggests that different feedbacks play critical roles in the switch-like regulation. Our results reveal that Vibrio harveyi uses multiple feedbacks to precisely control signal transduction.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314158
Yang Liu, Jin Zhou, Zhiping Liu, Luonan Chen, M. Ng
The study of gene regulatory network (GRN) and protein protein interaction network (PPI) is believed to be fundamental to the understanding of molecular processes and functions in system biology and therefore, attracted more and more attentions in past few years. However, there is little focus about network construction in single nucleotide polymorphism (SNP) level, which may provide a direct insight into mutations among individuals, potentially leading to new pathogenesis discovery and diagnostics. In this paper, we present a novel method to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. Specifically, based on logistic regression between two SNPs, we first construct a genome-wide SNP-SNP interaction network. Then by using gene information, selected SNP seeds are employed to detect SNP sub-networks with a maximal modularity. Finally to identify functional role of each SNP sub-network, its gene association network is constructed and their functional similarity values are calculated to show the biological relevance. Results show that our method is effective in SNP sub-network extraction and gene function prediction.
{"title":"Construction and analysis of genome-wide SNP networks","authors":"Yang Liu, Jin Zhou, Zhiping Liu, Luonan Chen, M. Ng","doi":"10.1109/ISB.2012.6314158","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314158","url":null,"abstract":"The study of gene regulatory network (GRN) and protein protein interaction network (PPI) is believed to be fundamental to the understanding of molecular processes and functions in system biology and therefore, attracted more and more attentions in past few years. However, there is little focus about network construction in single nucleotide polymorphism (SNP) level, which may provide a direct insight into mutations among individuals, potentially leading to new pathogenesis discovery and diagnostics. In this paper, we present a novel method to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. Specifically, based on logistic regression between two SNPs, we first construct a genome-wide SNP-SNP interaction network. Then by using gene information, selected SNP seeds are employed to detect SNP sub-networks with a maximal modularity. Finally to identify functional role of each SNP sub-network, its gene association network is constructed and their functional similarity values are calculated to show the biological relevance. Results show that our method is effective in SNP sub-network extraction and gene function prediction.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115317229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314106
Peng Xu, Xianghong Wang, Wenbin Liu
It has been known for quite some time that the 1 / f dynamics play a vital role in living organisms. Recently we studied the long-range correlated dynamics of Boolean networks, and found that some networks could present the 1 / f dynamics while others couldn't. An important question is what kind of networks can generate such dynamics? In this paper, we investigate this issue based on the attractor structure of Boolean networks. We find that multiple attractor networks prefer to generate the 1 / f dynamics and systems with large basin entropy tend to sustain such dynamics in a wide noise range. Models for eight real genetic networks also partially support these observations.
{"title":"The influence of the basin structure of Boolean networks on their long range correlated dynamics","authors":"Peng Xu, Xianghong Wang, Wenbin Liu","doi":"10.1109/ISB.2012.6314106","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314106","url":null,"abstract":"It has been known for quite some time that the 1 / f dynamics play a vital role in living organisms. Recently we studied the long-range correlated dynamics of Boolean networks, and found that some networks could present the 1 / f dynamics while others couldn't. An important question is what kind of networks can generate such dynamics? In this paper, we investigate this issue based on the attractor structure of Boolean networks. We find that multiple attractor networks prefer to generate the 1 / f dynamics and systems with large basin entropy tend to sustain such dynamics in a wide noise range. Models for eight real genetic networks also partially support these observations.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127795130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314137
Yuan Yi, J. Guan, Shuigeng Zhou
MicroRNA (miRNA in short) is a kind of small RNAs that acts as an important post-transcriptional regulator with the Argonaute family of proteins to regulate target mRNAs in animals and plants etc. Since its first recognition as a distinct class of small RNA molecules in the early 1990s, tens of thousands of miRNAs have been identified experimentally or computationally. Currently, the focus of miRNAs study is on single-miRNA functions that usually result in gene silencing and repression. With the rapid increase of miRNAs, biologists have manually organized these miRNAs into biologically meaningful families to facilitate further study. As the members in the same family tend to share similar biochemical functions, a high quality family organization will shed lights on the functions of unknown miRNAs. However, manually grouping large amounts of miRNAs is not only time-consuming but also expensive. In this paper, we employ a clustering method with N-grams and feature weighting to automatically group miRNAs into separate clusters (families). Our method is evaluated with datasets constructed from the online miRNA database miRBase. Experimental results show that the clustering method can successfully distinguishes most miRNA families, and outperforms the traditional K-means clustering algorithm and the average-link clustering approach.
{"title":"Effective clustering of microRNA sequences by N-grams and feature weighting","authors":"Yuan Yi, J. Guan, Shuigeng Zhou","doi":"10.1109/ISB.2012.6314137","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314137","url":null,"abstract":"MicroRNA (miRNA in short) is a kind of small RNAs that acts as an important post-transcriptional regulator with the Argonaute family of proteins to regulate target mRNAs in animals and plants etc. Since its first recognition as a distinct class of small RNA molecules in the early 1990s, tens of thousands of miRNAs have been identified experimentally or computationally. Currently, the focus of miRNAs study is on single-miRNA functions that usually result in gene silencing and repression. With the rapid increase of miRNAs, biologists have manually organized these miRNAs into biologically meaningful families to facilitate further study. As the members in the same family tend to share similar biochemical functions, a high quality family organization will shed lights on the functions of unknown miRNAs. However, manually grouping large amounts of miRNAs is not only time-consuming but also expensive. In this paper, we employ a clustering method with N-grams and feature weighting to automatically group miRNAs into separate clusters (families). Our method is evaluated with datasets constructed from the online miRNA database miRBase. Experimental results show that the clustering method can successfully distinguishes most miRNA families, and outperforms the traditional K-means clustering algorithm and the average-link clustering approach.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114645621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314151
Zhiyuan Yang, Yan Zhang, Luonan Chen
The naked mole rat (NMR, Heterocephalus glaber) is a long-lived underground mammal, whose maximum lifespan can be up to 30 years and more than 7 times longer than house mouse. In addition, they are resistant to both spontaneous and experimentally induced tumorigenesis. These special biologic or behavioral characteristics make them most suitable for cancer and longevity research. The recent genome sequencing of NMR has provided the opportunity for the study of molecular mechanisms of such extreme traits. In this study, we carried out a comparative analysis of the complete set of NMR and rat genes. First, we identified all orthologous genes shared between these two animals. We further focused on the rat genes that were absent in NMR and used KEGG pathway database to identify the biological meaning of their proteins. The top three pathways include “Cytokine-cytokine receptor interaction”, “Neuroactive ligand-receptor interaction” and “Pathways in cancer”, which was consistent with the unique NMR traits. Interestingly, in the rat cancer pathway which contains 13 paths leading to evading apoptosis, 8 of them appeared to be interrupted in NMR. Finally, we found that 50% of genes lacked in “Pathways in cancer” and 40% of genes lacked in “MAPK signaling pathway” have been known to be related to a variety of cancers. Overall, this study provides insights into searching for new cancer-related genes and understanding the anti-cancer mechanism of NMR.
裸鼹鼠(NMR, Heterocephalus glaber)是一种长寿的地下哺乳动物，其最长寿命可达30年，比家鼠长7倍以上。此外，它们对自发和实验诱导的肿瘤发生都有抵抗力。这些特殊的生物或行为特征使它们最适合癌症和长寿研究。最近的核磁共振基因组测序为研究这些极端性状的分子机制提供了机会。在这项研究中，我们对整套核磁共振和大鼠基因进行了比较分析。首先，我们确定了这两只动物之间共享的所有同源基因。我们进一步关注NMR中缺失的大鼠基因，并使用KEGG通路数据库确定其蛋白的生物学意义。排在前三位的途径包括“细胞因子-细胞因子受体相互作用”、“神经活性配体-受体相互作用”和“癌症途径”，这与其独特的NMR特征相一致。有趣的是，在包含13条逃避细胞凋亡途径的大鼠癌症途径中，有8条在NMR中被中断。最后，我们发现50%的“Pathways in cancer”缺失基因和40%的“MAPK signaling pathway”缺失基因与多种癌症相关。总的来说，本研究为寻找新的癌症相关基因和了解NMR的抗癌机制提供了新的见解。
{"title":"in silico identification of novel cancer-related genes by comparative genomics of naked mole rat and rat","authors":"Zhiyuan Yang, Yan Zhang, Luonan Chen","doi":"10.1109/ISB.2012.6314151","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314151","url":null,"abstract":"The naked mole rat (NMR, Heterocephalus glaber) is a long-lived underground mammal, whose maximum lifespan can be up to 30 years and more than 7 times longer than house mouse. In addition, they are resistant to both spontaneous and experimentally induced tumorigenesis. These special biologic or behavioral characteristics make them most suitable for cancer and longevity research. The recent genome sequencing of NMR has provided the opportunity for the study of molecular mechanisms of such extreme traits. In this study, we carried out a comparative analysis of the complete set of NMR and rat genes. First, we identified all orthologous genes shared between these two animals. We further focused on the rat genes that were absent in NMR and used KEGG pathway database to identify the biological meaning of their proteins. The top three pathways include “Cytokine-cytokine receptor interaction”, “Neuroactive ligand-receptor interaction” and “Pathways in cancer”, which was consistent with the unique NMR traits. Interestingly, in the rat cancer pathway which contains 13 paths leading to evading apoptosis, 8 of them appeared to be interrupted in NMR. Finally, we found that 50% of genes lacked in “Pathways in cancer” and 40% of genes lacked in “MAPK signaling pathway” have been known to be related to a variety of cancers. Overall, this study provides insights into searching for new cancer-related genes and understanding the anti-cancer mechanism of NMR.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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