2016 International Conference on Bioinformatics and Systems Biology (BSB)最新文献

Extraction of associated quantitative traits by association mining 基于关联挖掘的关联数量性状提取

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-08-25 DOI: 10.1109/BSB.2016.7552124

Kalpana Singh, Manish Kumar, Sekhar Verma

引用次数: 0

Exploring the role of EZH2 (PRC2) as epigenetic target 探讨EZH2 (PRC2)作为表观遗传靶点的作用

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-08-25 DOI: 10.1109/BSB.2016.7552131

Imlimaong Aier, Utkarsh Raj

引用次数: 1

Prediction of catalytic site of proteins based on amino acid triads approach using non parametric function 基于非参数函数的氨基酸三联体方法预测蛋白质的催化位点

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-08-25 DOI: 10.1109/BSB.2016.7552137

S. Srivastava, Gautam Kumar, Tapobarata Lahiri, Rajnish Kumar, Manoj Kumar Pal, Pragya Gupta, Rahul Gupta

{"title":"Prediction of catalytic site of proteins based on amino acid triads approach using non parametric function","authors":"S. Srivastava, Gautam Kumar, Tapobarata Lahiri, Rajnish Kumar, Manoj Kumar Pal, Pragya Gupta, Rahul Gupta","doi":"10.1109/BSB.2016.7552137","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552137","url":null,"abstract":"In this study, we present a method for the catalytic site prediction of proteins rest on the triads of amino acids residues using non parametric function - artificial neural network. Using this method, we can efficiently predict that whether the amino acid triads of a protein are the part of catalytic site or not. For the preparation of training and test datasets, catalytic site residues of protein are downloaded from the database of catalytic site atlas and residues for non catalytic site are taken which are not participating in the formation of catalytic site of protein. This method used the numerical value of six physiochemical properties of amino acids along with the difference between centers of mass of whole protein and amino acids triads as the input for the neural network. Our analysis shows that this method is worked with the efficiency of 83.66% which is higher than other existing model for the prediction of catalytic site of protein. Our analysis is based on the residues physiochemical and topological properties and not on the evolutionary and sequence similarities so, In future, this work may help the researchers to develop tool and predicting the nature of residues of catalytic or active site of protein and may be helpful in ligand designing and molecular docking.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132141307","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}

引用次数: 1

Identification of conserved regulatory motif signatures in human miRNA upstream regions 人类miRNA上游区域保守调控基序特征的鉴定

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-08-25 DOI: 10.1109/BSB.2016.7552128

Pankaj Kumar Singh, Jayita Roy, S. Ganguli, P. Basu, V. Vishal, Abhisek Ranjan Bera, Abhaydeep Pandey, R. Banik

引用次数: 0

Improving extraction of protein — Protein interaction datasets from KUPS using hashing approach 使用哈希方法改进从up中提取蛋白质-蛋白质相互作用数据集

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-08-25 DOI: 10.1109/BSB.2016.7552135

Gautam Kumar, Rajnish Kumar, Manoj Kumar Pal, Pragya Gupta, Rahul Gupta, S. Mehra

引用次数: 0

Statistical discrimination of breast cancer microarray data 乳腺癌微阵列数据的统计鉴别

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-08-25 DOI: 10.1109/BSB.2016.7552136

Gautam Kumar, Tapobarata Lahiri, Rajnish Kumar

{"title":"Statistical discrimination of breast cancer microarray data","authors":"Gautam Kumar, Tapobarata Lahiri, Rajnish Kumar","doi":"10.1109/BSB.2016.7552136","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552136","url":null,"abstract":"Recently published literatures indicate that, a consistent and precise classification system of microarray data is very essential for the successful identification of genes responsible for a cancer subtype and early treatment of cancer. However, in common clinical practice, diagnostic assertion of malignancies mostly relies on the morphological examination of tissues and clinical tests. In spite of the recent progress of treatment that uses semiempirical approaches involving computation methods, there are still uncertainties in cancer identification and diagnosis. High density oligonucleotide chips and genomic microarray data are being used progressively more in cancer research by generating huge expression data for thousands of genes simultaneously that poses problem of mining specific genes responsible for characterization of a specific cancer subtype. In this backdrop, we briefly address the impact of various statistical methods and their relative performances for the identification of potential probes and Discriminant genes for the breast cancer. We used Fisher's Discriminant Ratio (FDR), two tailed T-Test and vector norm on raw expression data and for each of the probe generated from the difference data accounting for up and down regulation of expression for each probes for various samples. The result indicates the potential of these methods to identify genes responsible for manifestation of breast cancer which is also well supported by the published result of experiments. The success of this approach not only gives the benefit of identification of cancer specific genes but also may help building of efficient classifier on the basis of these genes for automatic diagnostics of cancer.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114148644","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}

引用次数: 3

Molecular modeling and dynamics study of nonsynonymous SNP in bread wheat HSP16.9B gene 面包小麦HSP16.9B基因非同义SNP的分子模拟与动力学研究

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-03-04 DOI: 10.1109/BSB.2016.7552123

B. Pandey, Saurabh Gupta, Atmakuri Ramakrishna Rao, D. M. Pandey, R. Chatrath

引用次数: 1

Antimicrobial peptide Microcin C7 as an alternative drug candidate against Diphtheria toxin 抗菌肽Microcin C7作为抗白喉毒素的候选药物

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-03-04 DOI: 10.1109/BSB.2016.7552140

S. Ojha, Kanika Kundu, S. Kundu

引用次数: 0

Genome-wide analysis and identification of abiotic stress responsive transcription factor family genes and miRNAs in bread wheat (Triticumaestivum L.): Genomic study of bread wheat 面包小麦(Triticumaestivum L.)非生物胁迫应答转录因子家族基因和mirna的全基因组分析与鉴定:面包小麦的基因组研究

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-03-04 DOI: 10.1109/BSB.2016.7552159

Sarika Sahu, A. Rao, K. C. Bansal, S. Muthusamy, V. Chinnusamy

{"title":"Genome-wide analysis and identification of abiotic stress responsive transcription factor family genes and miRNAs in bread wheat (Triticumaestivum L.): Genomic study of bread wheat","authors":"Sarika Sahu, A. Rao, K. C. Bansal, S. Muthusamy, V. Chinnusamy","doi":"10.1109/BSB.2016.7552159","DOIUrl":"https://doi.org/10.1109/BSB.2016.7552159","url":null,"abstract":"Transcription factors (TFs) act as master regulators that directly bind to their respective distinct cis-regulatory elements and activate the expression of many downstream target genes (regulon), and thus play a key regulatory role in plant development and stress tolerance. TF families such as AP2/EREBP, AREB/ABF, bHLH, bZIP, C2H2, C3HIS, HB, DREB1/CBF, HSF, MADS, MYB, MYC, NAC, WRKY, etc., were known to regulate stress responses of plants and were relatively well studied in rice and Arabidopsis. Bread wheat (Triticumaestivum L) draft genome is recently released and is available in Ensembl Plants database. We used known rice TFs and build Hidden Markov Model (HMM) profiles for individual TF protein families. These Profile HMMs in turn were used to search respective wheat homologs. SMART tool was used for domain identification. Our analysis showed that the wheat genome consists of 201, 166, 265, 182, 200, 102, 200, 274, 54, 125, 315, 226 and 199 genes of AP2/EREBP, AREB/ABF, bHLH, bZIP, C2H2, C3HIS, HB, HIS, HSF, MADS, MYB, NAC and WRKY families, respectively. Genome-wide analysis of miRNAs from wheat genome resulted in identification of 4533 miRNAs from wheat. Further, miRNAs targeting abiotic stress responsive TFs is identified. The genome distribution of abiotic stress responsive TFs and miRNAs strongly supports the hypothesis that genome-wide and tandem duplication contributed to the expansion of these gene families in wheat.","PeriodicalId":363820,"journal":{"name":"2016 International Conference on Bioinformatics and Systems Biology (BSB)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127135957","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}

引用次数: 2

Identification of co-regulated genes of chick pea under abiotic stress 非生物胁迫下鹰嘴豆共调控基因的鉴定

2016 International Conference on Bioinformatics and Systems Biology (BSB) Pub Date : 2016-03-04 DOI: 10.1109/BSB.2016.7552156

D. Mishra, S. Mittal, I. Singh, Sanjeev Kumar, A. Rai

引用次数: 0