IPSJ Transactions on Bioinformatics最新文献_第4页

In silico Spleen Tyrosine Kinase Inhibitor Screening by ChooseLD 筛选脾脏酪氨酸激酶抑制剂

IPSJ Transactions on Bioinformatics Pub Date : 2015-03-13 DOI: 10.2197/IPSJTBIO.8.14

H. Umeyama, M. Iwadate, Y-h. Taguchi

{"title":"In silico Spleen Tyrosine Kinase Inhibitor Screening by ChooseLD","authors":"H. Umeyama, M. Iwadate, Y-h. Taguchi","doi":"10.2197/IPSJTBIO.8.14","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.8.14","url":null,"abstract":"Background: Spleen tyrosine kinase (SYK) is a protein related to various diseases. Aberrant SYK expression often causes the progression and initiation of several diseases including cancer and autoimmune diseases. Despite the importance of inhibiting SYK and identifying candidate inhibitors, no clinically effective inhibitors have been reported to date. Therefore, there is a need for novel SYK inhibitors. Results: Candidate compounds were investigated using in silico screening by chooseLD, which simulates ligand docking to proteins. Using this system, known inhibitors were correctly recognized as compounds with high affinity to SYK. Furthermore, many compounds in the DrugBank database were newly identified as having high affinity to the ATP-binding sites in the kinase domain with a similar affinity to previously reported inhibitors. Conclusions: Many drug candidate compounds from the DrugBank database were newly identified as inhibitors of SYK. Because compounds registered in the DrugBank are expected to have fewer side effects than currently available compounds, these newly identified compounds may be clinically useful inhibitors of SYK for the treatment of various diseases.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.8.14","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502943","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: 0

A Memory Efficient Short Read De Novo Assembly Algorithm 一种内存高效的短读从头组装算法

IPSJ Transactions on Bioinformatics Pub Date : 2015-01-01 DOI: 10.2197/IPSJTBIO.8.2

Yuki Endo, Fubito Toyama, C. Chiba, H. Mori, K. Shoji

{"title":"A Memory Efficient Short Read De Novo Assembly Algorithm","authors":"Yuki Endo, Fubito Toyama, C. Chiba, H. Mori, K. Shoji","doi":"10.2197/IPSJTBIO.8.2","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.8.2","url":null,"abstract":": Sequencing the whole genome of various species has many applications, not only in understanding bio- logical systems, but also in medicine, pharmacy, and agriculture. In recent years, the emergence of high-throughput next generation sequencing technologies has dramatically reduced the time and costs for whole genome sequencing. These new technologies provide ultrahigh throughput with a lower per-unit data cost. However, the data are generated from very short fragments of DNA. Thus, it is very important to develop algorithms for merging these fragments. One method of merging these fragments without using a reference dataset is called de novo assembly. Many algorithms for de novo assembly have been proposed in recent years. Velvet and SOAPdenovo2 are well-known assembly algorithms, which have good performance in terms of memory and time consumption. However, memory consumption increases dramatically when the size of input fragments is larger. Therefore, it is necessary to develop an alternative algorithm with low memory usage. In this paper, we propose an algorithm for de novo assembly with lower memory. In our experiments using E.coli K-12 strain MG 1655 and human chromosome 14, the memory consumption of our proposed algorithm was less than that of other popular assemblers.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.8.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502970","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: 0

Bacterial Type III Secretion System Effector Proteins are Distinct between Plant Symbiotic, Plant Pathogenic and Animal Pathogenic Bacteria 细菌III型分泌系统效应蛋白区别于植物共生菌、植物致病菌和动物致病菌

IPSJ Transactions on Bioinformatics Pub Date : 2014-01-01 DOI: 10.2197/IPSJTBIO.7.2

Yuuichi Nakano, M. Iwadate, H. Umeyama, Y-h. Taguchi

引用次数: 1

An Effective Method for the Inference of Reduced S-system Models of Genetic Networks 遗传网络简化s系统模型的一种有效推理方法

IPSJ Transactions on Bioinformatics Pub Date : 2014-01-01 DOI: 10.2197/IPSJTBIO.7.30

Shuhei Kimura, Masanao Sato, M. Okada‐Hatakeyama

{"title":"An Effective Method for the Inference of Reduced S-system Models of Genetic Networks","authors":"Shuhei Kimura, Masanao Sato, M. Okada‐Hatakeyama","doi":"10.2197/IPSJTBIO.7.30","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.7.30","url":null,"abstract":"The inference of genetic networks is a problem to obtain mathematical models that can explain observed time-series of gene expression levels. A number of models have been proposed to describe genetic networks. The S-system model is one of the most studied models among them. Due to its advantageous features, numerous inference algorithms based on the S-system model have been proposed. The number of the parameters in the S-system model is however larger than those of the other well-studied models. Therefore, when trying to infer S-system models of genetic networks, we need to provide a larger amount of gene expression data to the inference method. In order to reduce the amount of gene expression data required for an inference of genetic networks, this study simplifies the S-system model by fixing some of its parameters to 0. In this study, we call this simplified S-system model a reduced S-system model. We then propose a new inference method that estimates the parameters of the reduced S-system model by minimizing two-dimensional functions. Finally, we check the effectiveness of the proposed method through numerical experiments on artificial and actual genetic network inference problems.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.7.30","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503380","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: 5

Signal Processing Algorithm Development for Mass++ (Ver. 2): Platform Software for Mass Spectrometry mass++信号处理算法开发(版本2):质谱分析平台软件

IPSJ Transactions on Bioinformatics Pub Date : 2014-01-01 DOI: 10.2197/IPSJTBIO.7.24

Shin-ichi Utsunomiya, Yuichiro Fujita, Satoshi Tanaka, Shigeki Kajihara, K. Aoshima, Y. Oda, Koichi Tanaka

引用次数: 6

A novel evaluation measure for identifying drug targets from the biomedical literature 从生物医学文献中识别药物靶点的一种新的评价方法

IPSJ Transactions on Bioinformatics Pub Date : 2014-01-01 DOI: 10.2197/IPSJTBIO.7.16

Yeondae Kwon, Shogo Shimizu, H. Sugawara, S. Miyazaki

{"title":"A novel evaluation measure for identifying drug targets from the biomedical literature","authors":"Yeondae Kwon, Shogo Shimizu, H. Sugawara, S. Miyazaki","doi":"10.2197/IPSJTBIO.7.16","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.7.16","url":null,"abstract":"Identification of candidate target genes related to a particular disease is an important stage in drug development. A number of studies have extracted disease-related genes from the biomedical literature. We herein present a novel evaluation measure that identifies disease-associated genes and prioritizes the identified genes as drug target genes in terms of fewer side-effects using the biomedical literature. The proposed measure evaluates the specificity of a gene to a particular disease based on the number of diseases associated with the gene. The specificity of a gene is measured by means of, for example, term frequency-inverse document frequency (tf-idf), which is widely used in Web information retrieval. We assume that if a gene is chosen as a target gene for a disease, then side-effects are more likely to occur as the number of diseases associated with the gene increases. We verified the obtained ranking results by checking the ranks of known drug targets. As a result, 177 known drug targets were found to be ranked within the top 100 genes, and 21 drug targets were top ranked. The results suggest that the proposed measure is useful as a primary filter for extracting candidate target genes from a large number of genes.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.7.16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503185","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

NegFinder: A Web Service for Identifying Negation Signals and Their Scopes NegFinder:一个用于识别否定信号及其作用域的Web服务

IPSJ Transactions on Bioinformatics Pub Date : 2013-06-20 DOI: 10.2197/IPSJTBIO.6.29

Kazuki Fujikawa, Kazuhiro Seki, K. Uehara

引用次数: 3

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction 基于序列的配体结合位点预测方法

IPSJ Transactions on Bioinformatics Pub Date : 2013-06-01 DOI: 10.2197/IPSJTBIO.6.35

Chun Fang, T. Noguchi, H. Yamana

{"title":"SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction","authors":"Chun Fang, T. Noguchi, H. Yamana","doi":"10.2197/IPSJTBIO.6.35","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.6.35","url":null,"abstract":"In this paper, we propose a novel method, named SCPSSMpred (Smoothed and Condensed PSSM based prediction), which uses a simplified position-specific scoring matrix (PSSM) for predicting ligand-binding sites. Although the simplified PSSM has only ten dimensions, it combines abundant features, such as amino acid arrangement, information of neighboring residues, physicochemical properties, and evolutionary information. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the sequences only. Three ligands (FAD, NAD and ATP) were used to verify the versatility of our method, and three alternative traditional methods were also analyzed for comparison. All the methods were tested at both the residue level and the protein sequence level. Experimental results showed that the SCPSSMpred method achieved the best performance besides reducing 50% of redundant features in PSSM. In addition, it showed a remarkable adaptability in dealing with unbalanced data compared to other methods when tested on the protein sequence level. This study not only demonstrates the importance of reducing redundant features in PSSM, but also identifies sequence-derived hallmarks of ligand-binding sites, such that both the arrangements and physicochemical properties of neighboring residues significantly impact ligand-binding behavior *1.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.6.35","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502679","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

Improved Protein-ligand Prediction Using Kernel Weighted Canonical Correlation Analysis 基于核加权典型相关分析的改进蛋白质配体预测

IPSJ Transactions on Bioinformatics Pub Date : 2013-01-01 DOI: 10.2197/IPSJTBIO.6.18

Raissa Relator, Tsuyoshi Kato, Richard S. Lemence

{"title":"Improved Protein-ligand Prediction Using Kernel Weighted Canonical Correlation Analysis","authors":"Raissa Relator, Tsuyoshi Kato, Richard S. Lemence","doi":"10.2197/IPSJTBIO.6.18","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.6.18","url":null,"abstract":"Protein-ligand interaction prediction plays an important role in drug design and discovery. However, wet lab procedures are inherently time consuming and expensive due to the vast number of candidate compounds and target genes. Hence, computational approaches became imperative and have become popular due to their promising results and practicality. Such methods require high accuracy and precision outputs for them to be useful, thus, the problem of devising such an algorithm remains very challenging. In this paper we propose an algorithm employing both support vector machines (SVM) and an extension of canonical correlation analysis (CCA). Following assumptions of recent chemogenomic approaches, we explore the effects of incorporating bias on similarity of compounds. We introduce kernel weighted CCA as a means of uncovering any underlying relationship between similarity of ligands and known ligands of target proteins. Experimental results indicate statistically significant improvement in the area under the ROC curve (AUC) and F-measure values obtained as opposed to those gathered when only SVM, or SVM with kernel CCA is employed, which translates to better quality of prediction.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.6.18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68502929","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

Protein Experimental Information Management System (PREIMS) Based on Ontology: Development and Applications 基于本体的蛋白质实验信息管理系统(PREIMS)的开发与应用

IPSJ Transactions on Bioinformatics Pub Date : 2013-01-01 DOI: 10.2197/IPSJTBIO.6.9

Junko Sato, Kouji Kozaki, Susumu Handa, Takashi Ikeda, Ryotaro Saka, K. Tomizuka, Y. Nishiyama, Toshiyuki Okumura, S. Hirai, Tadashi Ohno, Mamoru Ohta, S. Date, Haruki Nakamura

引用次数: 0