Journal of Computer Aided Chemistry最新文献_第4页

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Artificial Intelligence, Knowledge Discovery and Data Mining Thirty Years of Experience in Cheminformatics 【献给冈田教授和西冈教授:化学中的数据科学】人工智能、知识发现和数据挖掘化学信息学三十年的经验

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.3

T. Okada

引用次数: 0

＜岡田孝先生・西岡孝明先生の退職記念号：化学データサイエンス＞Bayes ANOVA を用いた Euglena gracilis の代謝経路の網羅的発現変動解析《冈田孝、西冈孝明先生的退休纪念号:化学数据科学》利用Bayes ANOVA分析Euglena gracilis代谢路径的全面性发现变化

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.110

直亮小野, 直己横山, Md.Altuf-Ul Amin, 雅俊中本, 大策太田

引用次数: 0

Improvement of Pseudo-molecule Generation on Solvent Dipole Ordering Virtual Screening (SDO-VS) 溶剂偶极子有序虚拟筛选(SDO-VS)伪分子生成的改进

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.149

Shinya Nakamura, Hayao Kitayoshi, I. Nakanishi

引用次数: 0

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Centrality Values of Yeast Proteins in a PPI Network Are Related to Their Essentiality and Functions [献给T. Okada教授和T. Nishioka教授:化学中的数据科学]酵母蛋白在PPI网络中的中心性值与其本质和功能相关

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.94

M. Altaf-Ul-Amin, S. Wijaya, D. Chandra, S. Kanaya

{"title":"[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Centrality Values of Yeast Proteins in a PPI Network Are Related to Their Essentiality and Functions","authors":"M. Altaf-Ul-Amin, S. Wijaya, D. Chandra, S. Kanaya","doi":"10.2751/JCAC.18.94","DOIUrl":"https://doi.org/10.2751/JCAC.18.94","url":null,"abstract":"It has long been investigated and understood that centrality of proteins in the context of protein-protein interaction (PPI) networks are related to their essentiality. In the present work, we validate the relations between essentiality of yeast proteins and their centrality measures in a PPI network by following a different approach using the concept of the receiver operating characteristic (ROC) curve. We found that all centrality measures are related to essentiality. However, the degree centrality performed better in case of the data we used. By deeply examining different centrality values of yeast proteins we find that they are not highly correlated, which has leaded us to hypothesize that centralities might have some relations with gene/protein functions. Indeed, we found that many of the clusters generated based on the pattern of centrality values are rich with similar function proteins. Different types of centrality values imply different types of importance of a node in a network and the functions of genes are of various types. In the present work, we hypothesized that important genes of different functions may tend to show different patterns of centralities and here we show some preliminary links between groups of similar function genes and profiles of centrality values. The concepts of network biology discussed in this paper are applicable to other networks including networks of chemical compounds.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"18 1","pages":"94-109"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2751/JCAC.18.94","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69256094","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

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Chemical Annotation of ESI-MS/MS Spectral Data 【献给冈田教授和西冈教授:化学中的数据科学】ESI-MS/MS光谱数据的化学注释

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.15

T. Nishioka, H. Horai

引用次数: 0

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Classification of Alkaloid Compounds Based on Subring Skeleton (SRS) Profiling: On Finding Relationship of Compounds with Metabolic Pathways [献给T. Okada教授和T. Nishioka教授:化学中的数据科学]基于subbring Skeleton (SRS) Profiling的生物碱化合物分类:寻找化合物与代谢途径的关系

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.58

Ryohei Eguchi, N. Ono, H. Horai, Md. Altuf-Ul Amin, Aki Hirai, J. Kawahara, S. Kasahara, Tomoaki Endo, S. Kanaya

{"title":"[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Classification of Alkaloid Compounds Based on Subring Skeleton (SRS) Profiling: On Finding Relationship of Compounds with Metabolic Pathways","authors":"Ryohei Eguchi, N. Ono, H. Horai, Md. Altuf-Ul Amin, Aki Hirai, J. Kawahara, S. Kasahara, Tomoaki Endo, S. Kanaya","doi":"10.2751/JCAC.18.58","DOIUrl":"https://doi.org/10.2751/JCAC.18.58","url":null,"abstract":"Systematic representation of alkaloid biosynthetic pathways based on ring skeletons has been proposed because the skeleton nucleus of an alkaloid is the main criterion for determination in biosynthetic pathways. So the idea of ring skeletons was extended to apply classification of alkaloid compounds based on ring skeletons and to systematize alkaloid compounds and to examine the performance of this approach to predict biosynthetic pathways based on module elements. We constructed a 2-dimensional binary matrix corresponding to 2546 SRS and 478 pathway-known alkaloid compounds. Here, if ith substring skeleton is present in a target compound, the ith element was set to 1; otherwise, the ith element was set to 0. Relationship of alkaloid compounds with biosynthetic pathways are examined based on the dendrogram produced by Ward clustering method to the matrix. Of 12,243 alkaloid compounds accumulated in KNApSAcK Core DB (http://kanaya.naist.jp/knapsack_jsp/top.html), 3,124 compounds (25.5 %) correspond to the pathway-known ring skeletons (187 ring skeletons), but the remaining 9,119 (74.5%) compounds do not. By examining the sub-ring skeleton similarity of the remaining compounds, it might be possible to obtain clues of pathway information and systemization of all alkaloid compounds. Therefore, the present work focuses on comprehensive systematization of the alkaloid compounds and construction principles of ring skeletons in alkaloids based on subring skeleton profiling.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"18 1","pages":"58-75"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2751/JCAC.18.58","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255491","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: 4

＜岡田孝先生・西岡孝明先生の退職記念号：化学データサイエンス＞巻頭言：岡田孝先生、西岡孝明先生の退職特別企画：ケモインフォマティクスのデータ・サイエンスとしての広がり《冈田孝老师、西冈孝明老师的退休纪念号:化学数据科学》卷首语:冈田孝老师、西冈孝明老师的退休特别企划:momo infortics作为数据科学的扩展

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/jcac.18.1

重彦金谷

引用次数: 0

A Preliminary Study of Correction for Inter Fragment Interaction Energy (IFIE) between Fragments Sharing Bond Detached Atom (BDA) 共用键分离原子(BDA)碎片间相互作用能(IFIE)修正的初步研究

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.143

T. Nakano, Yuji Mochidzuki, Kaori Fukuzawa, Yoshio Okiyama, C. Watanabe

引用次数: 2

[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]The Contribution of Lipid Identification Tools Powered by In Silico MS/MS Spectral Libraries to Lipidomics [献给T. Okada教授和T. Nishioka教授:化学中的数据科学]基于硅质谱库的脂质鉴定工具对脂质组学的贡献

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/jcac.18.51

Takumi Ogawa, A. Okazawa, D. Ohta

引用次数: 0

Small Random Forest Models for Effective Chemogenomic Active Learning 有效化学基因组主动学习的小随机森林模型

Journal of Computer Aided Chemistry Pub Date : 2017-01-01 DOI: 10.2751/JCAC.18.124

C. Rakers, D. Reker, J. B. Brown

{"title":"Small Random Forest Models for Effective Chemogenomic Active Learning","authors":"C. Rakers, D. Reker, J. B. Brown","doi":"10.2751/JCAC.18.124","DOIUrl":"https://doi.org/10.2751/JCAC.18.124","url":null,"abstract":"The identification of new compound-protein interactions has long been the fundamental quest in the field of medicinal chemistry. With increasing amounts of biochemical data, advanced machine learning techniques such as active learning have been proven to be beneficial for building high-performance prediction models upon subsets of such complex data. In a recently published paper, chemogenomic active learning had been applied to the interaction spaces of kinases and G protein-coupled receptors featuring over 150,000 compound-protein interactions. Prediction models were actively trained based on random forest classification using 500 decision trees per experiment. In a new direction for chemogenomic active learning, we address the question of how forest size influences model evolution and performance. In addition to the original chemogenomic active learning findings that highly predictive models could be constructed from a small fraction of the available data, we find here that that model complexity as viewed by forest size can be reduced to one-fourth or one-fifth of the previously investigated forest size while still maintaining reliable prediction performance. Thus, chemogenomic active learning can yield predictive models with reduced complexity based on only a fraction of the data available for model construction.","PeriodicalId":41457,"journal":{"name":"Journal of Computer Aided Chemistry","volume":"18 1","pages":"124-142"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2751/JCAC.18.124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69255281","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: 16