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Predicting Strategies for Lead Optimization via Learning to Rank 基于学习排序的引证优化预测策略
IPSJ Transactions on Bioinformatics Pub Date : 2018-01-01 DOI: 10.2197/IPSJTBIO.11.41
Nobuaki Yasuo, Keisuke Watanabe, Hideto Hara, K. Rikimaru, M. Sekijima
{"title":"Predicting Strategies for Lead Optimization via Learning to Rank","authors":"Nobuaki Yasuo, Keisuke Watanabe, Hideto Hara, K. Rikimaru, M. Sekijima","doi":"10.2197/IPSJTBIO.11.41","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.11.41","url":null,"abstract":": Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding a ffi nity, target selectivity, physicochemical properties, and tox-icity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.11.41","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68500942","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: 6
Improvement and Evaluation of a Mathematical Model for Fertilization Calcium Waves in Caenorhabditis Elegans 秀丽隐杆线虫受精钙波数学模型的改进与评价
IPSJ Transactions on Bioinformatics Pub Date : 2018-01-01 DOI: 10.2197/IPSJTBIO.11.24
Momoko Imakubo, Jun Takayama, Shuichi Onami
{"title":"Improvement and Evaluation of a Mathematical Model for Fertilization Calcium Waves in Caenorhabditis Elegans","authors":"Momoko Imakubo, Jun Takayama, Shuichi Onami","doi":"10.2197/IPSJTBIO.11.24","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.11.24","url":null,"abstract":"Ca2+ waves propagate through the oocyte during fertilization, activate the oocyte and induce embryonic development. Ca2+-induced Ca2+-release (CICR) is a mechanism of Ca2+ wave formation. We previously quantified the Ca2+ waves in the nematode Caenorhabditis elegans by using high-speed imaging and image analysis. We found that the waves consist of a rapid local rise at the point of sperm entry and a slow global wave. We demonstrated that the Nagumo model, which models the CICR by a reaction–diffusion equation, can produce a similar biphasic waveform. However, the model cannot represent the observed gradual decrease in maximum Ca2+ concentration with increasing distance from the point of sperm entry. In this study, we introduced a linear monotonically decreasing function into the reaction part of the Nagumo model. We demonstrated that our new model can produce the gradual decrease in maximum Ca2+ concentration with increasing distance from the point of sperm entry and a biphasic waveform simultaneously.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68500929","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
Discovering Symptom-herb Relationship by Exploiting SHT Topic Model 利用SHT主题模型发现证药关系
IPSJ Transactions on Bioinformatics Pub Date : 2017-01-01 DOI: 10.2197/IPSJTBIO.10.16
Lidong Wang, Keyong Hu, Xiaodong Xu
{"title":"Discovering Symptom-herb Relationship by Exploiting SHT Topic Model","authors":"Lidong Wang, Keyong Hu, Xiaodong Xu","doi":"10.2197/IPSJTBIO.10.16","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.10.16","url":null,"abstract":"TCM has been widely researched through various methods in computer science in past decades, but none digs into huge amount of clinical cases to discover the meaningful treatment patterns between symptoms and herbs. To meet the challenge, we explore the unstructured and intricate experiential data in clinical case, and propose a method to discover the treatment patterns by introducing a novel topic model named SHT (Symptom-Herb Topic model). Combinational rules are incorporated into the learning process. We evaluate our method on 3,765 TCM clinical cases. The experiment validates the effectiveness of our method compared with LDA model and LinkLDA model.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.10.16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68500859","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 Fold Recognition with Representation Learning and Long Short-Term Memory 蛋白质折叠识别与表征学习和长短期记忆
IPSJ Transactions on Bioinformatics Pub Date : 2017-01-01 DOI: 10.2197/IPSJTBIO.10.2
Masashi Tsubaki, M. Shimbo, Yuji Matsumoto
{"title":"Protein Fold Recognition with Representation Learning and Long Short-Term Memory","authors":"Masashi Tsubaki, M. Shimbo, Yuji Matsumoto","doi":"10.2197/IPSJTBIO.10.2","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.10.2","url":null,"abstract":"","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.10.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68500909","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 Source and Functional Connectivity of Neurophysiological Correlates of Temporal Mental Orientation during Natural Language Processing 自然语言加工中时间心理取向的神经生理相关因素的信号来源和功能连接
IPSJ Transactions on Bioinformatics Pub Date : 2016-03-01 DOI: 10.2197/IPSJTBIO.9.12
T. Soshi, Satoko Hisanaga, K. Sekiyama
{"title":"Signal Source and Functional Connectivity of Neurophysiological Correlates of Temporal Mental Orientation during Natural Language Processing","authors":"T. Soshi, Satoko Hisanaga, K. Sekiyama","doi":"10.2197/IPSJTBIO.9.12","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.9.12","url":null,"abstract":"","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.9.12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503301","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
Linear and Nonlinear Regression for Combinatorial Optimization Problem of Multiple Transgenesis 多重转基因组合优化问题的线性与非线性回归
IPSJ Transactions on Bioinformatics Pub Date : 2016-01-01 DOI: 10.2197/IPSJTBIO.9.7
D. Tominaga, Kazuki Mori, S. Aburatani
{"title":"Linear and Nonlinear Regression for Combinatorial Optimization Problem of Multiple Transgenesis","authors":"D. Tominaga, Kazuki Mori, S. Aburatani","doi":"10.2197/IPSJTBIO.9.7","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.9.7","url":null,"abstract":"Combinatorial optimization problem is a difficult class of problems from which to obtain exact solutions, but such problems often arise in biotechnology, for example, it is often necessary to find optimal combinations of genes in transgenics to improve production of a useful compound by microorganisms. In the cases of 20 candidate genes for introduction into cells, the number of possible combinations of introduced genes is approximately 106. Testing all of their combinations by experimental observation is impossible practically. A few combinations are observed experimentally for large numbers of possible combinations generally. We tested two methods for the prediction of effects of transgenes: multivariate linear regression and the RBF (Radial Basis Function) network, with a simulated and an unpublished experimentally observed dataset of transgenic yeast. Results show that RBF network can detect a special gene (introduced gene) at the five percent significance level when the gene causes production values that are 1.5 times greater than other genes for the simulated dataset. Prediction by RBF network causes over-learning for larger numbers of learning data, however, it is superior than that by the linear regression model at the best condition.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.9.7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503012","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
Survival Ensemble with Sparse Random Projections for Censored Clinical and Gene Expression Data 筛选临床和基因表达数据的稀疏随机预测生存集合
IPSJ Transactions on Bioinformatics Pub Date : 2016-01-01 DOI: 10.2197/IPSJTBIO.9.18
Lifeng Zhou, Hong Wang, Qingsong Xu
{"title":"Survival Ensemble with Sparse Random Projections for Censored Clinical and Gene Expression Data","authors":"Lifeng Zhou, Hong Wang, Qingsong Xu","doi":"10.2197/IPSJTBIO.9.18","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.9.18","url":null,"abstract":"Random projection is a powerful method for dimensionality reduction while its applications in highdimensional survival analysis is rather limited. In this research, we propose a novel survival ensemble model based on sparse random projection and survival trees. Supported by the proper statistical analysis, we show that the proposed model is comparable to or better than popular survival models such as random survival forest, regularized Cox proportional hazard and fast cocktail models on high-dimensional microarray gene expression right censored data.","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.9.18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503431","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
Prediction of Gene Structures from RNA-seq Data Using Dual Decomposition 利用对偶分解从RNA-seq数据预测基因结构
IPSJ Transactions on Bioinformatics Pub Date : 2015-06-23 DOI: 10.2197/IPSJTBIO.9.1
Tatsumu Inatsuki, Kengo Sato, Y. Sakakibara
{"title":"Prediction of Gene Structures from RNA-seq Data Using Dual Decomposition","authors":"Tatsumu Inatsuki, Kengo Sato, Y. Sakakibara","doi":"10.2197/IPSJTBIO.9.1","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.9.1","url":null,"abstract":"","PeriodicalId":38959,"journal":{"name":"IPSJ Transactions on Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2197/IPSJTBIO.9.1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503215","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
Drug clearance pathway prediction based on semi-supervised learning 基于半监督学习的药物清除途径预测
IPSJ Transactions on Bioinformatics Pub Date : 2015-03-13 DOI: 10.2197/IPSJTBIO.8.21
Keisuke Yanagisawa, T. Ishida, Y. Akiyama
{"title":"Drug clearance pathway prediction based on semi-supervised learning","authors":"Keisuke Yanagisawa, T. Ishida, Y. Akiyama","doi":"10.2197/IPSJTBIO.8.21","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.8.21","url":null,"abstract":"It is necessary to confirm that a new drug can be appropriately cleared from the human body. However, checking the clearance pathway of a drug in the human body requires clinical trials, and therefore requires large cost. Thus, computational methods for drug clearance pathway prediction have been studied. The proposed prediction methods developed previously were based on a supervised learning algorithm, which requires clearance pathway information for all drugs in a training set as input labels. However, these data are often insufficient because of the high cost of their acquisition. In this paper, we propose a new drug clearance pathway prediction method based on semisupervised learning, which can use not only labeled data but also unlabeled data. We evaluated the effectiveness of our method, focusing on the cytochrome P450 2C19 enzyme, which is involved in one of the major clearance pathways.","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.8.21","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503077","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
Application for Evaluating and Visualizing the Sequence Conservation of Ligand-binding Sites 配体结合位点序列保守性评价与可视化的应用
IPSJ Transactions on Bioinformatics Pub Date : 2015-03-13 DOI: 10.2197/IPSJTBIO.8.9
Nobuaki Yasuo, M. Sekijima
{"title":"Application for Evaluating and Visualizing the Sequence Conservation of Ligand-binding Sites","authors":"Nobuaki Yasuo, M. Sekijima","doi":"10.2197/IPSJTBIO.8.9","DOIUrl":"https://doi.org/10.2197/IPSJTBIO.8.9","url":null,"abstract":": We developed a new application to quantitatively evaluate the sequence conservation of ligand-binding sites by integrating information pertaining to protein structures, ligand-binding sites, and amino acid sequences. These data are visualized onto protein structures via a Jmol or PyMOL interface. The visualization is very important for structure-based drug design (SBDD). Key features of this application are the visualization of slight di ff erences in specific ligand-binding sites and ConservationScore comparable among ligand-binding sites. Furthermore, we conducted an experiment to visualize the calculation and comparison of the ConservationScore of four viral proteins as well as an experiment to visualize the di ff erences between proteins belonging to the human β adrenergic receptor family. This application is available at http: // www.bio.gsic.titech.ac.jp / visco.html .","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.8.9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68503105","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
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