Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference)最新文献_第3页

Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease 基于文献挖掘的生物专家知识在人类疾病上位分析中的蚁群优化应用

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-04-03 DOI: 10.1007/978-3-642-37189-9_12

Arvis Sulovari, Jeff Kiralis, J. Moore

引用次数: 3

Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding 差分进化混合多目标人工蜂群在基序查找中的应用

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-04-03 DOI: 10.1007/978-3-642-37189-9_7

D. L. González-Álvarez, M. A. Vega-Rodríguez

引用次数: 6

Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions 利用交叉和径向基函数提高CGPANN在乳腺癌诊断中的性能

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-04-03 DOI: 10.1007/978-3-642-37189-9_15

T. Manning, P. Walsh

引用次数: 13

Dimensionality Reduction via Isomap with Lock-Step and Elastic Measures for Time Series Gene Expression Classification 基于锁步和弹性度量的等距图降维方法在时间序列基因表达分类中的应用

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-04-03 DOI: 10.1007/978-3-642-37189-9_9

C. Orsenigo, C. Vercellis

引用次数: 5

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins 基于aco的贝叶斯网络集成在衰老相关蛋白分层分类中的应用

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-04-03 DOI: 10.1007/978-3-642-37189-9_8

Khalid M. Salama, A. Freitas

引用次数: 11

Multiple Threshold Spatially Uniform ReliefF for the Genetic Analysis of Complex Human Diseases 用于复杂人类疾病遗传分析的多阈值空间均匀缓解

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-04-03 DOI: 10.1007/978-3-642-37189-9_1

Delaney Granizo-MacKenzie, J. Moore

引用次数: 36

Knowledge-constrained K-medoids Clustering of Regulatory Rare Alleles for Burden Tests. 负荷检测中调控稀有等位基因的知识约束k -媒介聚类。

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2013-01-01 DOI: 10.1007/978-3-642-37189-9_4

R Michael Sivley, Alexandra E Fish, William S Bush

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引用次数: 2

Biomedical text categorization with concept graph representations using a controlled vocabulary 使用受控词汇表的概念图表示的生物医学文本分类

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2012-08-12 DOI: 10.1145/2350176.2350181

Meenakshi Mishra, Jun Huan, S. Bleik, Min Song

引用次数: 21

Globalized bipartite local model for drug-target interaction prediction 药物-靶标相互作用预测的全球化二部局部模型

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2012-08-12 DOI: 10.1145/2350176.2350178

Jianxiang Mei, C. Kwoh, Peng Yang, X. Li, Jie Zheng

{"title":"Globalized bipartite local model for drug-target interaction prediction","authors":"Jianxiang Mei, C. Kwoh, Peng Yang, X. Li, Jie Zheng","doi":"10.1145/2350176.2350178","DOIUrl":"https://doi.org/10.1145/2350176.2350178","url":null,"abstract":"In pharmacology, it is essential to identify the interactions between drug and targets to understand its effects. Supervised learning with Bipartite Local Model (BLM) recently has been shown to be effective for prediction of drug-target interactions by first predicting target proteins of a given known drug, then predicting drugs targeting a known protein. However, this pure \"local\" model is inapplicable to new drug or target candidates that currently have no known interactions. In this paper, we extend the existing BLM method by integrating a strategy for handling new drug and target candidates. Based on the assumption that similar drugs and targets have similar interaction profiles, we present a simple neighbor-based training data inferring procedure and integrate it into the frame work of BLM. This globalized BLM called bipartite local model with neighbor-based inferring (BLMN) then has an extended functionality for prediction interactions between new drug candidates and new target candidates. Good performance of BLMN has been observed in the experiment of predicting interactions between drugs and four important categories of targets. For the Nuclear Receptors dataset, where there are more chances for the presented strategy to be applied, 20% improvement in terms of AUPR was achieved. This demonstrates the effectiveness of BLMN and its potential in prediction of drug-target interactions.","PeriodicalId":90497,"journal":{"name":"Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference)","volume":"47 1","pages":"8-14"},"PeriodicalIF":0.0,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78252135","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

2D similarity kernels for biological sequence classification 生物序列分类的二维相似核

Evolutionary computation, machine learning and data mining in bioinformatics. EvoBIO (Conference) Pub Date : 2012-08-12 DOI: 10.1145/2350176.2350179

P. Kuksa

引用次数: 6