用文本挖掘方法发现药物的副作用

分析了用文本挖掘方法探测药物副作用的必要性及可行性,从挖掘流程、挖掘/提取方法、结果评价和现有工具软件4个方面总结了用文本挖掘技术提取药物副作用的研究现状及尚未解决的问题和未来发展趋势。     

Literature-based concept profiles for gene annotation: the issue of weighting

BACKGROUND: Text-mining has been used to link biomedical concepts, such as genes or biological processes, to each other for annotation purposes or the generation of new hypotheses. To relate two concepts to each other several authors have used the vector space model, as vectors can be compared efficiently and transparently. Using this model, a concept is characterized by a list of associated concepts, together with weights that indicate the strength of the association. The associated concepts in the vectors and their weights are derived from a set of documents linked to the concept of interest. An important issue with this approach is the determination of the weights of the associated concepts. Various schemes have been proposed to determine these weights, but no comparative studies of the different approaches are available. Here we compare several weighting approaches in a large scale classification experiment. METHODS: Three different techniques were evaluated: (1) weighting based on averaging, an empirical approach; (2) the log likelihood ratio, a test-based measure; (3) the uncertainty coefficient, an information-theory based measure. The weighting schemes were applied in a system that annotates genes with Gene Ontology codes. As the gold standard for our study we used the annotations provided by the Gene Ontology Annotation project. Classification performance was evaluated by means of the receiver operating characteristics (ROC) curve using the area under the curve (AUC) as the measure of performance. RESULTS AND DISCUSSION: All methods performed well with median AUC scores greater than 0.84, and scored considerably higher than a binary approach without any weighting. Especially for the more specific Gene Ontology codes excellent performance was observed. The differences between the methods were small when considering the whole experiment. However, the number of documents that were linked to a concept proved to be an important variable. When larger amounts of texts were available for the generation of the concepts' vectors, the performance of the methods diverged considerably, with the uncertainty coefficient then outperforming the two other methods.     

LGscore: A method to identify disease-related genes using biological literature and Google data

Since the genome project in 1990s, a number of studies associated with genes have been conducted and researchers have confirmed that genes are involved in disease. For this reason, the identification of the relationships between diseases and genes is important in biology. We propose a method called LGscore, which identifies disease-related genes using Google data and literature data. To implement this method, first, we construct a disease-related gene network using text-mining results. We then extract gene–gene interactions based on co-occurrences in abstract data obtained from PubMed, and calculate the weights of edges in the gene network by means of Z-scoring. The weights contain two values: the frequency and the Google search results. The frequency value is extracted from literature data, and the Google search result is obtained using Google. We assign a score to each gene through a network analysis. We assume that genes with a large number of links and numerous Google search results and frequency values are more likely to be involved in disease. For validation, we investigated the top 20 inferred genes for five different diseases using answer sets. The answer sets comprised six databases that contain information on disease–gene relationships. We identified a significant number of disease-related genes as well as candidate genes for Alzheimer’s disease, diabetes, colon cancer, lung cancer, and prostate cancer. Our method was up to 40% more accurate than existing methods.     

Bioinformatics and cancer research: building bridges for translational research

Genome studies have revolutionised cancer research in recent years as high-throughput technologies can now be used to identify sets of genes potentially related with different processes in cancer. However, managing all this data and organising it into useful datasets is still a challenge in the bioinformatics field. Finding relationships between the molecular and genomic information and the clinical information available, within the medical informatics domain, is currently driving the development of translational research in biomedicine. The dispersion and complexity of the molecular information, the poor adherence to standards, together with the fast evolution of the experimental techniques, pose obvious challenges for the development of integrated molecular resources. In parallel, restricted access to medical information together with the gaps in the development of standard terminologies are typical limitations in the area of medical informatics. The development of research projects combining medical and molecular information together with the current efforts to standardise and integrate databases and terminologies are described in this review as a demonstration of the fruitful activity in this area.     

基于信息挖掘的细胞信号网络重构及其矩阵描述

目的:利用文献信息构建细胞信号网络,并研究对细胞信号网络进行分析、描述的新型方法.方法:采用适宜的数据挖掘工具进行数据挖掘和分析构建一定尺度的细胞信号网络,并借助矩阵对信号网络进行分析和描述.结果:通过数据挖掘构建得到了局部细胞信号网络,并使其通过矩阵在多个方面得到了较好的描述.结论:基于科学文献的数据挖掘是构建细胞信号网络的有效手段,矩阵在细胞信号网络的分析、描述上具有独特优势.