Biosilico最新文献

The function(s) of a novel gene or gene product can be inferred by associating the gene or gene product with those whose functions are known. It is now common practice to associate two genes if they have similar sequences. In recent years, computational methods have been developed that associate genes on the basis of features beyond similarity, using a variety of biological data beyond single-gene sequences. This review describes several promising methods that associate genes or gene products. These associative methods employ similarity of sequences and structures, features from whole-genome analysis, co-expression patterns from microarray and EST data, interacting properties from proteomic data, and links from literature mining. Finally, we outline issues surrounding the validation and integration of these methods.

The expansion of genomic information has made data integration as important to bioinformatics as computational analyses. A ‘systems biology’ approach to understanding drug targets requires integrating diverse types of data, including nucleotide and protein sequences, mRNA and protein expression measurements, model organism data, alternative splicing, single nucleotide polymorphisms (SNPs) and more. This review describes how publicly available databases and data formats facilitate such integration. However, this discussion is by no means comprehensive. It represents the tools and approaches that Bristol-Myers Squibb (BMS) Bioinformatics has chosen to pursue. At BMS, two tools provide access to this information. Genome browsers provide graphic overviews of sequence-based information, whereas a curated database of drug target information provides annotation and analyses. The integration of all these functions results in a flexible bioinformatics infrastructure for drug discovery.

Efficient information retrieval and extraction is a major challenge in molecular biology and genome-based clinical research. In addition, there is an increasing demand to combine information from different resources and across different disciplines in life sciences. Unfortunately, a large proportion of this information is only available in scientific articles. Moreover, the volume of literature is growing almost exponentially. Text mining provides methods to retrieve and extract information contained in free-text automatically. Here, we discuss the challenges and limitations of text mining in biology and medicine, including unsolved problems and necessary developments.

The information age has made the electronic storage of large amounts of data effortless. The proliferation of documents available on the Internet, corporate intranets, news wires and elsewhere is overwhelming. Search engines only exacerbate this overload problem by making increasingly more documents available in only a few keystrokes. This information overload also exists in the biomedical field, where scientific publications, and other forms of text-based data are produced at an unprecedented rate. Text mining is the combined, automated process of analyzing unstructured, natural language text to discover information and knowledge that are typically difficult to retrieve. Here, we focus on text mining as applied to the biomedical literature. We focus in particular on finding relationships among genes, proteins, drugs and diseases, to facilitate an understanding and prediction of complex biological processes. The LitMiner™ system, developed specifically for this purpose; is described in relation to the Knowledge Discovery and Data Mining Cup 2002, which serves as a formal evaluation of the system.

Successful life science data integration is a complex feat facing today's researchers and bioinformaticians. It demands the seamless access, integration and query of unprecedented amounts of disparate biological data to advance the pace and effectiveness of new drug discovery. This article outlines the current state of technologies available to help achieve this feat. It explores the evolutionary processes that created these challenges, and the underpinnings of several technological innovations working to overcome them. Together, these technologies aim to change the face of drug R&D through an enhanced understanding and interpretation of life sciences data.

Microarrays can be used as diagnostic clinical tools, providing a global overview of gene transcription in diseased tissues. Expression profiles can be easily obtained in a single assay and provide exhaustive information about molecular events that are often directly linked to the cause of a disease. However, the high complexity of the data are challenging. This article reviews recent efforts in bioinformatics and statistics to overcome this problem and make feasible the clinical analysis of gene expression profiles.