Introduction
Introduction


    Similarity searching using large-scaled transcriptome data has recently been introduced into chemical similarity comparison.However, they mainly emphasized on global expression comparison. It is inaccessible for researchers to see more deeply into underlying biology. For example, what contribute to their similarities or in what biological mechanisms do they resemble. Besides, investigation of side effort for drugs is challenging in biomedicine, because in most cases the action of a chemical is complicated. Most chemicals have multi-targets that target to more than one pathway. It's hard for global comparison to figure out the situation that two chemicals though do not act exactly alike, share some part of mechanisms.

    We devise a new method that makes some improvements in considering above two points. We set foot on a modularized concept and doing similarity comparison within small biological meaningful categories of genes. That is partitioning genes into gene ontology categories and implement similarity comparison within each category.

    Based on our method a web-based service was developed for facilitating researchers, especially experimenters to use. By far, whole data from connectivity map(build 1) are used as basic library for validating the rationality of our method and querying target chemicals. Persistent efforts will be made in adding data from other sources, including different disease state, other cell lines and organisms to upgrade , enlarge our data resource and expand the system's applications. Result is given in textual and graphical mode both supporting mail return.