Automatic recognition and annotation of gene expression patterns of fly embryos

J. Zhou, H. Peng
<span title="2007-06-15">2007</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a> </i> &nbsp;
Motivation: Gene expression patterns obtained by in situ mRNA hybridization provide important information about different genes during Drosophila embryogenesis. So far, annotations of these images are done by manually assigning a subset of anatomy ontology terms to an image. This time-consuming process depends heavily on the consistency of experts. Results: We develop a system to automatically annotate a fruitfly!s embryonic tissue in which a gene has expression. We formulate the task as an
more &raquo; ... e pattern recognition problem. For a new fly embryo image, our system answers two questions: 1) Which stage-range does an image belong to? 2) Which annotations should be assigned to an image? We propose to identify the wavelet-embryo features by multi-resolution 2D wavelet discrete transform followed by min-Redundancy max-Relevance feature selection, which yields optimal distinguishing features for an annotation. We then construct a series of parallel bi-class predictors to solve the multi-objective annotation problem since each image may correspond to multiple annotations.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btm244">doi:10.1093/bioinformatics/btm244</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zm5znbptlzhhdaizn47oovmtfa">fatcat:zm5znbptlzhhdaizn47oovmtfa</a> </span>
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