Cancer Informatics
Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.
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About this journal
Aims and scope:
We live in a time when it is widely recognized that scientific collaborations across traditional disciplines can yield exponential gains through synergy. Cancer research is currently benefiting from advances in many fields, including biology (genomics and proteomics), physical chemistry (mass spectrometry and radio imaging), computer science and biostatistics (machine learning, artificial intelligence), and many others. Medical informatics is a field nearly as wide that includes patient information systems and related critical components of information management in the information age.Bioinformatics and computational biology appear to play a central role at each nexus, in part because novel technologies lead to immense leaps in the amount and granularity of data from patients and patient samples.
The aim of Cancer Informatics is to provide open access to peer-reviewed high quality manuscripts reporting bioinformatic analysis of molecular genetic and/or clinical data pertaining to human cancer risk, prevention, outcome or treatment response.
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Submissions, excluding editorials, letters to the editor and dedications, will be peer reviewed by two reviewers. Reviewers are required to provide fair, balanced and constructive reports.
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Journal newsletter sent on week 3, 2009
Estimating the False Discovery Rate Using Mixed Normal Distribution for Identifying Differentially Expressed Genes in Microarray Data Analysis
Akihiro Hirakawa1, Yasunori Sato1, Takashi Sozu2, Chikuma Hamada3, Isao Yoshimura3
1Genetics Division, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan. 2The Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Osaka, Japan. 3Faculty of Engineering, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan.
Abstract
The recent development of DNA microarray technology allows us to measure simultaneously the expression levels of thousands of genes and to identify truly correlated genes with anticancer drug response (differentially expressed genes) from many candidate genes. Significance Analysis of Microarray (SAM) is often used to estimate the false discovery rate (FDR), which is an index for optimizing the identifiability of differentially expressed genes, while the accuracy of the estimated FDR by SAM is not necessarily confirmed. We propose a new method for estimating the FDR assuming a mixed normal distribution on the test statistic and examine the performance of the proposed method and SAM using simulated data. The simulation results indicate that the accuracy of the estimated FDR by the proposed method and SAM, varied depending on the experimental conditions. We applied both methods to actual data comprised of expression levels of 12,625 genes of 10 responders and 14 non-responders to docetaxel for breast cancer. The proposed method identified 280 differentially expressed genes correlated with docetaxel response using a cut-off value for achieving FDR <0.01 to prevent false-positive genes, although 92 genes were previously thought to be correlated with docetaxel response ones.
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