Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.
The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat-21 constellation of three spacecraft scheduled for launch in 2004. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. We also describe our working software prototype and preparations for flight.
We describe the design and implementation of a software system for producing, managing, and analyzing catalogs from the digital scans of the Second Palomar Observatory Sky Survey. The system (SKICAT) integrates new and existing packages for performing the full sequence of tasks from raw pixel processing, to object classification, to the matching of multiple, overlapping Schmidt plates and CCD calibration frames. We describe the relevant details of constructing SKICAT plate, CCD, matched, and object catalogs. Plate and CCD catalogs are generated from images, while the latter are derived from existing catalogs. A pair of programs complete the majority of plate and CCD processing in an automated, pipeline fashion, with the user required to execute a minimal number of pre-and post-processing procedures. We apply a modified version of FOCAS for the detection and photometry, and new software for matching catalogs on an object-by-object basis. SKICAT employs modem machine-learning techniques, such as decision trees, to perform automatic star-galaxy-artifact classification with a >90% accuracy down to ~1 mag above the plate detection limit. The system also provides a variety of tools for interactively querying and analyzing the resulting object catalogs.
Analysis of gene expression in clinical samples poses special challenges, including limited RNA availability and poor RNA quality. Quantitative information regarding reliability of RNA amplification methodologies applied to primary cells and representativeness of resulting gene expression profiles is limited. We evaluated four protocols for RNA amplification from peripheral blood mononuclear cells. Results obtained with 100 ng or 10 ng of RNA amplified using two rounds of cDNA synthesis and in vitro transcription were compared with control 2.5-microg RNA samples processed using a single round of in vitro transcription. Samples were hybridized to Affymetrix HG-U133A arrays. Considerable differences in results were obtained with different protocols. The optimal protocol resulted in highly reproducible gene expression profiles from amplified samples (r = 0.98) and good correlation between amplified and control samples (r = 0.94). Using the optimal protocol dissimilarities of gene expression between mononuclear cells from a normal individual and a patient with myelodysplastic syndrome were primarily maintained after amplification compared with controls. We conclude that small variations in methodology introduce considerable distortion of gene expression profiles obtained after RNA amplification from clinical samples and too strong a focus on a very small number of genes picked from an array analysis could be unduly influenced by seemingly acceptable methodologies. However, it is possible to obtain reproducible and representative results using optimized protocols.
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