This paper discusses about the data obtained from gene chips and methods of their analysis. Analyzes some methods for analyzing these data and focus on the method of "Random Forests". Shows dataset that is used for specific experiments. Methods are realized in R language environment. Than they are tested, and the results are presented and compared. Results with method "Random Forests" are compared with other experiments on same dataset.

This project is the result of a summer long internship I worked on with Dr. Beverly Smith in the ETSU Department of Physics and Astronomy. It was completed with the support of a grant from the NASA Tennessee Space Grant Consortium. Quenching galaxies are galaxies that are rapidly evolving from strongly star forming systems to galaxies with mostly old stars and low star formation rates. When identifying quenching galaxies, there are several methods in common use. Furthermore, there are several ways astronomers estimate the Star Formation Rate (SFR), in Solar Masses per year, and Stellar Mass (M*), in Solar Masses, of galaxies. For a large sample of galaxies, we used 6 derivations of M* and 4 for SFR, plotting them against each other for comparison. We also calculated and compared the specific SFR (sSFR), equal to SFR/M*, and compared the different methods of defining quenched galaxies. Finally, we divided up these plots by classification, Red Sequence/Green Valley/Blue Cloud, and different values of log (SFR) *

The Francolins constitute the largest genus in the Galliform family Phasianidae. There is little accord concerning the taxonomic classification of its members. In the past, information on this group has been provided by morphological and palaeontological evidence. An investigation into the molecular history of this group is presented, using mitochondrial DNA (mtDNA) as an evolutionary tool. A comparison of mtDNA restriction fragment lengths has been used to help define the phylogenetic relationships between 13 southern African Francolin species and a selected outgroup, the Japanese Quail. Both cladistic and distance-based analytical methods have been used to construct phylogenies from the molecular fragment data. The trees relating the Francolins are in general agreement with the traditional classification based on morphological, behavioural and morphometric studies, but differ in the branching order of two species, F. levaillantii and F. hartlaubi. A recent proposal for the partitioning of the genus into two monophyletic assemblages of quail-like "partridges" and pheasant-like "francolins" is supported by mtDNA fragment data, with the exception of the two aberrant taxa. On the basis of the initial fragment size comparison, F. hartlaubi and F. levaillantii constitute part of an unresolved quadrichotomy at the base of the tree. A restriction endonuclease site mapping approach has been utilized to provide a deeper resolution for the molecular phylogeny. Detailed mtDNA restriction endonuclease maps of F. levaillantii, F. hartlaubi, two species representing the "partridge" and "francolin" monophyletic groups respectively, and also of the Madagascar Partridge, have been constructed. Phylogenetic analysis of this data has helped to resolve the problematic placement of the two aberrant taxa by showing an early separation of F. levaillantii from the "partridge" lineage, and of F. hartlaubi from the "francolin" lineage. The Madagascar Partridge was anticipated to be a likely sister-taxon to the whole group, but instead appears to have close relationships within the "partridge" lineage.

This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimensional space using a linear combination of variables. Besides the classification, the method can be used to determine variables interaction and perform feature extraction. Theoretical investigations and numerical simulations were used to analyze the properties and performance of the new approach. Comparison with other popular classification methods was performed using simulated and real data examples. The algorithm was implemented as an extension package for the statistical computing environment R and is available for free download under the GNU General Public License.

Indiana University-Purdue University Indianapolis (IUPUI) / Today we know that there are several different types of breast cancer. Accurate identification breast cancer subtype is extremely important in treating this disease effectively. Consequently the process of invtro development of drugs to treat this disease should be naturally subtype specific. Until now several studies have identified multiple breast cancer cell lines and these cell lines have served as invaluable invitro tumor models. However very few of these cell lines are classified as per their subtypes. In this thesis an effort is made to classify 59 of such breast cancer cell lines using genetic profile comparison approach. This approach is based on comparing characteristic features such as copy number and gene expression of a given cell line to those observed from the tissue samples of different breast subtypes. The tissue data for this comparison comes from The Cancer Genome Atlas (TCGA) while cell line data is taken from Cancer Cell Line Encyclopedia (CCLE).

This dissertation examines and analyzes novel techniques that are useful in the collection and processing of data from a Frequency Modulated Continuous Wave Radar. The major topics discussed in this work are: reduction of amplitude modulation, signature collection without an anechoic chamber, transforming a signature into a matched filter, accounting for electromagnetic interference, accounting for digital noise, and the application of a Support Vector Machine to achieve classification. In addition, this work also provides a broad overview of a framework specifically developed to improve detection and classification without requiring expensive hardware modification. The four main categories analyzed in this work are distortion, spectral signature, optimal detection, and classification. Some notable contributions in this work include the assessment of a novel technique’s effectiveness to improve model accuracy by accounting for amplitude modulation in an FMCW radar, as well as discussion of improved techniques to perform signature collection with an FMCW radar in the absence of an anechoic chamber. The signature collection technique is a novel approach that utilizes physics and wavelets in an effort to improve Signal to Noise Ratio (SNR). This work also considers a novel technique to convert an FMCW target signature into coefficients for a matched filter, thus allowing for the full mathematical application of the optimal matched filter. In addition, this work provides an analysis of the improved performance of an FMCW radar through the development and use of a novel technique to account for both electromagnetic interference and digital noise. Finally the initial discovery, development, and refinement of an innovative application using SVM to classify the matched filter results of FMCW radar targets is given, thus resulting in previously uncollected and undocumented viable baseline data.

Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high resolution to improve microwave sensor-based soil moisture observations. Moreover, assimilation can also be used to predict related hydrological variables, e.g., precipitation products can be assimilated in a land information system to estimate soil moisture. High quality spatio-temporal observations of these processes are vital for a successful assimilation which in turn needs a detailed analysis and improvement. In this research, pattern recognition and adaptive signal processing methods are developed for the spatio-temporal analysis and enhancement of soil moisture and precipitation datasets. These methods are applied to accomplish the following tasks: (i) a consistency analysis of level-3 soil moisture data from the Advanced Microwave Scanning Radiometer – EOS (AMSR-E) against in-situ soil moisture measurements from the USDA Soil Climate Analysis Network (SCAN). This method performs a consistency assessment of the entire time series in relation to others and provides a spatial distribution of consistency levels. The methodology is based on a combination of wavelet-based feature extraction and oneclass support vector machines (SVM) classifier. Spatial distribution of consistency levels are presented as consistency maps for a region, including the states of Mississippi, Arkansas, and Louisiana. These results are well correlated with the spatial distributions of average soil moisture, and the cumulative counts of dense vegetation; (ii) a modified singular spectral analysis based interpolation scheme is developed and validated on a few geophysical data products including GODAE’s high resolution sea surface temperature (GHRSST). This method is later employed to fill the systematic gaps in level-3 AMSR-E soil moisture dataset; (iii) a combination of artificial neural networks and vector space transformation function is used to fuse several high resolution precipitation products (HRPP). The final merged product is statistically superior to any of the individual datasets over a seasonal period. The results have been tested against ground based measurements of rainfall over our study area and average accuracies obtained are 85% in the summer and 55% in the winter 2007.

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