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Cross-Platform Normalization Of Microarray And Rna-Seq Data For Machine Learning Applications, Jeffrey A. Thompson, Jie Tan, Casey S. Greene
Cross-Platform Normalization Of Microarray And Rna-Seq Data For Machine Learning Applications, Jeffrey A. Thompson, Jie Tan, Casey S. Greene
Dartmouth Scholarship
Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log …
Black Hole Variability And The Star Formation-Active Galactic Nucleus Connection: Do All Star-Forming Galaxies Host An Active Galactic Nucleus?, Ryan C. Hickox, James R. Mullaney, David M. Alexander, Chien-Ting J. Chen, Francesca M. Civano, Andy D. Goulding, Kevin N. Hainline
Black Hole Variability And The Star Formation-Active Galactic Nucleus Connection: Do All Star-Forming Galaxies Host An Active Galactic Nucleus?, Ryan C. Hickox, James R. Mullaney, David M. Alexander, Chien-Ting J. Chen, Francesca M. Civano, Andy D. Goulding, Kevin N. Hainline
Dartmouth Scholarship
We investigate the effect of active galactic nucleus (AGN) variability on the observed connection between star formation and black hole accretion in extragalactic surveys. Recent studies have reported relatively weak correlations between observed AGN luminosities and the properties of AGN hosts, which has been interpreted to imply that there is no direct connection between AGN activity and star formation. However, AGNs may be expected to vary significantly on a wide range of timescales (from hours to Myr) that are far shorter than the typical timescale for star formation (100 Myr). This variability can have important consequences for observed correlations. We …
A Simple And Computationally Efficient Approach To Multifactor Dimensionality Reduction Analysis Of Gene-Gene Interactions For Quantitative Traits, Jiang Gui, Jason H. Moore, Scott M. Williams, Peter Andrews, Hillege, Hans L. Hillege, Hans L., Pim Van Der Harst, Gerjan| Navis, Wiek H. Van Gilst, Folkert W. Asselbergs, Diane| Gilbert-Diamond
A Simple And Computationally Efficient Approach To Multifactor Dimensionality Reduction Analysis Of Gene-Gene Interactions For Quantitative Traits, Jiang Gui, Jason H. Moore, Scott M. Williams, Peter Andrews, Hillege, Hans L. Hillege, Hans L., Pim Van Der Harst, Gerjan| Navis, Wiek H. Van Gilst, Folkert W. Asselbergs, Diane| Gilbert-Diamond
Dartmouth Scholarship
We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR’s constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR’s testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.
Non-Gaussianity From Self-Ordering Scalar Fields, Daniel G. Figueroa, Robert R. Caldwell, Marc Kamionkowski
Non-Gaussianity From Self-Ordering Scalar Fields, Daniel G. Figueroa, Robert R. Caldwell, Marc Kamionkowski
Dartmouth Scholarship
The Universe may harbor relics of the post-inflationary epoch in the form of a network of self-ordered scalar fields. Such fossils, while consistent with current cosmological data at trace levels, may leave too weak an imprint on the cosmic microwave background and the large-scale distribution of matter to allow for direct detection. The non-Gaussian statistics of the density perturbations induced by these fields, however, permit a direct means to probe for these relics. Here we calculate the bispectrum that arises in models of self-ordered scalar fields. We find a compact analytic expression for the bispectrum, evaluate it numerically, and provide …
Effects Of Gravitational Slip On The Higher-Order Moments Of The Matter Distribution, Scott F. Daniel
Effects Of Gravitational Slip On The Higher-Order Moments Of The Matter Distribution, Scott F. Daniel
Dartmouth Scholarship
Cosmological departures from general relativity offer a possible explanation for the cosmic acceleration. To linear order, these departures (quantified by the model-independent parameter ϖ, referred to as a “gravitational slip”) amplify or suppress the growth of structure in the universe relative to what we would expect to see from a general relativistic universe lately dominated by a cosmological constant. As structures collapse and become more dense, linear perturbation theory is an inadequate descriptor of their behavior, and one must extend calculations to nonlinear order. If the effects of gravitational slip extend to these higher orders, we might expect to see …
Gibbsian Theory Of Power-Law Distributions, R. A. Treumann, C. H. Jaroschek
Gibbsian Theory Of Power-Law Distributions, R. A. Treumann, C. H. Jaroschek
Dartmouth Scholarship
It is shown that power-law phase space distributions describe marginally stable Gibbsian equilibria far from thermal equilibrium, which are expected to occur in collisionless plasmas containing fully developed quasistationary turbulence. Gibbsian theory is extended on the fundamental level to statistically dependent subsystems introducing an ‘‘ordering parameter‘‘ k. Particular forms for the entropy and partition functions are derived with superadditive (nonextensive) entropy, and a redefinition of temperature in such systems is given.