The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data

Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology

Method and apparatus for displaying information

A method for displaying large amounts of information. The method includes the steps of forming a spatial layout of tiles each corresponding to a representative reference element; mapping observed elements onto the spatial layout of tiles of representative reference elements; assigning a respective value to each respective tile of the spatial layout of the representative elements; and displaying an image of the spatial layout of tiles of representative elements. Each tile includes atomic attributes of representative elements. The invention also relates to an apparatus for displaying large amounts of information. The apparatus includes a tiler forming a spatial layout of tiles, each corresponding to a representative reference element; a comparator mapping observed elements onto said spatial layout of tiles of representative reference elements; an assigner assigning a respective value to each respective tile of said spatial layout of representative reference elements; and a display displaying an image of the spatial layout of tiles of representative reference elements

The LeFE Algorithm: Embracing the Complexity of Gene Expression in the Interpretation of Microarray Data

The LeFE algorithm has been developed to address the complex, non-linear regulation of gene expression. Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology.National Cancer Institute's Center for Cancer Researc

Towards a Holistic, Yet Gene-Centered Analysis of Gene Expression Profiles: A Case Study of Human Lung Cancers

Genome-wide gene expression profile studies encompass increasingly large number of samples, posing a challenge to their presentation and interpretation without losing the notion that each transcriptome constitutes a complex biological entity. Much like pathologists who visually analyze information-rich histological sections as a whole, we propose here an integrative approach. We use a self-organizing maps -based software, the gene expression dynamics inspector (GEDI) to analyze gene expression profiles of various lung tumors. GEDI allows the comparison of tumor profiles based on direct visual detection of transcriptome patterns. Such intuitive “gestalt” perception promotes the discovery of interesting relationships in the absence of an existing hypothesis. We uncovered qualitative relationships between squamous cell tumors, small-cell tumors, and carcinoid tumor that would have escaped existing algorithmic classifications. These results suggest that GEDI may be a valuable explorative tool that combines global and gene-centered analyses of molecular profiles from large-scale microarray experiments

Deterministic and stochastic sampling of two coupled Kerr parametric oscillators

The vision of building computational hardware for problem optimization has spurred large efforts in the physics community. In particular, networks of Kerr Parametric Oscillators (KPOs) are envisioned as simulators for finding the ground states of Ising Hamiltonians. It was shown, however, that KPO networks can feature large numbers of unexpected solutions that are difficult to sample with the existing deterministic (i.e., adiabatic) protocols. In this work, we experimentally realize a system of two coupled KPOs and find good agreement with the predicted mapping to Ising states. We then introduce a protocol based on stochastic sampling of the system and show how the resulting probability distribution can be used to identify the ground state of the corresponding Ising Hamiltonian. This method is akin to a Monte-Carlo sampling of multiple out-of-equilibrium stationary states and is less prone to become trapped in local minima than deterministic protocols

Perturbative Couplings and Modular Forms in N=2 String Models with a Wilson Line

We consider a class of four parameter D=4, N=2 string models, namely heterotic strings compactified on K3 times T2 together with their dual type II partners on Calabi-Yau three-folds. With the help of generalized modular forms (such as Siegel and Jacobi forms), we compute the perturbative prepotential and the perturbative Wilsonian gravitational coupling F1 for each of the models in this class. We check heterotic/type II duality for one of the models by relating the modular forms in the heterotic description to the known instanton numbers in the type II description. We comment on the relation of our results to recent proposals for closely related models.Comment: 42 pages, LaTeX, revised version contains additional reference

Caldesmon- dependent switching between capillary endothelial cell growth and apoptosis through modulation of cell shape and contractility.

Abstract Caldesmon (CaD), a protein component of the actomyosin filament apparatus, modulates cell shape and cytoskeletal structure when overexpressed. When capillary endothelial cells were infected with an adenoviral vector encoding GFP-CaD under Tet-Off control, progressive inhibition of contractility, loss of actin stress fibers, disassembly of focal adhesions, and cell retraction resulted. This was accompanied by a cell shape (rounding)-dependent increase in apoptosis and concomitant inhibition of cell cycle progression. Cell growth also was inhibited in low expressor cells in which cell tension was suppressed independently of significant changes in cell shape, cytoskeletal structure, or focal adhesions. Thus, changes in both cytoskeletal structure and contractility appear to be central to the mechanism by which extracellular matrix-dependent changes in capillary cell shape influence growth and apoptosis during angiogenesis, and hence the cytoskeleton may represent a potential target for antiangiogenesis therapy

Ancient human genomes suggest three ancestral populations for present-day Europeans

We sequenced the genomes of a ∼7,000-year-old farmer from Germany and eight ∼8,000-year-old hunter-gatherers from Luxembourg and Sweden. We analysed these and other ancient genomes1,2,3,4 with 2,345 contemporary humans to show that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians3, who contributed to both Europeans and Near Easterners; and early European farmers, who were mainly of Near Eastern origin but also harboured west European hunter-gatherer related ancestry. We model these populations’ deep relationships and show that early European farmers had ∼44% ancestry from a ‘basal Eurasian’ population that split before the diversification of other non-African lineages.Instituto Multidisciplinario de Biología Celula