IPF-LASSO: Integrative -Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data.

As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed "omics" data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction.

Connecting high-dimensional mRNA and miRNA expression data for binary medical classification problems.

In modern molecular biology, high-throughput experiments allow the simultaneous study of expression levels of thousands of biopolymers such as mRNAs, miRNAs or proteins. A typical goal of such experiments is to find molecular signatures that can distinguish between different types of tissue or that can predict a therapy outcome. While research typically focuses on just one type of molecular features of a gene, e.

Integration of gene expression data with prior knowledge for network analysis and validation.

Background: Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases

Results: A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules.

Planar cell movements and oriented cell division during early primitive streak formation in the mammalian embryo.

Formation of the mammalian primitive streak appears to rely on cell proliferation to a minor extent only, but compensating cell movements have not yet been directly observed. This study analyses individual cell migration and proliferation simultaneously, using multiphoton and differential interference contrast time-lapse microscopy of late pregastrulation rabbit blastocysts. Epiblast cells in the posterior gastrula extension area accumulated medially and displayed complex planar movements including U-turns and a novel type of processional cell movement.