Learning Polymorphic Neural ODEs With Time-Evolving Mixture.

Neural ordinary differential equations (NODE) present a new way of considering a deep residual network as a continuous structure by layer depth. However, it fails to overcome its representational limits, where it cannot learn all possible homeomorphisms of input data space, and therefore quickly saturates in terms of performance even as the number of layers increases. Here, we show that simply stacking Neural ODE blocks could easily improve performance by alleviating this issue.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data.

XMRF: an R package to fit Markov Networks to high-throughput genetics data.

Background: Technological advances in medicine have led to a rapid proliferation of high-throughput "omics" data. Tools to mine this data and discover disrupted disease networks are needed as they hold the key to understanding complicated interactions between genes, mutations and aberrations, and epi-genetic markers.

Results: We developed an R software package, XMRF, that can be used to fit Markov Networks to various types of high-throughput genomics data.

Graphical Models via Univariate Exponential Family Distributions.

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data.

Genomic Alterations in the RB Pathway Indicate Prognostic Outcomes of Early-Stage Lung Adenocarcinoma.

Purpose: To better understand the complete genomic architecture of lung adenocarcinoma.

Experimental Design: We used array experiments to determine copy number variations and sequenced the complete exomes of the 247 lung adenocarcinoma tumor samples along with matched normal cells obtained from the same patients. Fully annotated clinical data were also available, providing an unprecedented opportunity to assess the impact of genomic alterations on clinical outcomes.

Genomic portrait of resectable hepatocellular carcinomas: implications of RB1 and FGF19 aberrations for patient stratification.

Unlabelled: Hepatic resection is the most curative treatment option for early-stage hepatocellular carcinoma, but is associated with a high recurrence rate, which exceeds 50% at 5 years after surgery. Understanding the genetic basis of hepatocellular carcinoma at surgically curable stages may enable the identification of new molecular biomarkers that accurately identify patients in need of additional early therapeutic interventions. Whole exome sequencing and copy number analysis was performed on 231 hepatocellular carcinomas (72% with hepatitis B viral infection) that were classified as early-stage hepatocellular carcinomas, candidates for surgical resection.