Recent advances in next-generation sequencing (NGS) technologies have provided us with an unprecedented opportunity to better characterize the molecular signatures of human cancers. One hallmark of cancer genomes is aneuploidy, which engenders abnormal copy numbers amongst broadly connected sets of alleles. Structural variations (SVs) further modify the aneuploid cancer genomes into a mixture of rearranged genomic segments with extensive somatic copy number alterations (CNAs).
Insulin-like growth factor 1 (IGF1) signaling is involved in the initiation and progression of a subset of breast cancers by inducing cell proliferation and survival. Although the signaling cascade following IGF1 receptor activation is well studied, the key elements of the robust transcriptional response governing IGF1’s actions are not well understood.
The amount of data available due to the rapid spread of advanced information technology is exploding. It is expected that this data will be efficiently utilized for data-driven decision making, which is crucial, in particular, for interdisciplinary research where a comprehensive picture of the subject requires large amounts of data from disparate data sources.
Palindromes or inverted repeats in a genome sequence are a pair of complementary sequences that appear in reverse order. If the two complementary halves appear in tandem, the sequence is referred to as a palindrome; if the two halves are separated by intervening base pairs, they are referred to as an inverse repeat. The location and length of palindromic sequences in the genome may alter DNA replication and gene expression that may lead to genomic instability.
Outbreaks of infectious diseases such as influenza, SARS, and Ebola pose significant threats to human health. Early detection and characterization of outbreaks are crucial to containing and managing them. I will describe previous work on a system that processes emergency department reports for individual patients, computes probabilities of individual diseases, and builds epidemiological models of outbreaks. I will also describe current research to extend this work to cover multiple, simultaneous outbreaks of influenza and influenza-like illnesses. The extension to mu
In this talk we describe a new set of computational tools for manipulating pixel intensities based on optimal transport and hierarchical learning. We will show how these methods can be used to uncover predictive information that is too complex to be extracted by visual examination of raw image data. We will demonstrate these concepts in tasks related to drug discovery, diagnosis of cytology and histopathology images, acinar tissue quantification in pancreatitis from histology, amongst other applications.