Kevin A. Janes

ORCID: 0000-0002-8028-6138
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About
Contact & Profiles
Research Areas
  • Gene Regulatory Network Analysis
  • Bioinformatics and Genomic Networks
  • Cancer Cells and Metastasis
  • Single-cell and spatial transcriptomics
  • Cancer Genomics and Diagnostics
  • Cell Image Analysis Techniques
  • Gene expression and cancer classification
  • Computational Drug Discovery Methods
  • RNA Research and Splicing
  • MicroRNA in disease regulation
  • interferon and immune responses
  • Viral Infections and Immunology Research
  • Neurogenesis and neuroplasticity mechanisms
  • Lung Cancer Research Studies
  • Genetics, Bioinformatics, and Biomedical Research
  • Nutrition, Genetics, and Disease
  • Glutathione Transferases and Polymorphisms
  • RNA Interference and Gene Delivery
  • Cellular Mechanics and Interactions
  • Glycosylation and Glycoproteins Research
  • Genomics and Chromatin Dynamics
  • Epigenetics and DNA Methylation
  • Glioma Diagnosis and Treatment
  • Breast Cancer Treatment Studies
  • Protein Kinase Regulation and GTPase Signaling

University of Virginia
2015-2024

University of Virginia Cancer Center
2023-2024

University of Virginia Health System
2022-2023

University of Rochester Medical Center
2023

Charlottesville Medical Research
2011-2017

Harvard University
2006-2010

Massachusetts Institute of Technology
2004-2009

Decision Systems (United States)
2007

Pfizer (United States)
2006

Center for Cancer Research
2005

Signal transduction pathways control cellular responses to stimuli, but it is unclear how molecular information processed as a network. We constructed systems model of 7980 intracellular signaling events that directly links measurements 1440 response outputs associated with apoptosis. The accurately predicted multiple time-dependent apoptotic induced by combination the death-inducing cytokine tumor necrosis factor prosurvival factors epidermal growth and insulin. By capturing role...

10.1126/science.1116598 article EN Science 2005-12-08

Immunoblotting (also known as Western blotting) combined with digital image analysis can be a reliable method for analyzing the abundance of proteins and protein modifications, but not every immunoblot-analysis combination produces an accurate result. I illustrate how sample preparation, protocol implementation, detection scheme, normalization approach profoundly affect quantitative performance immunoblotting. This study implemented diagnostic experiments that assess workflow accuracy...

10.1126/scisignal.2005966 article EN Science Signaling 2015-04-07

Gene expression networks are complicated by the assortment of regulatory factors that bind DNA and modulate transcription combinatorially. Single-cell measurements can reveal biological mechanisms hidden population averages, but their value has not been fully explored in context mRNA regulation. Here, we adapted a single-cell profiling technique to examine gene program downstream Forkhead box O (FOXO) during 3D breast epithelial acinar morphogenesis. By analyzing patterns fluctuations among...

10.1073/pnas.1103423108 article EN Proceedings of the National Academy of Sciences 2011-08-22

Abstract Spheroid and organoid cultures are powerful in vitro models for biology, but size shape diversity within the culture is largely ignored. To streamline morphometric profiling, we developed OrganoSeg, an open-source software that integrates segmentation, filtering, analysis archived brightfield images of 3D culture. OrganoSeg more accurate flexible than existing platforms, illustrate its potential by stratifying 5167 breast-cancer spheroid 5743 colon colorectal-cancer morphologies....

10.1038/s41598-017-18815-8 article EN cc-by Scientific Reports 2018-03-28

Highlights•Small molecule inhibitors of CBFβ-RUNX protein-protein interaction developed.•Inhibitors alter occupancy RUNX1 on target genes and their expression.•Inhibitors show efficacy against leukemia cell lines basal-like (triple negative) breast cancer lines.Transcription factors are proteins that bind to DNA regulate how much other made. We describe the development between two transcription factors, CBFβ RUNX. Both these targets alterations in human as well a number solid tumors. The...

10.1016/j.ebiom.2016.04.032 article EN cc-by-nc-nd EBioMedicine 2016-04-30

Abstract In this article we describe our preliminary work involving the use of depolymerized, low molecular weight chitosan nanoparticles as carriers for proteins and peptides. We hypothesized that could favorably modulate particle protein release characteristics delivery certain bioactive macromolecules. Our primary objectives were to develop nanoparticle formulations stable reproducible across a range weights then characterize physicochemical in vitro properties functions polymer size....

10.1002/app.12016 article EN Journal of Applied Polymer Science 2003-03-26

Cell-signaling networks consist of proteins with a variety functions (receptors, adaptor proteins, GTPases, kinases, proteases, and transcription factors) working together to control cell fate. Although much is known about the identities biochemical activities these signaling ways in which they are combined into process transduce signals poorly understood. Network-level understanding requires data on wide processes such as posttranslational modification, assembly macromolecular complexes,...

10.1074/mcp.m500158-mcp200 article EN cc-by Molecular & Cellular Proteomics 2005-07-20

Biological signaling networks process extracellular cues to control important cell decisions such as death–survival, growth–quiescence, and proliferation–differentiation. After receptor activation, intracellular proteins change in abundance, modification state, enzymatic activity. Many of the have been identified, but it is not known how molecules work together decisions. To begin address this issue, we report use partial least squares regression an analytical method glean signal–response...

10.1089/cmb.2004.11.544 article EN Journal of Computational Biology 2004-08-01
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