MiRNA Proxy for Rapid Modeling of Low Abundance Proteins at Scale

Abstract

We leveraged microRNA (miR) profiles as a proxy for modeling protein expression networks driving biological systems. The aim was to develop a more accurate prediction algorithm for miR:protein interactions, validated with large scale interaction data generated using a flourescent reporter chip system. Three tasks were persued; 1) Producing large-scale experimentally validated miR:protein interaction data, 2) Developing a high performance miR:protein prediction algorithm, 3)Generating transcriptomics data sets for influenza and Zika. A multi-classifier based predictor of miR:protein interactions was developed with data sets being used to edentify miRs as pan-strain and pan-viral targets for anti-viral therapies.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 16, 2020
Accession Number
AD1115286

Entities

People

  • Bin Zhang
  • Elodie Ghedin
  • Lara Mahal

Organizations

  • New York University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Chemistry
  • Data Mining
  • Data Sets
  • Deep Learning
  • Gene Expression
  • Genetic Code
  • Information Science
  • Machine Learning
  • Neural Networks
  • Ontologies
  • Proteins
  • Supervised Machine Learning
  • Transcriptomics
  • Zika Virus

Fields of Study

  • Biology

Readers

  • Distributed Systems and Data Platform Development
  • Molecular Genetics
  • Oncology and Biomarker-Based Cancer Detection.