PerkinElmer
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PerkinElmer Signals Screening

Signals Screening is an intuitive, configurable, and flexible screening workflow processing engine on top of the unparalleled data visualization and analysis capabilities of TIBCO Spotfire. The initial offering of Signals Screening addresses three key drug discovery assays:

  • Basic Screening
  • High Content Screening (HCS)
  • Surface Plasmon Resonance (SPR)
See What’s New in the latest Signals Screening Release.

Part Number SignalsS
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Overview

From plate readers to high content data to SPR, Signals Screening processes raw data from multiple instrument sources, so scientists only need to learn one software package for all their data analysis needs. This one platform allows data comparisons from different experiments and instruments increasing confidence that the results are real. Templates can be saved for procedures that are run over and over providing a nearly automated analysis decreasing repetition and increasing the consistency of data processing day to day, within groups and across departments. Analyzing raw data directly output from an instrument means less data wrangling, cutting and pasting, decreasing chances for human error. Custom integration with electronic notebooks or corporate databases further increases data integrity.

User will benefit from:

  • Protocol templates: once set up, can be used multiple times
  • One Software package for multiple instruments and assay types saves time and increases data accuracy
  • Graphics driven by Spotfire enables meaningful visualization of data
  • Standard Statistical Analysis driven by Spotfire provides cluster analyses and unsupervised machine learning
  • Simplified reporting with easy export into Powerpoint, Excel and PDF format

Resources, Events & More
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Poster

An Automated Deep-Learning Workflow for High Content Screening

Imaging-based phenotypic screening of cell-based disease models has become an indispensable tool for modern drug discovery. Despite the growing adoption of high-content screening (HCS), analyzing the complex imaging data produced by these systems can take weeks and typically requires handson programming by data scientists.

Recent advances in deep learning have enabled the possibility of automating these analyses. In this work we present a framework to analyze multiple image datasets with minimal tuning or optimization.

PDF 1 MB

What’s New in Signals Screening

White Paper

Digital Transformation Journeys for Materials Science

Recent times have demonstrated the need for digital transformation, often in harsh terms, including a loss of R&D productivity. As a result, R&D organizations in advanced chemical manufacturing must find ways to improve efficiency in an increasingly challenging environment. This white paper provides guidance on which technologies yield improved turnaround times, smarter questions, and faster answers.

PDF 1 MB
High Content Screening in Three Dimensions

Researchers are increasingly looking to 3D cell cultures, microtissues, and organoids to bridge the gap between 2D cell cultures and in vivo animal models. This whitepaper documents a streamlined procedure for getting the most information, as quickly as possible, using solutions from PerkinElmer.

PDF 1 MB