Recurrent Neural Networks Can Boost Upstream Processes
Recurrent neural networks (RNNs) could optimize biopharmaceutical processes and even identify potential deviations during process development, according to new research.
Related Articles
Fed-Batch Data Inadequate for Continuous Cultivation in Certain Systems
Researchers assessed the transferability of B. licheniformis from batch and fed-batch fermentation to continuous cultivation.
U.K. Feels Bullish on Commercial Advanced Therapy Manufacturing
The U.K.’s advanced therapy sector is increasing its capability to manufacture at the commercial scale.
Strategies for Successful Integration of Computational and Empirical Data for Protein Drug Discovery
In this GEN webinar, our subject matter experts, Dr. Lee Fader, Dr. Jeremy Dupaul-Chicoine, and Dr. Maximilian Ebert, will provide an overview of current methodologies…
Touching Base
Biotech news and conversation from the editors of Genetic Engineering & Biotechnology News
Electrified Purification Could Eliminate Problematic Buffers
Electrified purification is based on a conductive polymer that changes in response to electric signals.
TG Therapeutics Licenses Maxcyte Tech to Develop Cell Therapies for Autoimmune Disease
MaxCyte’s technology will support TG Therapeutics’ efforts to develop a CD19 CAR-T cell therapy program for autoimmune diseases.
NanoMosaic and BMS Investigate Nanoneedle Technology to Improve the AAV Manufacturing Process
Nanoneedle technology offers the capability to simultaneously quantify titers of true, full viral genomes and a range of partial species.
Managing Multiple Biomarker Therapeutic Development
On this episode of GEN Live, we’ll give you a full AGBT breakdown. Experts from the NGS, single cell, and spatial fields join the show…
Macrophages in Newborn, But Not Adult, Mice Help Regenerate Heart Tissue
Understanding why newborns, but not adults, can regenerate the heart could lead to treatments that “reprogram” adult macrophages.
AI-Enabled Gene Editing Produces Fewer Off-Target Outcomes
By creating gene editors not found in nature, or optimizing existing editors, AI can improve the accuracy, effectiveness, and accessibility of gene editing.