AIMC Topic: Biotechnology
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Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control.
SLAS technology
Jul 9, 2019
During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but also by its sophistication (e.g., inputs, sensors, sampling frequency, analytics). High-throughput liq...
Metabolic cost adaptations during training with a soft exosuit assisting the hip joint.
Scientific reports
Jul 5, 2019
Different adaptation rates have been reported in studies involving ankle exoskeletons designed to reduce the metabolic cost of their wearers. This work aimed to investigate energetic adaptations occurring over multiple training sessions, while walkin...
A Study on the Application and Use of Artificial Intelligence to Support Drug Development.
Clinical therapeutics
Jun 24, 2019
PURPOSE: The Tufts Center for the Study of Drug Development (CSDD) and the Drug Information Association (DIA) in collaboration with 8 pharmaceutical and biotechnology companies conducted a study examining the adoption and effect of artificial intelli...
Biotechnology, Big Data and Artificial Intelligence.
Biotechnology journal
May 27, 2019
Developments in biotechnology are increasingly dependent on the extensive use of big data, generated by modern high-throughput instrumentation technologies, and stored in thousands of databases, public and private. Future developments in this area de...
Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering.
Biotechnology journal
May 2, 2019
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have beco...
SignalP 5.0 improves signal peptide predictions using deep neural networks.
Nature biotechnology
Feb 18, 2019
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish ...
Deep Learning: Current and Emerging Applications in Medicine and Technology.
IEEE journal of biomedical and health informatics
Jan 23, 2019
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical ...
AI Paradigms for Teaching Biotechnology.
Trends in biotechnology
Oct 22, 2018
Artificial intelligence (AI) is profoundly changing biotechnological innovation. Beyond direct application, it is also a useful tool for adaptive learning and forging new conceptual connections within the vast network of knowledge for the advancement...
Deep Learning with Microfluidics for Biotechnology.
Trends in biotechnology
Oct 6, 2018
Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural networks ...
Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.
Biotechnology and bioengineering
Apr 23, 2018
Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monit...
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