In the rapidly evolving biotech industry, manual analysis of DNA/RNA plates is often time-consuming and prone to errors, leading to costly delays and increased operational expenses. A 2021 study highlighted that errors in genetic sequences have compromised the integrity of numerous studies, emphasizing the need for more accurate and efficient analysis methods.
To address these challenges, a leading biotech firm collaborated with Working Excellence to implement a machine learning solution aimed at minimizing errors and streamlining the analysis process.
Introduction
The biotech sector demands precision and efficiency, particularly in processes involving DNA/RNA plate analysis. Manual methods are not only labor-intensive but also susceptible to errors, which can lead to significant project delays and increased costs. Recognizing these challenges, a prominent biotech company sought to automate their analysis process to enhance accuracy and operational efficiency.
Problem Statement
The company faced several critical issues:
Manual Analysis Errors:
Traditional manual analysis of DNA/RNA plates was error-prone, leading to compromised data integrity.
Robotic Inaccuracies:
Automated systems frequently missed wells, necessitating the refilling of entire plates and further delaying processes.
Operational Inefficiencies:
The combination of manual errors and robotic inaccuracies resulted in project delays and escalated operational costs.
Overview: AI Solution for Biotech
Working Excellence implemented a comprehensive solution to address these challenges:
1. Development of a Deep Neural Network Model: Utilizing Keras, a deep learning framework, the team developed a model to enhance the engineering process, improving accuracy in plate analysis.
2. Creation of a Custom Labeling Application: A bespoke application was built using React.JS and .NET Core to maintain assets and integrate seamlessly with the trained model, facilitating efficient data management.
3. Enhanced Feature Engineering and Regularization: Advanced techniques were employed to enable reliable automatic classification of filled plates into three categories: empty, non-empty, or tilted, reducing the likelihood of errors.
Implementation Process
Step 1: Needs Assessment
- Conducted a thorough analysis of existing workflows to identify specific pain points in the DNA/RNA plate analysis process.
Step 2: Model Development
- Developed a deep neural network model using Keras to accurately classify plate statuses, enhancing the precision of analyses.
Step 3: Application Development
- Built a custom labeling application with React.JS and .NET Core to facilitate seamless integration and asset management.
Step 4: Feature Engineering
- Applied advanced feature engineering and regularization techniques to improve model reliability and classification accuracy.
Step 5: System Integration and Testing
- Integrated the model and application into existing workflows and conducted rigorous testing to ensure system robustness and efficiency.
Results and Benefits
By the Numbers:
50% Reduction in Manual Analysis Time
Automation significantly decreased the time required for plate analysis, allowing for faster project completion.
70% Decrease in Plate Refilling Rate
40% Improvement in Operational Efficiency
Qualitative Benefits:
Enhanced Data Accuracy: The integration of machine learning reduced errors, leading to more reliable data for decision-making.
Resource Optimization: Automation allowed skilled personnel to focus on more complex tasks, improving productivity.
Competitive Advantage: The implementation of advanced technologies positioned the company ahead of competitors in process efficiency.
Lessons Learned
Strategic Integration of AI/ML: Incorporating machine learning solutions requires careful planning and alignment with existing workflows to achieve desired outcomes.
Importance of Custom Solutions: Tailored applications, such as the custom labeling tool, are crucial for meeting specific operational needs and ensuring seamless integration.
Continuous Improvement: Ongoing evaluation and refinement of AI models and processes are essential to maintain efficiency and adapt to evolving challenges.
Final Thoughts
By partnering with Working Excellence, the biotech firm successfully transformed its DNA/RNA plate analysis process through the integration of machine learning and custom applications. This collaboration not only enhanced operational efficiency but also set a benchmark for leveraging AI/ML solutions in the biotech industry.
Explore how Working Excellence can revolutionize your biotech processes with cutting-edge AI and machine learning solutions. Contact us or request a demo today!