I'm @LMigliet, a passionate Data Scientist with a rich background in Biology and Bioinformatics. My journey intersects data science, healthcare, and biological research, leveraging the power of machine learning to explore and understand complex biological processes.

Author of over 20 peer-reviewed articles and 5 international patents at Imperial College London. I am one of the inventors of Data-Driven Multiplexing, a timely and cost-effective framework to detect multiple pathogens in a SINGLE MOLECULAR TEST.

• 🌟 Data Scientist: Expertise in data analysis, machine learning, and predictive modeling.
• 🧬 Bioinformatician: Deep understanding of biological data, especially in genomics and molecular biology.
• 🧑‍🔬 Biologist: Practical experience in laboratory techniques, including PCR, dPCR, and other molecular biology methods.
• 💼 Data Engineer: Skilled in building data pipelines, ensuring data integrity, and optimizing performance for large datasets.

• Healthcare: Utilizing data science to improve patient outcomes, personalize medicine, and advance healthcare technologies.
• Machine Learning: Exploring innovative algorithms to gain insights from biological data and enhance predictive accuracy.
• Biological Processes: Investigating molecular mechanisms and genetic interactions through computational approaches.
• Collaboration: Eager to work on interdisciplinary projects that bridge the gap between biology and data science.

• 🌱 Exploring biological processes using advanced machine learning techniques.
• 🔬 Developing data processing pipelines for high-throughput biological data.
• 🤝 Looking to collaborate on data science projects that have a significant impact on healthcare and biological research.

A Framework for Hybrid Development of Multiplex PCR Assays (from singleplex data) - PATENTED

• Branch: "smartplexer_paper_nature" for the code reported in the Nature Paper.
• Description: Optimizes the selection of primer mixes for multiplex PCR assays through empirical testing and in-silico simulations.
• Performance: Demonstrated excellent selection of multiplex assay with over 95% chance to be suitable for Data-Driven Multiplexing (ACA). Capable of detecting 7 different CORONAVIRUS (including COVID) strains in single-well and single-channel PCR reaction (with conventional PCR instruments).

An Advanced Filtering Technique for Biological Data

• Description: Implements an adaptive filtering technique to enhance the accuracy and reliability of biological data analysis.
• Applications: Remove outliers or non-efficient sigmoidal signals for better PCR performance. Paper

Enhancing PCR-Based Diagnostics with Machine Learning

• Description: Combines machine learning algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays. Accurate Classification of 5 CPE genes in single-well and single-channel PCR reaction. Paper
• Applications: Identification of the five major carbapenem-resistant genes in clinical CPO-isolates. Paper
• Performance: Demonstrated excellent predictive performance with 99.6% accuracy.

• 💼 LinkedIn
• 🐦 X @LucaMigliettaIC

I'm always open to new opportunities and collaborations. Let's connect and make a difference together!