How the 2018 MCBIOS Conference is Revolutionizing Science
Imagine training a computer to recognize the building blocks of life as effortlessly as Facebook recognizes faces in your photos. That's exactly the kind of groundbreaking work presented at the XVth Annual MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference in Starkville, Mississippi in Spring 2018. This gathering of brilliant minds centered around a powerful theme: "Genomics and Big Data."
With 183 registered participants—including 97 students, 13 postdoctoral fellows, and 73 professionals—the conference served as a vibrant marketplace of ideas where biology, computer science, and statistics converged 1 . Researchers shared 157 abstracts across nine breakout sessions, tackling everything from plant genetics to precision medicine 1 .
Computational biology represents the marriage between biological data and computer algorithms to solve complex biological puzzles. Think of it this way: where traditional biologists might use microscopes to examine cells, computational biologists use algorithms to examine patterns within massive biological datasets.
The "Big Data" focus of MCBIOS 2018 highlighted a critical challenge in modern biology: we're generating biological information faster than we can interpret it. From DNA sequences to cellular images, the data deluge requires sophisticated computational approaches to extract meaningful insights.
One of the most compelling stories to emerge from MCBIOS 2018 was the 2018 Data Science Bowl—a global competition to create an algorithm that could automatically identify nuclei in microscope images across different experimental conditions 7 .
Before this breakthrough, biologists had to manually adjust their analysis software for nearly every new set of images—a time-consuming process requiring specialized expertise 7 . The Data Science Bowl set out to change this by creating a universal nucleus detector that could work across various cell types, microscopes, and staining methods without human intervention.
Researchers assembled a massive training set of 37,333 manually annotated nuclei from 841 images across more than 30 different experiments 7 .
The challenge was run on the Kaggle platform with 3,891 teams participating worldwide 7 .
Entries were judged on their ability to accurately segment nuclei in diverse image types, with the final evaluation containing approximately 100,000 nuclei across 3,200 images 7 .
Top teams used deep-learning models—a form of artificial intelligence where neural networks learn patterns directly from data.
The outcomes were staggering:
| Method Type | Configuration Required | Accuracy | Time Investment |
|---|---|---|---|
| Classical Algorithms | Extensive per-experiment |
|
3-5 hours expert time |
| Novice User | Minimal, but limited knowledge |
|
5 hours, poor results |
| Custom U-Net Models | Significant (~20 hours) |
|
20+ hours development |
| Top Competition Models | None |
|
None after development |
Behind every great computational discovery lies a set of powerful tools and resources. The MCBIOS conference highlighted several crucial components of the computational biologist's toolkit:
Advanced data analytics platform that enables complex statistical analysis of biological data.
Parameter optimization in models that automates finding optimal values for computational models.
Image segmentation and pattern recognition that identifies features in biological images without manual rules.
Optimizing experimental plans to maximize information gain while minimizing resources.
The MCBIOS 2018 conference wasn't just about research presentations—it reflected a vibrant, growing scientific community committed to nurturing new talent and exploring diverse biological questions.
The Young Scientist Excellence Award competition highlighted the future of computational biology, with postdoctoral fellows and graduate students presenting groundbreaking work 1 .
The conference's nine breakout sessions showcased the remarkable breadth of computational biology applications 1 :
The proceedings of MCBIOS 2018 offer more than just a snapshot of current research—they provide a roadmap for biology's future.
Through models that can accurately simulate cellular behavior
Through tools that work automatically across diverse conditions
The "No-Boundary Thinking" philosophy championed at the conference points toward a future where biological discovery is limited only by our imagination, not by our analytical capabilities 1 . From optimizing associative learning experiments through computational methods 4 to creating personalized heart cell models for drug testing 9 , the work presented at MCBIOS 2018 demonstrates that the most exciting breakthroughs happen at the intersections between fields.
As we look ahead, the type of research showcased at MCBIOS 2018 promises to accelerate our understanding of life's fundamental processes while delivering practical benefits in medicine, agriculture, and environmental science.