Hepatocytes don’t proliferate in culture, so the group created a screen for compounds that would cause the cells to self-renew. Also, a new visualization tool allows researchers to see their results overlaid on their multiwell plate experiments ( Bioinformatics, 32:3210-12, 2016).Īpplication example: Aiming to create human replacement livers, Sangeeta Bhatia’s MIT lab cocultured two cell types, fibroblasts and hepatocytes.
The original version of Analyst, coded in Java, classified only single phenotypes. (See Machine-Learning Glossary at bottom of page.) The latest version of the software, 2.0, is rewritten in Python and is equipped with several machine-learning algorithms that classify multiple biological phenotypes. To address the data problem, Carpenter and her colleagues developed CellProfiler Analyst, an open-source platform that allows researchers to explore and visualize their data. Intro:Soon after the launch of CellProfiler-a popular imaging software platform that allows biologists to recognize different cell types, phases, and conditions-its users were faced with a new problem: How do you process the thousands of measurements for each of hundreds of cells in a single image? “In many cases the data don’t even fit into Excel, and certainly the tools there are limiting,” says developer Anne Carpenter of the Broad Institute of MIT and Harvard University. The Scientist spoke with developers of machine-learning approaches in cell biology to help demystify these tools. Of course, there’s a level of trust involved in allowing machine learning to take the reins. In contrast, unsupervised machine-learning methods mine the data and infer its structure without any training. Once the machine-learning algorithm or program is “trained,” it can be applied to a larger set of data. In a branch of machine-learning methods called supervised learning, those classifications are tested for accuracy by measuring against the test set of data. Imaging applications of machine learning work by breaking an image down into numerical or other descriptors, called “features.” The algorithm then selects and classifies those features.
Cell biology will increasingly rely on machine learning and other computational approaches as automated fluorescence microscopy (high-content screening) continues to capture massive sets of images that can be mined in multiple ways.
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