麻豆淫院

EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses machine learning to gain meaningful insights from complex biological data. Details have been published in Nature Machine Intelligence.

CORNETO enables users to extract molecular networks鈥攎aps of how genes, proteins, and signaling pathways interact鈥攂y combining experimental data from different samples and conditions with prior biological knowledge, such as signaling or metabolic networks. This can help us to better understand the mechanisms that lead a cell to be healthy or diseased.

Understanding how molecules interact inside our cells is key to uncovering the mechanisms that can go wrong, leading to disease. But as the types of omics data available to researchers grow in size and complexity, researchers often struggle to extract useful, meaningful patterns from them.

CORNETO, which stands for Constrained Optimization for the Recovery of NETworks from Omics, combines machine learning techniques with biological prior knowledge to simultaneously analyze multiple types of omics data, including transcriptomics, proteomics, and metabolomics.

"We wanted to solve a common challenge in systems biology: how to make sense of omics data when you have so much complex data available all at once," said Julio Saez-Rodriguez, Head of Research at EMBL-EBI and Professor on leave at Heidelberg University.

"CORNETO helps by combining these complex data with prior information coming from biological databases to find patterns that are consistent, interpretable, and biologically meaningful."

Unified omics analyses

Traditionally, scientists analyze data from one condition at a time鈥攆or example, comparing healthy cells to diseased ones鈥攁nd build separate interaction networks for each. But this approach can miss the bigger picture.

CORNETO uses machine learning to analyze multiple samples or conditions together, highlighting biological processes that are shared across datasets, and pinpointing the differences between samples. CORNETO is also designed to allow researchers to customize it for specific use cases or extend it to new data types as needed.

"Using CORNETO is like finding the common threads in a tangled web," explained Pablo Rodr铆guez-Mier, postdoctoral researcher at Heidelberg University.

"It helps researchers pull out the key biological processes that are happening across many samples and understand what's different or the same in each one."

Real-world applications

Using CORNETO is especially valuable to researchers working in fields like cancer research, where there are similarities across patients, but no two patients are exactly alike. To demonstrate this, the researchers used CORNETO to analyze gene expression data from multiple cancer patients to discover which specific intracellular signaling pathways were behaving abnormally.

Using only transcriptomics data, CORNETO identified key deregulated kinases, enzymes that regulate cell signaling, which were also detected independently using phosphoproteomics. The resulting networks revealed both shared pathways and patient-specific differences, a step toward the kinds of insights that could one day support personalized treatment strategies.

CORNETO is also currently being used in the EU research project to identify deregulated signaling pathways associated with chemotherapy resistance in ovarian cancer patients.

The researchers also used CORNETO to analyze metabolic pathways in yeast strains in which different genes were inactivated. Here, CORNETO was able to find the key processes the yeast cells were using to survive and grow. Understanding these essential processes could help scientists design better yeast strains for making biofuels and other products for industrial manufacturing.

CORNETO is available as .