How do cancer cells differ from healthy cells? A new machine learning algorithm called "ikarus" knows the answer, reports a team led by MDC bioinformatician Altuna Akalin in the journal Genome Biology. The AI program has found a gene signature characteristic of tumors. When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets--be it for stock market analysis, image and speech recognition, or the classification of cells.

When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets – be it for stock market analysis, image and speech recognition, or the classification of cells. To reliably distinguish cancer cells from healthy cells, a team led by Dr. Altuna Akalin, head of the Bioinformatics and Omics Data Science Platform at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), has now developed a machine learning program called "ikarus." The program found a pattern in tumor cells that is common to different types of cancer, consisting of a characteristic combination of genes. According to the team's paper in the journal Genome Biology, the algorithm also detected types of genes in the pattern that had never been clearly linked to cancer before.

How do cancer cells differ from healthy cells? A new machine learning algorithm called "ikarus" knows the answer, reports a team led by MDC bioinformatician Altuna Akalin in the journal Genome Biology. The AI program has found a gene signature characteristic of tumors. When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets – be it for stock market analysis, image and speech recognition, or the classification of cells.

How do cancer cells differ from healthy cells? A new machine learning algorithm called "ikarus" knows the answer, reports a team led by MDC bioinformatician Altuna Akalin in the journal Genome Biology. The AI program has found a gene signature characteristic of tumors. When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets – be it for stock market analysis, image and speech recognition, or the classification of cells.

Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You'd spend way more time on preparation, than actually cooking. For computational biologists, it's been a similar time-consuming process for analyzing genomics data. Before they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models. To streamline this process, researchers from the Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC) developed a universal programming tool that converts a wide variety of genomics data into the required format for analysis by deep learning models.

A new deep-learning algorithm can quickly and accurately analyze several types of genomic data from colorectal tumors for more accurate classification, which could help improve diagnosis and related treatment options, according to new research published in the journal Life Science Alliance. Colorectal tumors are extremely varied in how they develop, require different drugs and have very different survival rates. Often, they are classified into subtypes based on analysis of gene expression levels. "Disease is much more complex than just one gene," said Altuna Akalin, bioinformatics scientist who leads the Bioinformatics Platform research group at MDC's Berlin Institute of Medical Systems Biology (BIMSB). "To appreciate the complexity, we have to use some kind of machine learning to really make use of all the data."