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We strive to bring the technology we create closer to you. Our discoveries in the areas of genomics and artificial intelligence are unique in the world and contribute to the ever-improving fight against cancer.

We simplify the functioning of genomics pipelines

The roots of MNM go back to the world of science, where sharing knowledge is essential. When academics openly publish the results of their research as soon as possible, then the whole science community reacts, and provides feedback or debate. This amazing mechanism assures constant development and self-regulation of mainstream science.

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Introducing Random Similarity Forests

In our recent paper, we introduce Random Similarity Forest, a machine learning algorithm capable of handling datasets with features of arbitrary data types while retaining each feature’s characteristic.

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Where is the AI in HealthTech?

If you suspect that there is an AI bubble in HealthTech – you are 100% right. So where really is the AI in HealthTech? The validation study performed by our partner on MNM’s AI tool for predicting the response of cancer patients to CDK4/6i treatment is a good example of how advanced the AI in HealthTech is.

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Are deep learning models exponentially more efficient than conventional models in learning complex patterns from DNA sequencing data?

A pilot study utilizing 4174 samples from The Cancer Genome Atlas (#TCGA) has published results of a 77.6% accuracy in predicting cancer types using the raw tumor sequences that integrate all somatic mutations and germline variants.

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Copy-number signatures - a new family member among cancer mutational signatures

Chromosomal instability (CIN) is a term referring to abnormal cytogenetic behavior such as gains, losses and rearrangements of chromosomes. CIN often leads to serious consequences for a cell and is a hallmark of cancer.

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Prediction of drug response with mutational signatures. Correlated mutational signatures and the drug phenotyping

There is strong evidence that alterations in the cancer genome can significantly affect the sensitivity and probability of response to treatment. Single gene mutations are increasingly being used as clinical guidance to enable an optimal predictive of anti-cancer therapies, e.x correlated with DNA repair pathways.

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What do we know about somatic noncoding mutation patterns in cancer?

Over the last couple of years, with incredible achievements in the field of DNA sequencing, scientists were able to identify many somatic driver mutations in protein-coding regions of the genome. This part of DNA comprises only 2% of the human genome. What about the remaining 98%?

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$100 Human Genome – is that finally possible?

The development of new genomic techniques is truly astonishing. More than twenty years after having the first sequenced human genome, the price of such service had fallen from billions of dollars to just $1000. But that’s not enough!

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Improvements in sequencing methods

The discovery of genomic and epigenetic changes in individual cancers has led to many medical innovations and therapeutic successes in oncology. This has underpinned the development of precision oncology, used as molecular profiling of tumors to identify changes that may be targeted for therapy.

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