AI and Machine Learning Techniques Boost Genetic Sequence Analysis

An established Mexican entrepreneur, Luis Miguel Chong Chong guides Disruptiva Works, S.A. de C.V., and has extensive experience in the food production sphere. Reflecting a background in healthcare and pharmaceutical industries, Luis Miguel Chong Chong maintains a strong interest in human life sciences services, including genomics.

The human genome was first sequenced 15 years ago, and high-throughput sequencing has provided vital tools for accessing genetic data. Unfortunately, it has traditionally been difficult for researchers to differentiate random errors related to the sequencing process from small mutations that may be linked to cancer and other diseases.

A recent MIT Technology Review article drew attention to improvements in AI techniques and machine learning that are enabling more efficient ways of analyzing the vast quantities of data contained within the genetic sequence. Google’s DeepVariant automatically transforms readouts from high-throughput sequencing into a comprehensive picture of the entire genome. In particular, tiny single-base-pair mutations and deletion and insertion mutations within the sequence can be efficiently identified and examined in depth.

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