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It is well known that science has given us large amounts of free accessible data, such as https://www.1000genomes.org and https://www.ncbi.nlm.nih.gov/genbank. How can we play around with the data and apply data science/machine learning to it? What could be some ideas?

My own ideas:

  • Biological data visualisation
  • Gene prediction using hidden-markov-model

Any more?

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  • Determine the function of genes and the elements that regulate genes throughout the genome.
  • Find variations in the DNA sequence among people and determine their significance. The most common type of genetic variation is known as a single nucleotide polymorphism or SNP (pronounced “snip”). These small differences may help predict a person’s risk of particular diseases and response to certain medications.
  • Discover the 3-dimensional structures of proteins and identify their functions.
  • Explore how DNA and proteins interact with one another and with the environment to create complex living systems.
  • Develop and apply genome-based strategies for the early detection, diagnosis, and treatment of disease.
  • Sequence the genomes of other organisms, such as the rat, cow, and chimpanzee, in order to compare similar genes between species.
  • Develop new technologies to study genes and DNA on a large scale and store genomic data efficiently.
  • Continue to explore the ethical, legal, and social issues raised by genomic research.

Source: https://ghr.nlm.nih.gov/handbook/genomicresearch?show=all

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You may build models to classify genomes by population. Run unsupervised learning (clustering) to see if populations are reconstructed in the model. Build models to infer missing genotypes

To do a Scalable DNA analysis you may check Adam software based on Apache Spark

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