Cassava (Manihot esculenta) is the main food staple in Ghana, crucial for sustenance and income for many families. It makes up 22% of Ghana's Agricultural Gross Domestic Product (AGDP) and with an impressive per capita consumption rate of 152 kg, it is also responsible for 30% of the average daily calorie intake (Acheampong et al., 2021). Cassava is a prime example of an ‘orphan crop’, i.e. crops which have been under-researched in comparison to worldwide cash crops such as maize or rice (Yaqoob et al., 2023). One of cassava’s most intriguing secrets is its resistance to drought (Yaqoob et al., 2023), which can help the world face the challenges of climate change and a growing population.
A farmer working in a cassava plantation in Ghana.
In changing environmental conditions, such as exposure to drought, an organism responds by activating specific genes. Key regulators of gene activity are parts of DNA called ‘enhancers’ - specific sections of DNA that act like power boosters to increase gene expression. Standard genomics methods struggle to identify enhancers accurately because their activity is highly dependent on the context around them and they can be very far from their target genes (Wang et al., 2020).
An activator protein bound to DNA at an upstream enhancer sequence can attract proteins to the promoter region that activate RNA polymerase (green) and thus transcription. The DNA can loop around on itself to cause this interaction between an activator protein and other proteins that mediate the activity of RNA polymerase. (Nature Education)
In recent years, natural language processing (NLP) and its concept of attention have successfully been applied to similar challenges in genomics (Dalla-Torre et al., 2023). The AgroNT model was recently developed by InstaDeep in collaboration with Google Cloud, providing an unsupervised foundational NLP model pre-trained on DNA sequences from 56 plant species, with a primary focus on edible plants. This model has been evaluated across numerous species and complex genomic prediction tasks including regulatory features, gene-based annotation, RNA processing, and gene expression.
In this hackathon, we will leverage experimentally mapped cassava enhancers and InstaDeep’s AgroNT model to build predictors for enhancer regulatory activity in the cassava genome.
About InstaDeep (www.instadeep.com)
InstaDeep Ltd is an EMEA leader in decision-making AI products for the Enterprise, with headquarters in London, and offices in Paris, Tunis, Lagos, Dubai and Cape Town. With expertise in both machine intelligence research and practical business deployments, the Company provides a competitive advantage to its partners in an AI-first world. Leveraging its extensive know-how in GPU-accelerated computing, deep learning and reinforcement learning, InstaDeep has built products and solutions that tackle the most complex challenges across a range of industries. The firm’s hands-on approach to research, combined with a broad spectrum of clients, ensures an exciting and rewarding environment to work and thrive in. InstaDeep has also developed collaborations with global leaders in the Artificial intelligence ecosystem, such as Google DeepMind, Nvidia and Intel.
The error metric for this competition is Accuracy.
For every row in the dataset, submission files should contain 2 columns: ID and Target.
Your submission file should look like:
ID Target
ID_3qQWny 0
ID_S4Jc8H 1
1st winner: $1 500 USD
2nd winner: $1 000 USD
3rd winner: $500 USD
There are 500 Zindi points available. You can read more about Zindi points here.
Prizes will only be awarded to registered attendees of Deep Learning Indaba Ghana 2023.
This challenge starts on 5 September 17:00 PM.
Competition closes on 9 September 14:00 PM.
The private leaderboard will be revealed on 8 September 14:00 PM.
We reserve the right to update the contest timeline if necessary.
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A README markdown file is required
It should cover:
Your code needs to run properly, code reviewers do not have time to debug code. If code does not run easily you will be bumped down the leaderboard.
Consequences of breaking any rules of the competition or submission guidelines:
Monitoring of submissions
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