In the UmojaHack Tunisia hackathon we invited participants to predict the functional class of enzymes called protein kinases based on their amino acid sequences. For this challenge we’re taking it one step further. Given a library of labelled sequences from some well-known organisms, your task is to create a model that can label sequences from new organisms. Each sequence could represent any kind of enzyme - not just kinases.
All enzymes are made of one or more chains of amino acids, which determine their structure, behaviour, and interactions with other enzymes and molecules. That means it should be possible to predict the protein’s function and behaviour given just the amino acid sequence.
A model able to perform this task would have many applications. In addition to enzymes from known organisms (which we have from studying their proteomes), there are vast numbers of metagenomic sequences - this is proteomic sequence data from environmental samples. Being able to quickly annotate them with function using this model (i.e. going beyond simple sequence similarity) would be indispensable. Models developed in the course of this challenge may contribute to furthering the understanding of the world around us.
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.
This challenge is open to all and not restricted to any country.
Teams and collaboration
You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.
Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.
Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).
Datasets and packages
The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.
You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.
If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.
You may use pretrained models as long as they are openly available to everyone.
The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.
You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.
Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.
Submissions and winning
You may make a maximum of 10 submissions per day.
The maximum number of submissions for this competition is 300 overall per user or team.
Before the end of the competition, you need to choose 2 submissions to be judged on for the private leaderboard. If you do not make a selection your 2 best public leaderboard submissions will be used to score on the private leaderboard.
These models often need to be applied at scale, so large ensembles aren’t encouraged. To incentivise more lightweight solutions, we are adding an additional submission criteria: your submission should take a reasonable time to train and run inference. Specifically, we should be able to re-create your submission on a single-GPU machine (eg Nvidia P100) with less than 8 hours training and two hours inference.
Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 50% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 50% of the test dataset, will be made public and will constitute the final ranking for the competition.
If you are in the top 20 at the time the leaderboard closes, we will email you to request your code. On receipt of email, you will have 48 hours to respond and submit your code following the submission guidelines detailed below. Failure to respond will result in disqualification.
If your solution places 1st, 2nd, or 3rd on the final leaderboard, you will be required to submit your winning solution code to us for verification, and you thereby agree to assign all worldwide rights of copyright in and to such winning solution to Zindi.
If two solutions earn identical scores on the leaderboard, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).
The winners will be paid via bank transfer, PayPal, or other international money transfer platform. International transfer fees will be deducted from the total prize amount, unless the prize money is under $500, in which case the international transfer fees will be covered by Zindi. In all cases, the winners are responsible for any other fees applied by their own bank or other institution for receiving the prize money. All taxes imposed on prizes are the sole responsibility of the winners.
You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.
Zindi is committed to providing solutions of value to our clients and partners. To this end, we reserve the right to disqualify your submission on the grounds of usability or value. This includes but is not limited to the use of data leaks or any other practices that we deem to compromise the inherent value of your solution.
These models often need to be applied at scale, so large ensembles aren’t encouraged. Your submission should take a reasonable time to train and run inference. Specifically, we should be able to re-create your submission on a single-GPU machine (eg Nvidia P100) with less than 8 hours training and two hours inference.
Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.
Consequences of breaking any rules of the competition or submission guidelines:
Monitoring of submissions
The evaluation metric for this challenge is Accuracy. You must submit predicted labels for each SEQUENCE_ID.
Your submission should look like the following:
The top 10 submissions will earn up to 2000 Zindi Points.
The top three data scientists will be eligible for an interview for the opportunity to join Instadeep.
Competition closes on 21 February 2021.
Final submissions must be received by 11:59 PM GMT.
We reserve the right to update the contest timeline if necessary.