WorksheetsCSCI 2952G Enformer Quiz
Total questions: 10
Worksheet time: 5mins
Which statement explains how base pairs can interact to influence gene expression?
Base pairs do not interact, so each pair should be assessed individually and without context
It is best to only consider base pairs that are next to each other when predicting gene expression
Base pairs that are not near each other sequentially can still influence each other
Base pairs that are far apart are typically more interactive than base pairs that are close together
Which of the following is not a benefit of dilated convolution?
It is less demanding on computational resources
It can help understand long-range interactions more efficiently than normal convolution
It is not a black-box model and instead can display causality
Which of the following correctly compares the Enformer model with attention layers, the Enformer model with dilated convolutional layers, and the Basenji2 model?
There was no substantial difference between the three models tested
The Enformer model with attention layers outperformed the convolutional Enformer in all test cases
The Enformer model with dilated convolution was, on average, the best-performing model
Neither Enformer model was able to improve on Basenji2
In the Enformer paper, "multi-head attention" is used. Which of the following is true?
All of the attention heads were initialized with the exact same random weights to facilitate training.
All attention heads had different random initializations to allow diversity to be learned
The Enformer model trained with multi-head attention, but used a single attention head at inference.
Each attention head deployed different dilated convolutions to allow for diverse self-attention.
In the ABC model for enhancer-gene matching, what does "A" stand for?
Attention
Activation
Activity
Athletics
The state of the art model that Enformer was extensively compared to in the paper was called:
(a)
Which of the following was not a task included in the Enformer paper's results section?
Transcription factor binding prediction
Enhancer-gene matching
Narrowing disease-associated regions found from genome-wide association studies (GWAS)
eQTL identification via in silico mutagenesis (ISM)
What is the main advantage of the use of multi headed attention layers over an LSTM?
Long range dependency capture which avoids the vanishing gradient problem
Ability to process input sequences in parallel
A memory gate to keep track of long term dependencies
They are essentially equivalent
What is the main advantage of the use of multi headed attention layers over an RNN?
Attention can allow outputs to depend on any piece of the input without the vanishing gradient problem
A memory gate to keep track of long term dependencies
A recursive structure which is quick to train
Which of the following is a limitation of Enformer?
The model is not able to take in sequences longer than 60 kbp
The model cannot generalize to novel cell types or assays beyond those in the training set
The model cannot generalize to predict eQTLs, TAD boundaries, or do enhancer-gene matching
Enformer is limited by the inclusion of convolutions and could benefit from self-attention like Basenji2
