BLOCK 1 BIO5 Flashcards
DeepMind’s AlphaFold is known for its breakthrough in which area?
AlphaFold has made significant breakthroughs in protein structure prediction by solving the protein folding problem
Which data type often requires AI and big data techniques for analysis in proteomics?
Big data
How do neural networks help in predicting protein structures?
Neural networks leverage their ability to learn complex patterns from data to predict protein structures
- Input Data
- Neural Network Architecture: CNN and RNN
-Training with Known Structures
-Predicting Protein Structures
Why is big data crucial for protein-protein interaction studies?
Overall, big data plays a crucial role in advancing our understanding of protein-protein interactions by providing the necessary scale, depth, and complexity required for comprehensive analysis and interpretation of biological networks.
Which of the following databases is commonly used for retrieving protein structures for AI modeling?
Uniprot Database
The AlphaFold Protein Structure Database
Which of the following best describes the main challenge of protein folding that AI aims to tackle?
The main challenge of protein folding that AI aims to tackle is the prediction of a protein’s three-dimensional structure solely based on its amino acid sequence.
What feature do many deep learning models in protein science leverage for sequence pattern recognition?
Many deep learning models in protein science leverage the amino acid sequence itself for sequence pattern recognition.
These models often represent amino acids as vectors or embeddings and process them through recurrent neural networks (RNNs), convolutional neural networks (CNNs) or transformer-based architectures to extract features and make predictions about the protein.
Which of the following statements best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Artificial Intelligence (AI): A subset of computer science that enables machines to carry out tasks more traditionally done by humans
Machine Learning (ML): a subset of Ai that enables computers to effectively learn from data, without being specifically programmed to do so
Deep Learning (DL): further sub-field of ML that seeks to deal with information with a logic structure similar to how a human would draw conclusions
Transfer learning, a method where pre-trained models are fine-tuned for a new task, has been adapted in protein prediction tasks. What’s the main advantage of this approach?
The main advantage of using transfer learning in protein prediction tasks is that it allows leveraging knowledge and insights gained from pre-trained models on large datasets for related tasks
-Reduced Data Requirements
-Improved Performance
-Faster Training
-Domain Adaptation
What is a potential pitfall when training AI models on protein databases that have bias towards well-studied proteins?
A potential pitfall when training AI models on protein databases biased towards well-studied proteins is the lack of generalization to proteins that are less well-studied or structurally diverse.
-Bias in Training Data
-Limited Generalization
-Biased Predictions
Which of the following statements accurately differentiates between Supervised, Unsupervised, and Reinforcement Learning?
Supervised: A ML that uses labeled datasets to train algorithms to predict outcomes and recognize patterns.
Unsupervised: A type of ML that learns from data without human supervision to train algorithms with unlabeled data and allowed to discover patterns and insights without any explicit guidance
Reinforcement Learning: A (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals
Which of the following best describes Neural Networks in the context of Machine Learning?
Neural networks in the context of machine learning are computational models inspired by the structure and function of the human brain.
Which of the following best describes the phenomenon of overfitting in machine
learning models?
Overfitting, is where a model learns to capture noise or random fluctuations in the training data rather than the underlying patterns or relationships.
This results in a model that performs well on the training data but fails to generalize to new, unseen data.
In machine learning, why is data typically split into training, validation, and test sets?
Training Set: The model learns to capture patterns and relationships in the data by adjusting its parameters (e.g., weights in a neural network)
Validation Set: Is used to evaluate the performance of the model during training and to tune parameters. Validation set helps prevent overfitting to the training data, as it provides an independent measure of the model’s generalization performance
Test Set: It serves as an unbiased evaluation of the model’s ability to generalize to new, unseen data
The test set should be kept completely separate from the training and validation sets to ensure an objective assessment
In the context of deep learning, particularly in models like Transformers, what role does the “attention” mechanism play?
In deep learning models like Transformers, the “attention” mechanism plays a crucial role in capturing dependencies between different parts of the input sequence and focusing on relevant information during processing.
