Lecture 11b Machine Learning and Bioinformatics
46 Questions
0 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to Lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What is used to calculate the error on the output layer in a neural network?

  • Cost function
  • Activation function
  • Performance metric
  • Loss function (correct)

    • In a fully connected neural network, each neuron in one layer is connected to every neuron in the previous layer.

    True (A)

    Name a type of neural network that is specifically designed for analyzing image data.

    Convolutional Neural Network (CNN)

    A gradient value is calculated by multiplying the delta by the ______.

    <p>weight</p> Signup and view all the answers

    Match the following terms with their definitions:

    <p>Loss Function = Calculates the error of the network Gradient = Indicates the direction and magnitude to adjust weights Fully Connected Layers = Neurons in one layer are connected to every neuron in the next layer Convolution Layer = Applies filters to analyze image data</p> Signup and view all the answers

    What is the main purpose of Machine Learning (ML)?

    <p>To enable computers to learn from data (D)</p> Signup and view all the answers

    Supervised Learning involves using known outputs to train the model.

    <p>True (A)</p> Signup and view all the answers

    Name one type of activation function used in neural networks.

    <p>ReLU, Sigmoid, or Tanh.</p> Signup and view all the answers

    Deep Learning refers to the use of multiple layers of __________ for data analysis.

    <p>artificial neurons</p> Signup and view all the answers

    Match the types of learning with their descriptions:

    <p>Supervised Learning = Uses labeled data for training Unsupervised Learning = Finds patterns in unlabeled data Deep Learning = Involves many layers of neurons Reinforcement Learning = Learns through feedback from actions</p> Signup and view all the answers

    Which of the following is NOT a type of neural network mentioned?

    <p>Support Vector Network (B)</p> Signup and view all the answers

    Activation functions allow neural networks to approximate any function.

    <p>True (A)</p> Signup and view all the answers

    What is the main function of the back-propagation method?

    <p>To train deep neural networks by calculating errors.</p> Signup and view all the answers

    In K-Means Clustering, the goal is to partition data into __________ distinct clusters.

    <p>K</p> Signup and view all the answers

    Which activation function bounds the output between -1 and 1?

    <p>Tanh (D)</p> Signup and view all the answers

    What do convolution filters learn primarily?

    <p>Features of an image (A)</p> Signup and view all the answers

    Convolutional Neural Networks (CNNs) have fully connected layers that classify images.

    <p>True (A)</p> Signup and view all the answers

    What year were transformers introduced?

    <p>2017</p> Signup and view all the answers

    The ability to correctly identify true positives is known as _______.

    <p>sensitivity</p> Signup and view all the answers

    Match the following applications to their corresponding neural network:

    <p>DeepBind = Identification of DNA motifs iDeepS = Prediction of RNA motifs</p> Signup and view all the answers

    What capability do transformers have that sets them apart from CNNs?

    <p>They allow encoding positional information all at once (A)</p> Signup and view all the answers

    Transformers are replacing CNNs and RNNs in many problem domains.

    <p>True (A)</p> Signup and view all the answers

    What aspect of biological systems makes traditional machine learning approaches difficult?

    <p>Noise and complexity</p> Signup and view all the answers

    Neural networks learn important features from _______ sources of experimental data.

    <p>multiple</p> Signup and view all the answers

    What is the main purpose of the Receiver Operating Characteristic (ROC) curve?

    <p>To measure the quality of a classification model (A)</p> Signup and view all the answers

    What type of neural network is used by Google's Deep Variant to analyze DNA read mappings?

    <p>Convolutional Neural Network (A)</p> Signup and view all the answers

    The Broad Institute's DL SNP discovery system identifies polymorphisms solely through the use of CNNs.

    <p>False (B)</p> Signup and view all the answers

    What framework is used to classify Arabidopsis plants by variety?

    <p>CNN-LSTM framework</p> Signup and view all the answers

    Google's Deep Variant encodes DNA read mappings as an ______ image.

    <p>RGB</p> Signup and view all the answers

    Match the following systems to their described functions:

    <p>Google's Deep Variant = Analyzes DNA read mappings as RGB images Broad Institute's DL SNP discovery = Identifies regions surrounding potential polymorphisms CNN-LSTM framework = Classifies plants by variety Read tensor = Encodes sequence alignment and read characteristics</p> Signup and view all the answers

    What combination of information does the first mention utilize for SOTA predictions?

    <p>Primary sequence information and RNA 2-D structure (C)</p> Signup and view all the answers

    Region identification for SNP Discovery Systems is based exclusively on DNA sequences.

    <p>False (B)</p> Signup and view all the answers

    What is encoded in a 'read tensor' in the Broad Institute's SNP discovery system?

    <p>Sequence alignment, read characteristics, and quality</p> Signup and view all the answers

    What type of model is DNA-BERT based on?

    <p>Bidirectional encoder representations from transformers (A)</p> Signup and view all the answers

    AlphaFold was awarded a Nobel Prize for its contributions to protein structure prediction.

    <p>False (B)</p> Signup and view all the answers

    What is the median accuracy achieved by AlphaFold2 across all categories?

    <p>93</p> Signup and view all the answers

    DNA-BERT has achieved SOTA performance in predicting ______, splice-sites, and transcription factor binding sites.

    <p>promoters</p> Signup and view all the answers

    Match the following deep learning models with their primary function:

    <p>CNN = Develop visual features for classification LSTM = Analyze changes over time DNA-BERT = Learn genomic regulatory grammar AlphaFold = Predict protein structures</p> Signup and view all the answers

    Which of the following elements is crucial for AlphaFold's protein structure predictions?

    <p>10^300 possible conformations for proteins (C)</p> Signup and view all the answers

    RoseTTAFold outperforms AlphaFold in terms of computational power required for predictions.

    <p>True (A)</p> Signup and view all the answers

    What is the primary improvement offered by Meta's new protein folding software compared to AlphaFold2?

    <p>It speeds up protein folding predictions by 60 times. (D)</p> Signup and view all the answers

    What significant achievement did AlphaFold2 accomplish in 2020?

    <p>Achieved a median accuracy of 93 across all categories</p> Signup and view all the answers

    AlphaFold3 shows higher accuracy in predicting protein-ligand docking compared to classical tools like AutoDock.

    <p>True (A)</p> Signup and view all the answers

    What major challenge does AlphaFold3 face in predictions?

    <p>Static structure predictions.</p> Signup and view all the answers

    One of the applications of protein structure predictions is in ______ design.

    <p>drug</p> Signup and view all the answers

    Match the following features with their descriptions:

    <p>AlphaFold2 = Initial version of protein folding prediction software AlphaFold3 = Latest AI-based tool with improved accuracy Neural Networks in Bioinformatics = Increasingly popular for handling complex datasets Black-Box Nature = Uncertainty in understanding prediction mechanisms</p> Signup and view all the answers

    Flashcards

    Machine Learning

    Using algorithms to let computers learn from data without specific rules, enabling tasks like prediction.

    Supervised Learning

    Machine learning where a model learns to map input to known outputs for predictions.

    Unsupervised Learning

    Machine learning where a model learns patterns and structures from data without known outputs.

    Deep Learning

    Using multiple layers of artificial neurons for data analysis, improving models' accuracy.

    Signup and view all the flashcards

    Artificial Neuron

    A function combining weighted inputs, a bias, and an activation function to process information.

    Signup and view all the flashcards

    Activation Function

    Adds non-linearity to neural networks, allowing them to approximate complex functions.

    Signup and view all the flashcards

    Multi-layer Perceptron

    A type of artificial neural network with multiple layers of interconnected neurons.

    Signup and view all the flashcards

    Feedforward Network

    A neural network where information flows in one direction, without cycles.

    Signup and view all the flashcards

    Backpropagation

    A common method for training neural networks, adjusting weights based on errors.

    Signup and view all the flashcards

    ReLU Activation Function

    A popular activation function where output is the maximum between input and zero.

    Signup and view all the flashcards

    Delta

    The contribution of a neuron to the overall error.

    Signup and view all the flashcards

    Fully Connected Neural Network

    A neural network where each neuron in one layer is connected to every neuron in the next.

    Signup and view all the flashcards

    Convolutional Neural Network (CNN)

    A neural network designed for image data, using filters to detect patterns.

    Signup and view all the flashcards

    Overfitting

    A problem where a neural network performs extremely well on the training data but poorly on unseen data.

    Signup and view all the flashcards

    Deep Variant (Google)

    A method that uses RGB images of DNA read mappings to identify polymorphisms using a CNN.

    Signup and view all the flashcards

    DL SNP discovery system (Broad Institute)

    Finds potential polymorphisms by analyzing "read tensors".

    Signup and view all the flashcards

    CNN

    A type of neural network that excels at analyzing images and identifying patterns.

    Signup and view all the flashcards

    RNA 2-D structure prediction

    Predicting the shape of RNA molecules using primary sequence information.

    Signup and view all the flashcards

    SOTA predictions

    The state-of-the-art models for a particular task.

    Signup and view all the flashcards

    Phenotype/Genotype Prediction

    A method to predict an organism's traits (phenotype) from its DNA sequence (genotype).

    Signup and view all the flashcards

    Polymorphism

    Variations in the DNA sequence among individuals

    Signup and view all the flashcards

    Read tensors

    A form of data representation used in genomics and biology that encode alignments and quality metrics regarding DNA read data.

    Signup and view all the flashcards

    Protein Folding Software

    Computer programs that predict the 3D structure of proteins from their amino acid sequence.

    Signup and view all the flashcards

    AlphaFold3

    Latest AI-based protein structure prediction tool from Google DeepMind. It predicts structures for proteins, nucleic acids, ligands, and ions.

    Signup and view all the flashcards

    Diffusion-based Architecture

    A neural network architecture that uses a probabilistic approach to iteratively refine the predicted structure until it converges on a likely solution.

    Signup and view all the flashcards

    Protein-Ligand Docking

    Predicting how a small molecule (ligand) binds to a protein, which is essential for drug design and development.

    Signup and view all the flashcards

    Black-Box Problem

    The difficulty in understanding how complex AI models (like AlphaFold3) make their predictions, even though they accurately predict protein structures.

    Signup and view all the flashcards

    Convolutional Filters (CNNs)

    Learned filters in CNNs that extract features (like edges, corners, textures) from images.

    Signup and view all the flashcards

    Fully Connected Layers (CNNs)

    Layers in CNNs that process data from earlier layers to make classifications (e.g., identifying objects in images).

    Signup and view all the flashcards

    Transformers

    A type of neural network architecture that excels at understanding sequence data and has proven quite useful for tasks like natural language processing.

    Signup and view all the flashcards

    Self-Attention (Transformers)

    A mechanism in transformers that allows the model to weigh the importance of different parts of an input sequence when processing it.

    Signup and view all the flashcards

    Sensitivity (ROC)

    The ability of a model to correctly identify true positives.

    Signup and view all the flashcards

    Specificity (ROC)

    The ability of a model to correctly identify true negatives.

    Signup and view all the flashcards

    DeepBind

    A CNN used to locate DNA motifs that proteins bind to.

    Signup and view all the flashcards

    iDeepS

    Utilizes two convolutional neural networks and a Recurrent Neural network (RNN) to predict RNA motifs that proteins bind to.

    Signup and view all the flashcards

    Bioinformatics

    Using computational methods to understand biological data, such as DNA, RNA sequences and protein structures

    Signup and view all the flashcards

    CNN for Plant Classification

    A Convolutional Neural Network (CNN) is used to extract visual features from images of plants. These features are then used to classify plants into different ecotypes.

    Signup and view all the flashcards

    LSTM for Plant Growth

    A Long Short-Term Memory (LSTM) network analyzes changes in plant images over time (growth), helping to classify the plants based on their developmental trajectory.

    Signup and view all the flashcards

    DNA-BERT: Genomics Grammar

    DNA-BERT is a language model that treats genomic DNA like a text with its own grammar rules. It can learn these rules and predict genomic elements like promoters and binding sites.

    Signup and view all the flashcards

    What does DNA-BERT predict?

    It can predict promoters, splice sites, and transcription factor binding sites in DNA using its learned grammar.

    Signup and view all the flashcards

    AlphaFold: Protein Folding Challenge

    AlphaFold is a deep learning model that solves the problem of predicting 3D protein structures, a complex task with huge computational challenges.

    Signup and view all the flashcards

    AlphaFold's Accuracy

    AlphaFold2 achieved a median accuracy of 93% in predicting protein structures, considered comparable to experimental results.

    Signup and view all the flashcards

    AlphaFold: Release of Protein Structures

    The AlphaFold team released predicted structures for numerous proteins across many model species, a significant contribution to the field.

    Signup and view all the flashcards

    RoseTTAFold Inspiration

    Inspired by AlphaFold, RoseTTAFold also predicts protein structures with almost equal accuracy but requires less computational power.

    Signup and view all the flashcards

    Study Notes

    Introduction to Machine Learning and Bioinformatics

    • Bioinformatics utilizes machine learning techniques to analyze and interpret biological data.
    • Machine learning (ML) is a subset of artificial intelligence (AI) focused on enabling computers to learn from experiences.
    • Deep learning is a subset of machine learning that makes the computation of multi-layer neural networks feasible.

    Artificial Intelligence

    • Artificial intelligence encompasses any technique enabling computers to mimic human behavior.

    Machine Learning

    • Machine learning is the study and application of algorithms to enable computers to learn from data without explicit rules.
    • It employs a paradigm of providing training data to the computer system followed by testing with unseen data.
      • Supervised learning builds a model to transform input data into known outputs to make predictions. This includes classification and regression.
      • Unsupervised learning builds a model of data without known outputs. This includes clustering, pattern discovery, dimensionality reduction, and feature learning.
    • Specific supervised learning methods include:
      • Support Vector Machine (SVM)
      • Linear Regression
      • Logistic Regression
    • Specific unsupervised learning methods include:
      • K-Means Clustering
      • Hierarchical Clustering
      • Principle Component Analysis (PCA)

    Deep Learning

    • Deep learning utilizes artificial neurons to build layered models for data analysis.
    • Applicable to both supervised and unsupervised learning.
    • Deep learning models utilize various neural network architectures, including fully connected, convolutional, and recurrent networks.
    • Current advancements in hardware, along with increased access to large datasets, have boosted the popularity of deep learning.
    • Deep learning models often do not require significant feature engineering.

    Artificial Neuron

    • An artificial neuron takes multiple inputs.
    • Inputs are multiplied by weights.
    • Bias may be added to weighted sum.
    • Weighted sum is processed by an activation function to propagate the signal.

    Multilayer Perceptron (MLP)

    • A type of neural network containing interconnected layers of artificial neurons.
      • The input layer receives input data.
      • The hidden layers process the data.
      • The output layer provides the final prediction/classification.
      • The loss layer measures the difference between predicted and actual values.

    Activation Functions

    • Activation functions introduce non-linearity to neural networks, allowing them to approximate any function.
    • Specific activation functions include:
      • Sigmoid/Logistic - Output values between 0 and 1, centered on 0.5
      • Tanh - Output values between -1 and 1, centered at zero
      • ReLU - Output is either the input or zero, whichever is greater.

    Feedforward Neural Networks

    • A type of artificial neural network with no cycles within the network graph.
    • Includes single-layer perceptrons and multilayer perceptrons.
    • Employs back-propagation.

    Backpropagation

    • The most popular method for training deep neural networks.
    • The difference between known results and model output is calculated as an error term.
    • Error propagates backward through the network to adjust weights.

    Fully Connected Neural Networks

    • A neural network design where each neuron in a layer is connected to every neuron in the subsequent layer. The input layer receives data as tensors.
    • Often used in preliminary stages of training before introducing more robust models such as convolutional networks.

    Convolutional Neural Networks (CNNs)

    • A popular class for processing image data.
    • Composed of convolutional layers, pooling layers, and fully connected layers.
    • Convolutional layers employ kernels (filters) of fixed size to analyze the input image.
    • These kernels learn image features as a convolution with the image.
    • The final layers learn classifications.
    • CNNs excel at preserving spatial relationships of image data unlike fully connected networks.

    CNN Learned Filters

    • Convolutional neural networks learn filters that detect specific features in images.
    • These filters are visualized as grayscale image examples.

    CNN Classifier

    • CNNs can classify images, such as identifying objects within a scene composed of multiple objects.

    CNN for Medical Imaging

    • CNNs are applicable to medical imaging data.
    • They involve the use of convolutional layers (filters) to process images like MRI, X-ray imaging.

    Transformers

    • Introduced in 2017, Transformers are a state-of-the-art neural network architecture.
    • Utilizes 'self-attention' to analyze data.
    • Enables parallel execution or encoding of positional data.
    • Replacing standard recurrent models in NLP and other tasks.

    Transformers and Attention

    • Analyzing input data, focusing on specific parts within data, and predicting outputs.

    Receiver Operating Characteristic (ROC) Curve

    • A graph visualizing the performance of a binary classifier system in terms of true positive rate against false positive rate.

    Confusion Matrix

    • A table visualizing the performance of a classification model summarizing correct and incorrect predictions into four buckets: true positives, true negatives, false positives, and false negatives.

    Applications in Bioinformatics

    • Deep learning is a growing field used in bioinformatics, capable of handling large datasets.
    • Applications include DNA/RNA binding motif determination, SNP genotyping, and phenotype/genotype estimations.

    AlphaFold2

    • A deep learning model for protein structure prediction.
    • Employs a sequence-based approach to predict protein structures.
    • Accurately predicts protein structures from amino acid sequences.
    • Used in protein structure determination.

    AlphaFold3

    • The latest iteration of AlphaFold, improving upon the previous version by incorporating and potentially refining previous prediction models and techniques.
    • Improved accuracy in predicting protein structures to handle various situations in the target data itself.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    Machine Learning and Bioinformatics Lecture Notes PDF

    Description

    Explore the intersection of machine learning and bioinformatics in this quiz. Learn how machine learning techniques are employed to analyze biological data and the various subsets like deep learning and supervised learning. This quiz will help solidify your understanding of how artificial intelligence mimics human behavior in data processing.

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser