All Things Bioinformatics

Jay Chung

Data Science & Machine Learning for Biology

Categories

All (7)

Autoencoder (2)

Caret (2)

Convolutional Neural Networks (2)

Data Leakage (1)

Deep Learning (3)

DepMap (4)

Elastic Net (2)

ESM2 (3)

Keras (3)

Machine Learning (4)

Multi-omics (2)

Pandas (1)

Protein Language Models (3)

Python (4)

R (3)

Random Forest (2)

Scikit-Learn (1)

Self-Attention (1)

Evaluating Data Leakage in Protein Binding Affinity Prediction

Python

Deep Learning

Convolutional Neural Networks

Self-Attention

Keras

ESM2

Protein Language Models

Data Leakage

Data leakage is a critical issue in machine learning, especially in the context of protein-protein interaction (PPI) prediction. Data leakage occurs when information from…

Peptide-MHCII Binding Affinity Prediction with Convolutional Neural Networks and Protein Large Language Model

Python

Deep Learning

Convolutional Neural Networks

Keras

ESM2

Protein Language Models

To accurately predict peptide-MHCII binding affinity, I developed a 1D Convolutional Neural Network (CNN) combined with Multi-Layer Perceptron (MLP) model using protein…

Predicting Peptide-MHC Class II Binding with ESM2 and Neural Networks

Python

Deep Learning

Keras

ESM2

Protein Language Models

In this post, I will build a neural network to predict the binding affinity between peptides and MHC Class II alleles, utilizing embeddings extracted from the Evolutionary…

Machine learning for drug sensitivity prediction (Part 3): Training an Elastic Net gene expression model to predict Erlotinib sensitivity

R

DepMap

Machine Learning

Elastic Net

Caret

In my previous posts (part 1 and part 2), I explored whether using autoencoders (AE) to compress high-dimensional multi-omics data could improve the performance of drug…

Training a Random Forest model with Scikit-Learn on DepMap data

Python

Machine Learning

DepMap

Random Forest

Scikit-Learn

Pandas

In this specific blog post, we will explore the Cancer Dependency Map (DepMap) dataset to understand the relationship between genetic dependencies (CRISPR knockout effects)…

Machine learning for drug sensitivity prediction (Part 2): will deep features improve accuracy?

R

DepMap

Autoencoder

Multi-omics

Machine Learning

Random Forest

Elastic Net

Caret

In Part 1, I explored using autoencoders (AE) to compress high-dimensional DepMap omics data into deep features. I visualized these features using UMAP and observed that…

Machine learning for drug sensitivity prediction (Part 1): using autoencoders to extract deep features from DepMap OMICS

R

DepMap

Autoencoder

Multi-omics

Machine Learning

During my time in the industry, I have analyzed quite a few cell panel screens aiming to identify biomarkers of drug sensitivity. These screens are often carried out by…

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