NB
N. Brouwer
Contributed
5 records found
Strategies for Fine-Tuning Geneformer to Predict the Exposure Level of Cancer Cells to Treatments
A Comparison of Different Fine-Tuning Strategies for Foundation Models
Studying the interactions of genes within a cell is an area of significant interest in the field of medicine as it can provide answers to what exactly drives the behavior of a cell under specific circumstances, such as diseases. Once understood, gene interactions can enable the s
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Attention on Genes
Unveiling Key Genes For Cancer Cell-state Predictions of the Geneformer Model by Inspecting the Attention Weights
Geneformer is a transformer which is pretrained on Geneformer-30M, a dataset consisting of 29.9 million healthy cells. This paper focuses on how Geneformer shifts its attention, when fine-tuned on a dataset of cancer cells, whose gene expression is expected to be distinct, and wh
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As a cell, is it better to be single?
Exploring the feasibility of fine-tuning Geneformer on bulk RNA sequencing data
Powerful new machine learning models in biomedicine are being developed constantly, further hastened by the advent of transformer-based architectures. These advanced systems can be used for various applications, from diagnostics to assessing drug effectiveness. Many of these are
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Evaluating Machine Learning Approaches for Predicting Drug Response in Cancer Cells
A Comparative Analysis of Geneformer and Support Vector Machine
Accurately predicting how cancer cells respond to drug treatment is important to advance drug development. This paper presents a comparative analysis of Geneformer, a deep-learning transformer pre-trained on transcriptomic data, and Support Vector Machine. Using the Sciplex2 data
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Comparative Analysis of Geneformer and Traditional Machine Learning Techniques in Predicting Perturbation Combination Efficacy on Cancer Cell Lines
An Empirical Evaluation Using the sciplex2 Dataset
Cancer poses a significant clinical, social, and economic burden, necessitating the development of effective treatments. Understanding how drugs interact with cancer cells and their downstream effects is critical for creating new therapies and overcoming drug resistance. This pap
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