Publications by authors named "Taein Kang"
Article Synopsis
- A systematic evaluation is necessary to understand how different model architectures and training strategies affect the performance of genomics models, prompting the organization of a DREAM Challenge.
- In the challenge, competitors used a vast dataset of yeast DNA sequences and expression levels to train models, with the best models employing various neural network architectures and training approaches.
- The development of the Prix Fixe framework allowed for an in-depth analysis of these models, leading to improved performance, and demonstrating that top models not only excelled on yeast data but also outperformed existing benchmarks in Drosophila and human datasets.
Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression.
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- The study presents an innovative high-throughput method to analyze millions of random promoters, aiming to decode the rules behind gene expression.
- Researchers developed a new transformer model called Proformer, which processes DNA sequences to accurately predict gene expression values using unique architectural features like additional feed-forward layers and convolution.
- Proformer outperforms traditional methods by effectively managing training data size and introduces improved techniques for understanding how regulatory sequences influence gene expression.
Purpose: Spatiotemporal regulation of cell membrane dynamics is a major process that promotes cancer cell invasion by acting as a driving force for cell migration. Beta-Pix (βPix), a guanine nucleotide exchange factor for Rac1, has been reported to be involved in actin-mediated cellular processes, such as cell migration, by interacting with various proteins. As yet, however, the molecular mechanisms underlying βPix-mediated cancer cell invasion remain unclear.
View Article and Find Full Text PDFβPix is a guanine nucleotide exchange factor for the Rho family small GTPases, Rac1 and Cdc42. It is known to regulate focal adhesion dynamics and cell migration. However, the role of βPix is currently not well understood.
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