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GTC ON-DEMAND

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Abstract:
We'll discuss how interpretable deep learning can significantly advance our understanding of genomic regulation. All our cells have the same DNA sequence, yet different cell-types express different genes in a process called genomic regulation. This regulation is driven by binding regulatory proteins to DNA. The vast majority of disease-associated mutations do not disrupt the DNA sequences of genes, but rather disrupt DNA sequences important for regulatory protein binding. Unfortunately, conventional computational models fail to explain which regulatory proteins are impacted for over 90 percent of such mutations. We show that by using deep learning coupled with our interpretation algorithms DeepLIFT (https://github.com/kundajelab/deeplift) and TF-MoDISco (https://github.com/kundajelab/tfmodisco) we can explain a substantially greater fraction of mutations that impact genomic regulation and obtain novel biological insights that are not provided by other methods.
We'll discuss how interpretable deep learning can significantly advance our understanding of genomic regulation. All our cells have the same DNA sequence, yet different cell-types express different genes in a process called genomic regulation. This regulation is driven by binding regulatory proteins to DNA. The vast majority of disease-associated mutations do not disrupt the DNA sequences of genes, but rather disrupt DNA sequences important for regulatory protein binding. Unfortunately, conventional computational models fail to explain which regulatory proteins are impacted for over 90 percent of such mutations. We show that by using deep learning coupled with our interpretation algorithms DeepLIFT (https://github.com/kundajelab/deeplift) and TF-MoDISco (https://github.com/kundajelab/tfmodisco) we can explain a substantially greater fraction of mutations that impact genomic regulation and obtain novel biological insights that are not provided by other methods.  Back
 
Topics:
Genomics & Bioinformatics, Deep Learning & AI Frameworks, Computational Biology & Chemistry
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9632
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Abstract:

Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand. However, this is difficult if the model is a black box. We present algorithms that provide detailed explanations for individual predictions made by a deep learning model and discover recurring patterns across the entire dataset. Our algorithms address significant limitations of existing interpretability methods. We show examples from genomics where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights.

Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand. However, this is difficult if the model is a black box. We present algorithms that provide detailed explanations for individual predictions made by a deep learning model and discover recurring patterns across the entire dataset. Our algorithms address significant limitations of existing interpretability methods. We show examples from genomics where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights.

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Topics:
AI in Healthcare, Genomics & Bioinformatics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8907
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Abstract:
We'll present novel algorithms to help interpret deep learning models. Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand, but this is difficult if the model is a black box. We'll present algorithms that address significant limitations of previous approaches to interpretability. Our algorithms can provide detailed explanations for individual predictions made by a deep learning model and can also discover recurring patterns across an entire dataset. We'll show examples from genomics and computer vision, including cases where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights that aren't provided by other methods. The algorithms developed are domain-agnostic and can work with any deep learning architecture.
We'll present novel algorithms to help interpret deep learning models. Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand, but this is difficult if the model is a black box. We'll present algorithms that address significant limitations of previous approaches to interpretability. Our algorithms can provide detailed explanations for individual predictions made by a deep learning model and can also discover recurring patterns across an entire dataset. We'll show examples from genomics and computer vision, including cases where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights that aren't provided by other methods. The algorithms developed are domain-agnostic and can work with any deep learning architecture.  Back
 
Topics:
Healthcare and Life Sciences, AI in Healthcare, Artificial Intelligence and Deep Learning, Computational Biology & Chemistry
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7669
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