Deep Genomics Incorporated

Hui Yuan Xiong

Brendan John Frey

Andrew Delong

Hannes Bretschneider

Babak Alipanahi

Andrew Thomas DeLong

Leo J Lee

Michael K K Leung

Brendan Frey

Andrew Thomas Delong

Michael Kk Leung

Described herein are systems and methods that receive as input a DNA or RNA sequence, extract features, and apply layers of processing units to compute one ore more condition-specific cell variables, corresponding to cellular quantities measured under different conditions. The system may be applied to a sequence containing a genetic variant, and also to a corresponding reference sequence to determine how much the condition-specific cell variables change because of the variant. The change in the condition-specific cell variables are used to compute a score for how deleterious a variant is, to classify a variant's level of deleteriousness, to prioritize variants for subsequent processing, and to compare a test variant to variants of known deleteriousness. By modifying the variant or the extracted features so as to incorporate the effects of DNA editing, oligonucleotide therapy, DNA- or RNA-binding protein therapy or other therapies, the system may be used to determine if the deleterious effects of the original variant can be reduced.

Systems and methods for classifying, prioritizing and interpreting genetic variants and therapies using a deep neural network

Methods and systems for expanding a training set of one or more original biological sequences are provided. An original training set is obtained, wherein the original training set comprises one or more original biological sequences. Saliency values corresponding to one or more elements in each of the one or more original biological sequences are obtained. For each of the original biological sequences, one or more modified biological sequences are produced and the one or more modified biological sequences are associated with the original biological sequence. One or more elements are generated in each of the one or more modified biological sequences using one or more elements in the associated original biological sequence and the corresponding saliency values. The one or more modified biological sequences for each of the original biological sequences are added to the original training set to form an expanded training set.

Methods and systems for producing an expanded training set for machine learning using biological sequences

Systems and methods for training a neural network or an ensemble of neural networks are described. A hyper-parameter that controls the variance of the ensemble predictors is used to address overfitting. For larger values of the hyper-parameter, the predictions from the ensemble have more variance, so there is less overfitting. This technique can be applied to ensemble learning with various cost functions, structures and parameter sharing. A cost function is provided and a set of techniques for learning are described.

System and method for training neural networks

We describe systems and methods for generating and training convolutional neural networks using biological sequences and relevance scores derived from structural, biochemical, population and evolutionary data. The convolutional neural networks take as input biological sequences and additional information and output molecular phenotypes. Biological sequences may include DNA, RNA and protein sequences. Molecular phenotypes may include protein-DNA interactions, protein-RNA interactions, protein-protein interactions, splicing patterns, polyadenylation patterns, and microRNA-RNA interactions, which may be described using numerical, categorical or ordinal attributes. Intermediate layers of the convolutional neural networks are weighted using relevance score sequences, for example, conservation tracks. The resulting molecular phenotype convolutional neural networks may be used in genetic testing, to identify drug targets, to identify patients that respond similarly to a drug, to ascertain health risks, or to connect patients that have similar molecular phenotypes.

Systems and methods for generating and training convolutional neural networks using biological sequences and relevance scores derived from structural, biochemical, population and evolutionary data

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Toronto, Canada

Hui Yuan Xiong