公开(公告)号：US11923044B1

公开(公告)日：2024-03-05

申请号：US16896907

申请日：2020-06-09

Applicant: Amazon Technologies, Inc.

Inventor： Alexander Sewall Ford ,  Vanessa Nguyen ,  Layne Christopher Price ,  Franziska Seeger ,  Yen Ling Adelene Sim

IPC: G16B15/00 ,  G16B30/20 ,  G16B40/00

CPC classification number: G16B15/00 ,  G16B30/20 ,  G16B40/00

Abstract: Techniques for predicting a protein sequence are described. An exemplary method includes receiving a request to predict a missing area of a protein's primary sequence and a corresponding three-dimensional position of the missing area; applying a machine learning model to backbone Cartesian coordinates of the protein's primary sequence and a protein vector of a representation of the protein's primary sequence including the missing area to predict a missing area of the protein primary sequence and a corresponding three-dimensional position for the missing area, wherein the machine learning model is selected from the group consisting of: an attention-based machine learning model, a bidirectional long short term memory-based model, and a convolutional neural network-based model; and outputting a result of the machine learning model.

公开(公告)号：US11929152B1

公开(公告)日：2024-03-12

申请号：US16896877

申请日：2020-06-09

Applicant: Amazon Technologies, Inc.

Inventor： Alexander Sewall Ford ,  Zachary Wu ,  Layne Christopher Price ,  Franziska Seeger ,  Yen Ling Adelene Sim

IPC: G01N33/48 ,  G06N3/044 ,  G16B5/20 ,  G16B15/00 ,  G16B40/00

CPC classification number: G16B40/00 ,  G06N3/044 ,  G16B5/20 ,  G16B15/00

Abstract: Techniques for predicting a pair of an enzyme primary sequence and a substrate, and interaction probability for the pair are described. An exemplary method includes receiving a request to predict a pair of an enzyme primary sequence and a substrate, and interaction probability for the pair; combining an enzyme vector, a substrate vector, and an interaction indication for the enzyme and substrate to form a machine learning model input; applying a machine learning model to the machine learning model input to predict the pair of an enzyme primary sequence and a substrate, and interaction probability for the pair; and outputting a result of the application of the machine learning model including the predicted pair of an enzyme primary sequence and a substrate, and interaction probability for the pair.

公开(公告)号：US11216701B1

公开(公告)日：2022-01-04

申请号：US15955617

申请日：2018-04-17

Applicant: Amazon Technologies, Inc.

Inventor： Yen Ling Adelene Sim ,  Andrew Borthwick

IPC: G06K9/62 ,  G06K9/72 ,  G06N20/00 ,  G06F16/28 ,  G06N3/08

Abstract: Techniques for generating record embeddings from structured records are described. A record embeddings generating engine processes structured records to build a token vocabulary. Token embeddings are created for each token in the vocabulary. The token embeddings are trained using a loss function that relates the token embeddings to the record-attribute-data structure of the structured records. A record embedding is assembled from the trained token embeddings.