Patents by Inventor Brendan Frey

Brendan Frey has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20180165412
    Abstract: We describe a system and a method that ascertains the strengths of links between pairs of biological sequence variants, by determining numerical link distances that measure the similarity of the molecular phenotypes of the variants. The link distances may be used to associate knowledge about labeled variants to other variants and to prioritize the other variants for subsequent analysis or interpretation. The molecular phenotypes are determined using a neural network, called a molecular phenotype neural network, and may include numerical or descriptive attributes, such as those describing protein-DNA interactions, protein-RNA interactions, protein-protein interactions, splicing patterns, polyadenylation patterns, and microRNA-RNA interactions. Linked genetic variants may be used to ascertain pathogenicity 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.
    Type: Application
    Filed: December 13, 2017
    Publication date: June 14, 2018
    Inventors: Brendan Frey, Andrew Delong
  • Publication number: 20180137338
    Abstract: Systems and methods that receive as input microscopy images, extract features, and apply layers of processing units to compute one or more set of cellular phenotype features, corresponding to cellular densities and/or fluorescence measured under different conditions. The system is a neural network architecture having a convolutional neural network followed by a multiple instance learning (MIL) pooling layer. The system does not necessarily require any segmentation steps or per cell labels as the convolutional neural network can be trained and tested directly on raw microscopy images in real-time. The system computes class specific feature maps for every phenotype variable using a fully convolutional neural network and uses multiple instance learning to aggregate across these class specific feature maps. The system produces predictions for one or more reference cellular phenotype variables based on microscopy images with populations of cells.
    Type: Application
    Filed: November 16, 2016
    Publication date: May 17, 2018
    Inventors: Oren KRAUS, Jimmy BA, Brendan FREY
  • Publication number: 20180107927
    Abstract: The present disclosure provides methods and systems that can ascertain how genetic variants impact molecular phenotypes. Such methods and systems may use additional conservation information. In an aspect, the present disclosure provides a method for training a molecular phenotype neural network (MPNN), comprising: (a) providing a molecular phenotype neural network (MPNN) comprising one or more parameters; (b) providing a training data set comprising (i) a set of one or more inputs comprising biological sequences and (ii) for each input in the set of one or more inputs, a set of one or more molecular phenotypes corresponding to the input; (c) configuring the one or more parameters of the MPNN based on the training data set to minimize a total loss of the training data set, thereby training the MPNN; and (d) outputting the one or more parameters of the MPNN.
    Type: Application
    Filed: December 13, 2017
    Publication date: April 19, 2018
    Inventor: Brendan Frey
  • Publication number: 20170024642
    Abstract: 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.
    Type: Application
    Filed: March 11, 2016
    Publication date: January 26, 2017
    Inventors: Hui Yuan XIONG, Andrew DELONG, Brendan FREY
  • Publication number: 20160364522
    Abstract: 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.
    Type: Application
    Filed: June 15, 2015
    Publication date: December 15, 2016
    Inventors: Brendan FREY, Michael K.K. LEUNG, Andrew Thomas DELONG, Hui Yuan XIONG, Babak ALIPANAHI, Leo J. LEE, Hannes BRETSCHNEIDER
  • Patent number: 8335683
    Abstract: The present invention involves using one or more statistical classifiers in order to perform task classification on natural language inputs. In another embodiment, the statistical classifiers can be used in conjunction with a rule-based classifier to perform task classification.
    Type: Grant
    Filed: January 23, 2003
    Date of Patent: December 18, 2012
    Assignee: Microsoft Corporation
    Inventors: Alejandro Acero, Ciprian Chelba, Ye-Yi Wang, Leon Wong, Brendan Frey
  • Publication number: 20070104383
    Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.
    Type: Application
    Filed: September 23, 2006
    Publication date: May 10, 2007
    Applicant: MICROSOFT CORPORATION
    Inventors: Nebojsa Jojic, Brendan Frey
  • Publication number: 20070024635
    Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.
    Type: Application
    Filed: September 23, 2006
    Publication date: February 1, 2007
    Applicant: MICROSOFT CORPORATION
    Inventors: Nebojsa Jojic, Brendan Frey
  • Publication number: 20070019884
    Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.
    Type: Application
    Filed: September 23, 2006
    Publication date: January 25, 2007
    Applicant: MICROSOFT CORPORATION
    Inventors: Nebojsa Jojic, Brendan Frey
  • Publication number: 20060190226
    Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.
    Type: Application
    Filed: December 30, 2005
    Publication date: August 24, 2006
    Applicant: Microsoft Corporation
    Inventors: Nebojsa Jojic, Vladimir Jojic, David Heckerman, Brendan Frey, Christopher Meek
  • Publication number: 20060178861
    Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.
    Type: Application
    Filed: December 30, 2005
    Publication date: August 10, 2006
    Applicant: Microsoft Corporation
    Inventors: Nebojsa Jojic, Vladimir Jojic, David Heckerman, Brendan Frey, Christopher Meek
  • Publication number: 20050273325
    Abstract: A method and computer-readable medium are provided for identifying clean signal feature vectors from noisy signal feature vectors. Aspects of the invention use mixtures of distributions of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors.
    Type: Application
    Filed: July 20, 2005
    Publication date: December 8, 2005
    Applicant: Microsoft Corporation
    Inventors: Brendan Frey, Alejandro Acero, Li Deng
  • Publication number: 20050256706
    Abstract: A method and computer-readable medium are provided for identifying clean signal feature vectors from noisy signal feature vectors. One aspect of the invention includes using an iterative approach to identify the clean signal feature vector. Another aspect of the invention includes using the variance of a set of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors.
    Type: Application
    Filed: July 20, 2005
    Publication date: November 17, 2005
    Applicant: Microsoft Corporation
    Inventors: Brendan Frey, Alejandro Acero, Li Deng