Patents by Inventor Nikolai YAKOVENKO

Nikolai YAKOVENKO 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).

  • Denoising ATAC-Seq Data With Deep Learning
    Publication number: 20230245718
    Abstract: The present invention provides methods, systems, computer program products that use deep learning with neural networks to denoise ATAC-seq datasets. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying genomic sites of chromatin accessibility in a wide range of tissue and cell types.
    Type: Application
    Filed: April 11, 2023
    Publication date: August 3, 2023
    Applicant: NVIDIA Corporation
    Inventors: Johnny ISRAELI, Nikolai YAKOVENKO
  • Denoising ATAC-seq data with deep learning
    Patent number: 11657897
    Abstract: The present invention provides methods, systems, computer program products that use deep learning with neural networks to denoise ATAC-seq datasets. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying genomic sites of chromatin accessibility in a wide range of tissue and cell types.
    Type: Grant
    Filed: December 31, 2018
    Date of Patent: May 23, 2023
    Assignee: NVIDIA Corporation
    Inventors: Johnny Israeli, Nikolai Yakovenko
  • Genetic mutation detection using deep learning
    Patent number: 11443832
    Abstract: The present disclosure provides methods, systems, and computer program products that use deep learning models to classify candidate mutations detected in sequencing data, particularly suboptimal sequencing data. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying mutations from a wide range of sequencing data.
    Type: Grant
    Filed: March 7, 2019
    Date of Patent: September 13, 2022
    Assignee: NVIDIA Corporation
    Inventors: Johnny Israeli, Avantika Lal, Michael Vella, Nikolai Yakovenko, Zhen Hu
  • SEQUENCE VARIATION DETECTION USING DEEP LEARNING
    Publication number: 20200365234
    Abstract: The present disclosure provides methods, systems, and computer program products that use embeddings of candidate variation information and deep learning models to accurately and efficiently detect variations in biopolymer sequencing data, particularly suboptimal sequencing data.
    Type: Application
    Filed: May 13, 2019
    Publication date: November 19, 2020
    Applicant: NVIDIA Corporation
    Inventors: Nikolai YAKOVENKO, Johnny ISRAELI, Avantika LAL, Michael VELLA, Zhen HU
  • GENETIC MUTATION DETECTION USING DEEP LEARNING
    Publication number: 20200286587
    Abstract: The present disclosure provides methods, systems, and computer program products that use deep learning models to classify candidate mutations detected in sequencing data, particularly suboptimal sequencing data. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying mutations from a wide range of sequencing data.
    Type: Application
    Filed: March 7, 2019
    Publication date: September 10, 2020
    Applicant: NVIDIA Corporation
    Inventors: Johnny ISRAELI, Avantika LAL, Michael VELLA, Nikolai YAKOVENKO, Zhen HU
  • Denoising ATAC-Seq Data With Deep Learning
    Publication number: 20200211674
    Abstract: The present invention provides methods, systems, computer program products that use deep learning with neural networks to denoise ATAC-seq datasets. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying genomic sites of chromatin accessibility in a wide range of tissue and cell types.
    Type: Application
    Filed: December 31, 2018
    Publication date: July 2, 2020
    Applicant: NVIDIA Corporation
    Inventors: Johnny ISRAELI, Nikolai YAKOVENKO