Ling Shen

US Patent:

20230065917, Mar 2, 2023

Filed:

Jan 29, 2021

Appl. No.:

17/759714

Inventors:

- South San Francisco CA, US
Ling SHEN - Redwood City CA, US
Hui XU - Fremont CA, US
Daniel SERIE - San Mateo CA, US

Assignee:

Venn Biosciences Corporation - South San Francisco CA

International Classification:

G01N 33/574
G01N 33/68
G16B 40/10

Abstract:

Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as but not limited to, cancer (e.g., ovarian), an autoimmune disease, fibrosis and aging conditions. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning algorithms.

US Patent:

20190316209, Oct 17, 2019

Filed:

Apr 15, 2019

Appl. No.:

16/384784

Inventors:

- Menlo Park CA, US
Samuel S. Gross - Sunnyvale CA, US
Darya Filippova - Sunnyvale CA, US
Ling Shen - Redwood City CA, US
Oliver Claude Venn - San Francisco CA, US
Alexander Weaver Blocker - Mountain View CA, US
Nan Zhang - Fremont CA, US
Tara Maddala - Sunnyvale CA, US
Alex Aravanis - San Mateo CA, US
Qinwen Liu - Fremont CA, US
Anton Valouev - Palo Alto CA, US
Virgil Nicula - Cupertino CA, US

International Classification:

C12Q 1/6886
G16H 50/30
G16H 50/20
C12Q 1/6806
C40B 40/06
C12Q 1/6869

Abstract:

A predictive cancer model generates a cancer prediction for an individual of interest by analyzing values of one or more types of features that are derived from cfDNA obtained from the individual. Specifically, cfDNA from the individual is sequenced to generate sequence reads using one or more physical assays, examples of which include a small variant sequencing assay, whole genome sequencing assay, and methylation sequencing assay. The sequence reads of the physical assays are processed through corresponding computational analyses to generate each of small variant features, whole genome features, and methylation features. The values of features can be provided to a predictive cancer model that generates a cancer prediction. In some embodiments, the values of different types of features can be separately provided into different predictive models. Each separate predictive model can output a score that can serve as input into an overall model that outputs the cancer prediction.