Mohammed Al Alquraishi

US Patent:

20120244631, Sep 27, 2012

Filed:

Mar 22, 2012

Appl. No.:

13/426824

Inventors:

Harley McAdams - Stanford CA, US
Mohammed AlQuraishi - Stanford CA, US

Assignee:

The Board of Trustees of the Leland Stanford Junior University - Palo Alto CA

International Classification:

G06F 15/00
G01N 33/53
G01N 23/207
G01N 23/00
G01D 21/00
G01R 33/44

US Classification:

436501, 702127, 738658, 324307, 378 73, 250307

Abstract:

Techniques for determining values for a metric of microscale interactions include determining a mesoscale metric for a plurality of mesoscale interaction types, wherein a value of the mesoscale metric for each mesoscale interaction type is based on a corresponding function of values of the microscale metric for the plurality of the microscale interaction types. A plurality of observations that indicate the values of the mesoscale metric are determined for the plurality of mesoscale interaction types. Values of the microscale metric are determined for the plurality of microscale interaction types based on the plurality of observations and the corresponding functions and compressed sensing.

US Patent:

20200234788, Jul 23, 2020

Filed:

Jan 6, 2020

Appl. No.:

16/735354

Inventors:

- Cambridge MA, US
Mohammed AlQuraishi - Cambridge MA, US

International Classification:

G16B 15/00
G16B 45/00

Abstract:

The present disclosure provides for systems and methods for generating and displaying a three dimensional map of a protein sequence. An exemplary method can provide for using deep learning models to predict protein folding and model protein folding using three dimensional representations. The method more effectively exploits the potential of deep learning approaches. The method approach overall involves three stages—computation, geometry, and assessment.

US Patent:

20180150596, May 31, 2018

Filed:

Mar 22, 2017

Appl. No.:

15/466329

Inventors:

- Palo Alto CA, US
Mohammed N. AlQuraishi - Cambridge MA, US
Solomon Itani - Vallejo CA, US
Garry P. Nolan - Menlo Park CA, US
Sean C. Bendall - San Mateo CA, US
Tyler J. Burns - Stanford CA, US

International Classification:

G06F 19/18

Abstract:

A method and apparatus for simultaneously determining multiple different biological molecule types in a sample include labeling each different biological molecule type in a biological sample with a unique combination of a plurality of labels. Each different biological molecule type is selected from a population of M different biological molecules types. The plurality of labels is selected from a population of L different labels; and, M is greater than L. Measurements are obtained of relative abundances of the L different labels in the sample. Relative abundance of up to M different biological molecule types in the sample are determined based on the measurements and a method of compressed sensing.

US Patent:

20140106976, Apr 17, 2014

Filed:

Mar 15, 2013

Appl. No.:

13/834260

Inventors:

Karen Sachs - Palo Alto CA, US
Mohammed N. AlQuraishi - Stanford CA, US
Solomon Itani - Vallejo CA, US
Garry P. Nolan - Menlo Park CA, US
Sean C. Bendall - San Mateo CA, US
Tyler J. Burns - Stanford CA, US

Assignee:

The Board of Trustees of the Leland Stanford Junior University - Palo Alto CA

International Classification:

G06F 19/18

US Classification:

506 9, 506 12, 506 39, 506 18

Abstract:

A method and apparatus for simultaneously determining multiple different biological molecule types in a sample include labeling each different biological molecule type in a biological sample with a unique combination of a plurality of labels. Each different biological molecule type is selected from a population of M different biological molecules types. The plurality of labels is selected from a population of L different labels; and, M is greater than L. Measurements are obtained of relative abundances of the L different labels in the sample. Relative abundance of up to M different biological molecule types in the sample are determined based on the measurements and a method of compressed sensing.