Dake Z Sun

7569348, Aug 4, 2009

Feb 3, 2006

11/347188

Charles L. Magness - Seattle WA, US
Dake Sun - Redmond WA, US

Illumigen Biosciences Inc. - Seattle WA

C12Q 1/68

435 6

This invention provides methods for identifying mutations and methylation patterns in diploid DNA sequence signal data. In particular, the invention provides methods for (1) obtaining two parental allele sequences from diploid DNA sequence signal data, (2) identifying the mutation and haplotype patterns in the two parental allele sequences, (3) assigning likelihood scores for the mutations thus identified, and (4) identifying patterns of methylation.

20040175702, Sep 9, 2004

Mar 7, 2003

10/383922

Charles Magness - Seattle WA, US
Dake Sun - Redmond WA, US

Illumigen Biosciences, Inc. - Seattle WA

C12Q001/68
G06F019/00
G01N033/48
G01N033/50

435/006000, 702/020000

This invention provides methods for identifying mutations and methylation patterns in diploid DNA sequence signal data. In particular, the invention provides methods for (1) obtaining two parental allele sequences from diploid DNA sequence signal data, (2) identifying the mutation and haplotype patterns in the two parental allele sequences, (3) assigning likelihood scores for the mutations thus identified, and (4) identifying patterns of methylation.

20160302671, Oct 20, 2016

Apr 16, 2015

14/688934

- Redmond WA, US
Zongyi Liu - Issaquah WA, US
Dake Sun - Redmond WA, US
Haithem Albadawi - Redmond WA, US

A61B 5/0205
A61B 5/01
A61B 5/00
A61B 5/024
A61B 5/08

Collection and analysis of physiological reading can predict when a person is likely to develop a fever before that person's body temperature increases. In an implementation a device such as a wearable band collects physiological information from its wearer. The physiological information may include heart rate or respiration rate. The person's physiological information classified by an algorithm derived through machine learning techniques. The algorithm may be trained by using data from other individuals who are both healthy and who are sick and/or trained from past reading of the person's own physiological readings. The algorithm may evaluate a value of the person's physiological information to generate probabilities that the person is healthy or that the person likely to become sick and/or develop a fever in the next few days.