Sharecare Ai, Inc.

Hammer of the Gods Inc.

Doc.ai, Inc.

H42, Inc.

Johns Hopkins University

Mdxhealth, Sa

Geert Trooskens

Walter Adolf De Brouwer

Stephen B Baylin

Srivatsa Akshay Sharma

Kartik Thakore

Wim Van Criekinge

Meelis Lootus

Wim Maria R Van Criekinge

Brett Robert Redinger

Devin Daniel Reich

Scott Michael Kirk

Satish Srinivasan Bhat

Josef Straub

Ricardo Lopez Arredondo

Leslie Cope

Frederick Franklin Kautz, Iv

Axel Sly

Young Jin Lee

Sam Leroux

Holly Ly

James Herman

Johan Vandersmissen

Adriaan Verhelle

Sandra Ann R Steyaert

Johan Irma H Vandersmissen

Leander Van Neste

Kornel Schuebel

Leander Vanneste

First and second sequenced outputs are accessed. The sequenced outputs contain variants occurring at different carriers and at different carrier positions. Hashes are generated over a selected pattern length of positions for those carrier positions that are shared between the sequenced outputs to produce window hashes for base patterns in first and second sequences. Each sequence is based on the shared carrier positions and the respective sequenced output. The window hashes are non-unique. Window hashes that occur less than a ceiling number times are selected. The selected window hashes are compared between the sequences on a starting position basis such that selected window hashes for base patterns having same start positions in the sequenced outputs are compared. Common window hashes are identified between the sequences based on the comparing. A similarity measure is determined between the sequences based on the common window hashes.

Hash-based efficient comparison of sequencing results

We propose a method of generating an epigenetic age prediction. The method includes providing a sample extraction test kit, and receiving a sample extracted by the sample extraction test kit. The method further includes extracting DNA from the sample and processing the DNA to receive processed DNA. The method further includes amplifying a plurality of loci in the processed DNA to receive amplified DNA and processing the amplified DNA to receive a plurality of methylation values for one or more CpG sites in the plurality of loci.

PCR-based epigenetic age prediction

The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Artificial intelligence-based generation of anthropomorphic signatures and use thereof

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

Bin-specific and hash-based efficient comparison of sequencing results

Ordinal position-specific and hash-based efficient comparison of sequencing results

The technology disclosed relates to systems and methods of cross-platform programming of tiny machine learning (ML) applications. The method includes providing a first declarative instruction that, when processed, interacts with a cross-platform capability of tiny ML hardware. The method includes providing a second declarative instruction that, when processed, invokes at least one procedure block. The method includes providing a third declarative instruction that, when processed, causes output from the tiny ML hardware. The method includes compiling the ML procedure block and the tiny ML model into bytecode. The bytecode interacts, via a virtual machine (VM) layer, with the capability to produce the output responsive to the first, second and third declarative instructions. The method includes executing the bytecode on the tiny ML hardware to process the declarative instructions without recompilation of the bytecode to adapt to alternative instances of the tiny ML hardware, each running its alternative VM layer.

Systems and methods for preparing cross-platform machine learning applications

The technology disclosed relates to systems and methods for deploying cross-platform applications to tiny ML hardware. The system provides tools to maintain definitions of a first, a second and a third declarative instruction. The system provides tools to maintain compiled bytecode for a procedure block and a tiny ML model that runs on the tiny ML hardware. The compiled bytecode further includes a manifest of one or more capabilities, one or more procedure blocks and at least one output supported by the tiny ML model. The system provides a loader that connects to an instance of the tiny ML hardware. The loader includes logic to verify that the instance of the ML hardware supports the one or more capabilities, the one or more procedure blocks and at least one output specified in the manifest. The loader loads the bytecode and verifies integrity of the load.

Systems and methods for preparing and deploying cross-platform applications

Systems and methods for generating and using anthropomorphic signatures to authenticate users

Systems and methods to verify identity of an authenticated user using a digital health passport

Growing community of inventors

Meise, Belgium

Geert Trooskens