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Analysis

1 / 609
1.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

2.WO/2023/141277SYSTEMS AND METHODS FOR SKIN BIOMOLECULAR PROFILE ASSESSMENT USING ARTIFICIAL INTELLIGENCE
WO - 27.07.2023
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No PCT/US2023/011249 Applicant VANDERBILT UNIVERSITY Inventor MARASCO, Christina, C.
Skin biomolecular profile assessment methods and systems that can analyze the molecular composition of the skin using molecular-level, user-specific data to assess an individual's skin state and/or disease state are described herein. An example method includes receiving skin data associated with a subject, where the skin data includes a biomolecular profile. The method also includes inputting the skin data into a trained artificial intelligence (AI) model and receiving, from the trained AI model, a skin care prediction.
3.20220139498APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
US - 05.05.2022
Int.Class G16B 40/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
 Appl.No 17088734 Applicant BASF CORPORATION Inventor Erin Marie Davis

Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements (e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators (e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

4.20240071569APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
US - 29.02.2024
Int.Class G16B 40/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
 Appl.No 18034417 Applicant BASF CORPORATION Inventor Erin Marie Davis

Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements {e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators {e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

5.WO/2022/098588APPARATUSES, SYSTEMS, AND METHODS FOR EXTRACTING MEANING FROM DNA SEQUENCE DATA USING NATURAL LANGUAGE PROCESSING (NLP)
WO - 12.05.2022
Int.Class C12Q 1/68
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
68involving nucleic acids
 Appl.No PCT/US2021/057491 Applicant BASF CORPORATION Inventor DAVIS, Erin, Marie
Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements {e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators {e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.
6.WO/2018/089574PREDICTIVE ANALYTIC METHODS AND SYSTEMS
WO - 17.05.2018
Int.Class G06F 17/10
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
10Complex mathematical operations
 Appl.No PCT/US2017/060766 Applicant MINITAB, INC. Inventor STEINBERG, Dan
Apparatus and associated methods relate to developing a predictive analytic model based on data records partitioned as a function of at least one relationship between parts and folds, assigning more than one part to test each fold, and assigning at least one part to test more than one fold; and evaluating the predictive analytic model based on more than one prediction determined for each observation in each test data record as a function of a predictive analytic model not trained on the test data record. In an illustrative example, the relationship between parts and folds may exclude some of the training data in common, with the same degree of overlap in the data between each pair of folds. Various examples may advantageously produce models built on each pair of folds having nearly equal pairwise-correlation of their predictions with models built on any other pair of folds.
7.20180137415PREDICTIVE ANALYTIC METHODS AND SYSTEMS
US - 17.05.2018
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 15805548 Applicant Minitab, Inc. Inventor Dan Steinberg

Apparatus and associated methods relate to developing a predictive analytic model based on data records partitioned as a function of at least one relationship between parts and folds, assigning more than one part to test each fold, and assigning at least one part to test more than one fold; and evaluating the predictive analytic model based on more than one prediction determined for each observation in each test data record as a function of a predictive analytic model not trained on the test data record. In an illustrative example, the relationship between parts and folds may exclude some of the training data in common, with the same degree of overlap in the data between each pair of folds. Various examples may advantageously produce models built on each pair of folds having nearly equal pairwise-correlation of their predictions with models built on any other pair of folds.

8.WO/2008/033100METHOD OF PREDICTING PROTEIN ALLERGENICITY USING A SUPPORT VECTOR MACHINE
WO - 20.03.2008
Int.Class G06N 7/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
7Computing arrangements based on specific mathematical models
08using chaos models or non-linear system models
 Appl.No PCT/SG2007/000293 Applicant AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH Inventor TONG, Joo Chuan
A method for predicting the allergenicity of a protein using a non-linear prediction model based on an algorithm such as a probabilistic function, artificial neural network, hidden Markov model, multiple regression or Bayesian network, and in particular a support vector machine. The method comprises a learning phase where a data set of allergenic proteins is used to train the prediction model, and a prediction phase where the allergenicity of a protein is determined using the trained prediction model. In one embodiment the support vector machine is trained by creating a hyperplane separating and classifying the training examples according to a kernel function, where a third degree polynomial kerne function was optimal.
9.20160371431METHODS OF PREDICTING PATHOGENICITY OF GENETIC SEQUENCE VARIANTS
US - 22.12.2016
Int.Class G06F 19/24
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
24for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
 Appl.No 15189957 Applicant Counsyl, Inc. Inventor Imran Saeedul HAQUE

Recent developments in cost-effective DNA sequencing allows for individualized genomic screening of a subject for genetic sequence variants. Training a pathogenicity prediction model using semi-supervised training methods produces a better model for predicting the pathogenicity of a test genetic sequence variant. Provided herein are methods for predicting the pathogenicity of a test genetic sequence variant by utilizing a training data set comprising labeled benign genetic sequence variants unlabeled genetic sequence variants, the unlabeled genetic sequence variants comprising a mixture of benign genetic sequence variants and pathogenic genetic sequence variants. The genetic sequences are annotated with one or more features and a machine learning model is trained in a semi-supervised process based on the training data. The test genetic sequence is then annotated using the one or more features and the probability that the test genetic sequence variant is pathogenic is predicted based on the trained machine learning model.

10.20180173847ESTABLISHING A MACHINE LEARNING MODEL FOR CANCER ANTICIPATION AND A METHOD OF DETECTING CANCER BY USING MULTIPLE TUMOR MARKERS IN THE MACHINE LEARNING MODEL FOR CANCER ANTICIPATION
US - 21.06.2018
Int.Class G06F 19/24
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
24for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
 Appl.No 15382212 Applicant JANG-JIH LU Inventor JANG-JIH LU

A method of establishing a machine learning model for cancer anticipation includes collecting test results of a plurality of tumor markers of a plurality of eligible individuals and corresponding conditions of cancer; performing a variable selection process on the collected data to select a plurality of robust variables; and using the selected variables, numerals, and conditions of cancer by cooperating with a machine learning method to establish a cancer anticipation model. A method of detecting cancer by using a plurality of tumor markers in a machine learning model for cancer anticipation is also provided.

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