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Analysis

1.WO/2020/136667INCORPORATING VARIANT INFORMATION INTO OMICS DATA
WO 02.07.2020
Int.Class G06T 11/60
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
112D image generation
60Editing figures and text; Combining figures or text
Appl.No PCT/IN2019/050851 Applicant SRINIVASAN, N.A. Mahalakshmi Inventor SRINIVASAN, N.A. Mahalakshmi
The invention is a code to incorporate variant information into omics data. The omics data so obtained could be used for determining the biomolecules, pathways, deciphering the taxonomy, probe designing, understanding evolutionary transitions at the omics level, data analysis, annotation, interpretation, visualization and any further studies. The omics data obtained after incorporation of variants is in compliance with the file formats of omics data and can be used as any other omics data in Bioinformatics software(s) or as standalone tool. The code could be hosted as an online tool. The code has been originally developed using R. The invention involves approaches to enable editing of omics data, using code which transforms the data type, manipulates, incorporate information in a sequential manner and converts them back to the same omics file format specification. The code could be written using various programming languages and compatible with all operating systems.
2.WO/2020/138479SYSTEM AND METHOD FOR PREDICTING TRAIT INFORMATION OF INDIVIDUALS
WO 02.07.2020
Int.Class G16B 40/20
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
20Supervised data analysis
Appl.No PCT/JP2019/051564 Applicant OSAKA UNIVERSITY Inventor KONNO Masamitsu
The present disclosure relates to predicting trait information from the genetic information of individuals, and generating a model therefor. Learning is performed using a plurality of types of genetic information from a plurality of individuals, and a model for predicting trait information is generated. For said learning, it is possible to create images of the genetic information, and provide the same to said learning. The images in the present disclosure can store both sequence information and expression information. Moreover, the layout of genetic factors in the images can be optimized. Said learning can be performed as split learning, and the data after said split learning can be consolidated.
3.WO/2020/139834METHODS FOR IDENTIFYING EPITOPES AND PARATOPES
WO 02.07.2020
Int.Class C07K 16/28
CCHEMISTRY; METALLURGY
07ORGANIC CHEMISTRY
KPEPTIDES
16Immunoglobulins, e.g. monoclonal or polyclonal antibodies
18against material from animals or humans
28against receptors, cell surface antigens or cell surface determinants
Appl.No PCT/US2019/068346 Applicant VISTERRA, INC. Inventor WOLLACOTT, Andrew, M.
Disclosed are methods of identifying an epitope on a target polypeptide and methods of identifying a paratope on an antibody.
4.WO/2020/136133TUMOR CLASSIFICATION BASED ON PREDICTED TUMOR MUTATIONAL BURDEN
WO 02.07.2020
Int.Class G16B 20/00
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
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Appl.No PCT/EP2019/086781 Applicant F. HOFFMANN-LA ROCHE AG Inventor LAM, Hugo Y. K.
The present disclosure provides systems and methods of classifying and/or identifying a cancer subtype. The present disclosure also provides methods of enhancing the prediction of a tumor mutational burden by using both synonymous and non-synonymous somatic mutations in the computation method. It is believed that by increasing the number of mutations in the computation of the tumor mutational burden, a comparatively more consistent tumor mutational burden may be derived, especially for targeted-panel sequencing. It is believed that the consistent computation of the tumor mutational burden from targeted panels allows for computationally quicker and less costly analysis of sequencing data as compared with a tumor mutational burden computed from whole exome sequencing data.
5.WO/2020/139782COMPOSITIONS AND METHODS USEFUL IN DETECTING AND TREATING MULTIPLE SCLEROSIS AND OTHER DEMYELINATING DISEASES
WO 02.07.2020
Int.Class A61P 31/04
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
PSPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
31Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
04Antibacterial agents
Appl.No PCT/US2019/068068 Applicant UNIVERSITY OF UTAH RESEARCH FOUNDATION Inventor KRIESEL, John
Disclosed herein, are methods of detecting one or more microbes in a subject having or suspected of having a demyelinating disease including multiple sclerosis. Also, disclosed herein are methods of treating subjects who are developing or experiencing a worsening demyelination based on microbial detection.
6.WO/2020/138720SYSTEM AND METHOD FOR PROVIDING AND ANALYZING SKIN-RELATED PERSONALIZED HEALTH FUNCTIONAL FOOD INFORMATION
WO 02.07.2020
Int.Class G16H 20/90
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
20ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
90relating to alternative medicines, e.g. homeopathy or oriental medicines
Appl.No PCT/KR2019/015808 Applicant LOGSYNK CO., LTD. Inventor LEE, Soo Il
The present invention relates to a system and a method for providing and analyzing personalized health functional food information and, more specifically, is to objectively and scientifically present health functional food suitable for an individual’s skin by performing a comparative analysis on skin genetic test result data capable of scientifically identifying individual’s skin conditions, and data obtained by phenomenal self-determination for the skin and through a skin measuring apparatus.
7.WO/2020/136192METHOD FOR MEASURING TELOMERE ASSOCIATED VARIABLES AND USES THEREOF FOR THE DIAGNOSIS AND/OR PROGNOSIS OF TELOMERIC-ASSOCIATED DISEASES
WO 02.07.2020
Int.Class C12Q 1/6841
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
6813Hybridisation assays
6841In situ hybridisation
Appl.No PCT/EP2019/087005 Applicant LIFE LENGTH, S.L. Inventor NAJARRO PARRA, María Pilar
The invention provided methods in vitro for diagnosis and/or prognosis of a clinical outcome in a subject with a telomere-associated disease comprising determining specific telomere associated variables (TAVs) of a test sample cell from the subject, wherein the value of the TAVs obtained for the test sample compared to a control sample is indicative of the telomerase-associated disease and/or the clinical outcome of the subject.
8.WO/2020/133588RAPID AND STABLE METHOD FOR EVALUATING INDIVIDUAL ANIMAL GENOME BREEDING VALUES
WO 02.07.2020
Int.Class G16B 20/00
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
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Appl.No PCT/CN2019/071514 Applicant HUAZHONG AGRICULTURAL UNIVERSITY Inventor ZHAO, Shuhong
Provided is a rapid and stable method for evaluating individual animal genome breeding values, relating to the field of animal breeding technology. The method uses HIBLUP to predict genomic breeding values using phenotype, genotype, and genealogical information, the final output comprising the estimated individual genetic value, the additive and dominant effect values of each individual, and the reverse parsing value of each genetic marker effect used in a genotyping chip. The method makes full use of genealogy, phenotype, and genotype information to predict the genetic value of each animal and the effect value of each SNP marker site, achieving the most advanced genomic breeding value prediction and variance component estimation algorithm to achieve genomic selection.
9.3673490METHOD FOR SECURING A MACHINE LEARNING BASED DECISION SYSTEM
EP 01.07.2020
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 18743840 Applicant THALES DIS FRANCE SA Inventor DAO FRÉDÉRIC
The present invention relates to a method for securing a system being configured to perform decision tasks carried out by a machine learning engine, equipped for operating with at least one machine learning model, the system comprising at least one training component for improving the at least one machine learning model, a device for carrying out decisions based on said machine learning model and a set of input data, and an interaction interface for exchanging said at least one machine learning model between training component and device, wherein the device comprises a model attestation checker, the method comprising the steps of: for the device: -acquiring input data, -ascertaining at least one machine learning model over the interaction interface, for the model attestation checker: -checking if said at least one machine learning model is trusted by a model attestation, -considering by the machine learning engine for said decision making only those machine learning models that are trusted, for the machine learning engine: -carrying out the decision task for acquired input data by using said at least one trusted machine learning model, -providing a result attestation for the decision output.
10.3675129PROCESSING READS OF SEQUENCED NUCLEIC ACID
EP 01.07.2020
Int.Class G16B 30/00
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
30ICT specially adapted for sequence analysis involving nucleotides or amino acids
Appl.No 18248105 Applicant IMEC VZW Inventor FOSTIER JAN
Example embodiments relate to a method for processing reads (502) of sequenced nucleic acid (500) comprising: i) determining (503) k-mers (504, 551); ii) determining (505) first counts (553) indicative for the respective number of occurrences of the respective k-mers (551), and second counts (554) indicative for the respective number of overlaps (560) between respective k-mers; iii) determining (507) a k-mer spectrum (530) from the first counts; iv) determining (507) first output counts indicative for the respective number of occurrences of the respective k-mers in the nucleic acid based on a position (531, 532, 533) of the k-mers in the k-mer spectrum (530) and based on a constraint (509, 510). The constraint comprising: i) a first output count of a respective k-mer equals the total number of overlaps of the respective k-mer with other k-mers in the nucleic acid; and/or ii) a first output count of a respective k-mer equals the total number of overlaps of other k-mers with the respective k-mer.