[go: up one dir, main page]

WO2025166407A1 - Analyse assistée de données d'échantillon biologique - Google Patents

Analyse assistée de données d'échantillon biologique

Info

Publication number
WO2025166407A1
WO2025166407A1 PCT/AU2025/050079 AU2025050079W WO2025166407A1 WO 2025166407 A1 WO2025166407 A1 WO 2025166407A1 AU 2025050079 W AU2025050079 W AU 2025050079W WO 2025166407 A1 WO2025166407 A1 WO 2025166407A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
datapoints
natural language
user
prompt
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/AU2025/050079
Other languages
English (en)
Inventor
Christopher COWLED
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Commonwealth Scientific and Industrial Research Organization CSIRO
Original Assignee
Commonwealth Scientific and Industrial Research Organization CSIRO
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2024900255A external-priority patent/AU2024900255A0/en
Application filed by Commonwealth Scientific and Industrial Research Organization CSIRO filed Critical Commonwealth Scientific and Industrial Research Organization CSIRO
Publication of WO2025166407A1 publication Critical patent/WO2025166407A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/40—Processing or translation of natural language
    • G06F40/55—Rule-based translation
    • G06F40/56—Natural language generation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • G06F3/04842—Selection of displayed objects or displayed text elements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • G06F3/04845—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0475—Generative networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • This disclosure relates to user-assisted analysis of biological sample data.
  • nucleic acid sequencing which is also a measurement of a biological sample
  • nucleic acid sequencing data provides sequencing information for many genes or even all genes, which is in contrast to gene panel tests, which only test for a small number of genes.
  • current computer technology is not able to interpret the sequencing data meaningfully and provide a descriptive analysis. Especially in cases where multiple genes interact - in which case it is an advantage to have the data across all genes - it is difficult for current computer technology to generate meaningful analyses.
  • This disclosure provides methods for data analysis which are guided by user input. These methods are based on user input that identifies a subset of datapoints and generates a prompt to a machine learning (ML) model, trained to generate natural language text, including the measurement data of the subset of the multiple datapoints as selected by the user.
  • ML machine learning
  • the ML model is trained on a large corpus of texts in applicable technical fields and can therefore produce a meaningful analysis that incorporates available publications and other input. This way, the disclosed methods leverage the capabilities of trained ML models to provide an analysis of data, as selected by the user, in the form of natural language output text.
  • the measurement data comprises quantitative data or qualitative data or both.
  • the datapoints represent samples from different individuals.
  • the prompt further comprises input data related to the one or more biological samples that is independent from the measurement data. [0011] In some embodiments, the prompt further comprises input data provided by a user through the user interface.
  • the method further comprises creating a further natural language prompt for the machine learning model, the further natural language prompt comprises information about an experiment that generates the measurements and text that causes the machine learning model to generate a further natural language output text characterising the experiment.
  • the method further comprises creating a graphical user interface, the graphical user interface comprising multiple graphical data elements, each of the multiple graphical data elements representing one of the datapoints of the measurement data, the multiple graphical data elements being arranged in two dimensions on the graphical user interface.
  • the user input is indicative of an area on the graphical user interface selected by the user and the method further comprises selecting the subset of the datapoints represented by the multiple graphical data elements that are within the area selected by the user.
  • each data point represents one of multiple genes; the measurement data comprises sequencing data; and the prompt comprises a name for each of the multiple genes.
  • the sequencing data comprises expression data of the multiple genes
  • the graphical data elements are visually formatted to represent the expression data
  • the natural language prompt text comprises number values indicating the expression data for the subset of genes.
  • each of the graphical data elements represents a step of a biological pathway.
  • the method further comprises identifying one or more biological pathways that are related to a change in expressions levels of the multiple genes as indicated by the sequencing data; and arranging the graphical data elements to represent the one or more biological pathways.
  • each of the graphical data elements represents measurement data, including gene expression data, from a respective cell from one sample from an individual.
  • the method further comprises performing a method of data analysis on the measurement data to arrange the graphical data elements in the two dimensions.
  • the method further comprises calculating a position of each of the graphical data elements based on an output of the method of data analysis.
  • the graphical data elements are points of a scatter plot that are arranged in two dimensions.
  • the datapoints in the subset are selected by the user by drawing a free-form shape that encompasses the subset.
  • the method further comprises calculating a score for each datapoint of the sub-set of datapoints.
  • the method further comprises filtering the sub-set of datapoints based on the score to reduce a number of datapoints that is provided as the prompt to the machine learning model.
  • the method further comprises ordering the measurement data in the prompt based on the score of the sub-set of multiple datapoints.
  • the measurement data is obtained from an experiment of sampling a population of individuals with a sequencer and the prompt comprises text obtained from a gene database that is independent from the experiment.
  • a computer system for generating a natural language output text characterising one or more biological samples comprises one or more processors configured to perform the steps of: receiving user input from a user indicative of a selected subset of datapoints relating to the one or more biological samples, the subset of datapoints being selected on a user interface that graphically presents the datapoints to the user; creating a natural language prompt for a machine learning model trained to generate natural language text, the natural language prompt comprising measurement data related to the selected subset of the datapoints; and evaluating the machine learning model on the natural language prompt to generate the natural language output text characterising the one or more biological samples.
  • Figure 1 illustrates a biological pathway with user selection of genes.
  • Figure 2 illustrates a computer system with processing modules.
  • Figure 3 illustrates a method for characterising sequencing data.
  • Figure 4 illustrates a computer system for analysing sequencing data.
  • Figure 5 illustrates a volcano plot with a user-selected subset of datapoints.
  • Figure 6 illustrates graphical data elements arranged according to the result of a principle component analysis and a user-selected subset of datapoints.
  • the present disclosure provides methods where a user selects a subset of datapoints, such as genes, samples or experimentally-determined quantities, measurements or observations, and the computer system generates a natural language prompt including the subset and a representation of the measurement data, such as gene expression data or titre data, in a form that can be interpreted by the trained language model, which is a ML model trained to generate text.
  • the trained language model which is a ML model trained to generate text.
  • the raw data is partially analysed to get it into a form that the trained language model can understand.
  • the trained language model can ingest the natural language prompt and draw on the training on medical literature to generate an output that, in effect, analyses the measurement data.
  • Figure 1 illustrates a biological pathway 100 as may be displayed on a user interface to a user.
  • Each rectangle in the pathway 100 denotes one datapoint that represents one protein that is linked to a gene in the human genome, noting that this disclosure equally applies to any other species.
  • the rectangles are filled with diagonal hatching to indicate an upregulated gene and a square hatching to indicate a downregulated gene.
  • the user can now draw a freeform area 101 to select a subset of genes.
  • the computer system constructs a natural language prompt to be used as an input prompt to a trained ML model. That model then provides as its output an analysis of the sequencing data in natural language. As a result, the output is readily understandable by the user, which may be a researcher, clinician or other user.
  • the output can incorporate a vast amount of medical literature on which the model has been trained. It is noted that the output may be provided to the user as a stream that appears as the ML model generates the output or as a complete output once the ML generation is complete. It the example of the stream output, it may be possible for the user to stop the generation during generation and before the output generation has finished. Further, the user interface may comprise an input text box where a user can enter follow-up questions or other text prompts that are provided to the ML model in the context (the same chat) of the selected datapoints.
  • FIG. 2 illustrates a computer system 200 comprising a bioinformatics pipeline module 201, a user interface module 202, a prompt generation module 203 and a language model module 204.
  • Each of the modules of computer system 200 may be implemented as software modules, such as classes, functions, libraries, etc. as well as separate services or servers, such as remote or distributed services or servers providing application programming interfaces (API).
  • the bioinformatics pipeline module 201 receives the measurement data from laboratory equipment, such as the sequencer that may run an RNA sequencing process and, for example, a sequencing by synthesis process for RNA or a mass spectrometry process for sequencing proteins.
  • the bioinformatics pipeline 201 may perform the mapping of short reads to a reference genome or the assembly of amino acid fragments into proteins. Further, the bioinformatics pipeline 201 may determine variants or may calculate quantitative data, such as expression information, from the processed sequencing data.
  • the user interface module 202 receives the processed measurement data from the bioinformatics pipeline module 201 and creates a user interface that enables the user to select a subset of datapoints, such as genes. Such a selection should be based on the measurement data and therefore, it is advantageous to present the measurement data to the user in a suitable way that enables the user to select the subset of datapoints directly from the representation of the measurement data.
  • the sequencing data is presented in the form of a biological pathway.
  • the user may be able select one or more pathways out of all known pathways.
  • the computer system 200 selects one or more pathways that are predominantly affected by the observed changes in the sequencing data compared to a baseline. For example, with RNA expression levels, the computer system 200 can select one or more pathways that are significantly affected by up- or downregulated genes.
  • the user interface module 202 receives the selection from the user.
  • This selection may be an array of selected datapoints representing genes or may be an array of coordinates that define an area in the user interface.
  • a separate software program generate the graphical visualisation of the pathway as an image object (e.g., jpg or png) and then the user interface module 202 creates an overlay of invisible elements with calculated coordinates so that they overlay the corresponding genes in the pathway image object.
  • the user interface module 202 In response to receiving coordinate data indicating a selection area, the user interface module 202 then calculates which of the invisible elements are within the area and creates the subset of genes this way.
  • FIG 9 illustrates spatial RNA-Seq data. Similar to single cell, but now each dot is a "spot" and may consist of a few cells or even parts of cells (depending on the size and location of the spot). The analysis is similar as for single cell (i.e. the method starts with UMAP and dot plot), except now every spot also has coordinates that map it to a location on a microscope image.
  • the chart on the top is for a single gene and uses a quantitative colour palette (shown in black and white in Figure 9 for reproducibility). The one on the right uses a qualitative colour palette (again shown in black and white) to show the different clusters (interpreted as different cell types).
  • computer system 200 may calculate a score for each datapoint of the sub-set of the datapoints and filter the sub-set of the multiple datapoints based on the score to reduce a number of datapoints that is provided as the prompt to the generative language model.
  • the output generated by the trained generative language model depends significantly on the order of the datapoints provided in the input prompt. Therefore, computer system 200 orders the measurement data in the prompt based on the score of the sub-set of datapoints.
  • the experimental data may be provided in a user interface that comprises an input box for the description mentioned above as well as input fields for data variables. Such as variables for treatment where the user can enter different treatment labels or knockout gene names as text values.
  • the user interface may also comprise input fields for title, data source, data type (e.g., mRNA) and species.
  • the user interface may also comprise input fields for entering a number of replicates for different groups.
  • the user interface may also comprise selection lists to form pairs of groups for differential expression analysis as well as selection of (potentially ranked) biological pathways).
  • Training involves updating weights or parameters (as referred to as hyperparameters) of the machine learning model, which define the machine learning model, to minimise a loss value, thereby creating a trained machine learning model (in other words, a machine learning model trained to generate an output). This may involve a gradient descent and backpropagation method.
  • the machine learning model may be trained on a broad range of different data such that it can be applied across a wide range of use cases. Such a machine learning model may be referred to as a “foundational model”. Some foundational models that are applicable to the disclosed method include those that are publicly available and/or trained on publicly available data. In other embodiments, the machine learning model may be trained on a specific set of training data, in order to focus the generated outputs of the machine learning model to a specific task or area of interest. In yet other examples, the foundational model is further trained on the specific set of training data to improve the model in the area of interest.
  • the machine learning model is a multimodal machine learning model, in which multiple inputs of different modalities (e.g., text, image data and audio data) are used to provide one or more generated outputs.
  • An example of a multimodal machine learning model is an object detection model, which detects the location of a specific object (specified by input text, for example) in an image. This example model may generate output text that describes the location of the specified object in the image.
  • the multimodal machine learning model can be evaluated on multiple input of different modalities, the multimodal machine learning model can also be evaluated on a single input and still generate an output based on the single input.
  • the machine learning model may be trained to generate output text based on input text and hence, may be a chat-based machine learning model.
  • the machine learning model may also be referred to as a trained generative language model.
  • Both the input and output text may be in the form of “natural language” (i.e., any language that occurs naturally in a human community by a process of use, such as spoken English, for example).
  • Such machine learning models may be referred to as “chatbots”.
  • a chatbot (which may also be referred to as a chatterbot) is designed to mimic human conversation (using natural language) through text or voice interactions. More particular, the chatbot response to input natural language using output natural language. Examples of such chatbots currently include ChatGPT (using GPT-3 or GPT-4), Microsoft’s Bing Chat/Copilot (which may use OpenAI's GPT-4) and Google’s Bard.
  • LLMs like GPT i.e., GPT-3, GPT-3.5, GPT-4, GPT-4o, GPT-4o-mini, DeepSeek
  • LLaMA LLaMA
  • NLP natural language processing
  • LLMs excel in NLP tasks through the technique of prompt engineering. In prompt engineering, tasks are conveyed as text descriptions, and these descriptions are presented to the model for interpretation and the generation of corresponding responses.
  • the LLM may be an artificial neural network, such as transformer model (e.g., a generative pre-trained transformer) which utilises encoder and decoder networks.
  • transformer model e.g., a generative pre-trained transformer
  • These LLMs are (pre-) trained using self- supervised learning and semi-supervised learning. In essence, LLMs are trained to predict what word comes next in a sequence of words which can be based on the semantic closeness of the words. LLMs form these predictions by ‘tokenising’ the words in the output text (i.e., converting the words into a vector of numbers). These tokens can also incorporate other information such as the position of the word in the input text and the information about the adjust words.
  • the machine learning model may also comprise an attention mechanism that applies weights to the tokens and may be characterized by its self-attention layers. These layers enable the model to assess the significance of words in an input relative to each other, thereby providing a more nuanced comprehension of the text.
  • the attention mechanism may also include a scaled dot-product between different matrices generated by the model to calculate the weights. In simple terms, the attention mechanism allows a model to focus on different parts of the input when generating each element of the output. This dynamic focusing capability results in a more contextually aware model, producing better results in tasks like translation, summarization, or text generation. Other ways of achieving attention within the model would be equally possible.
  • the machine learning model can process a large number of input values, such as an input text paragraph, at one time rather than sequentially in order to consider the context of each word. Nevertheless, the overall number of parameters in the machine learning model is relatively large, which is the reason those models are referred to as large models, such as large language models or large action models. In some examples, a model is large if it has more than 100 million parameters or more than 1 billion parameters or more than 1 trillion parameters.
  • the machine learning model referred to in this disclosure may be multiple machine learning models that have a ‘global’ input and a ‘global’ output.
  • the multiple machine learning models may be “daisy chained” together, such that the output of one machine learning model becomes the input for the next machine learning model within the chain.
  • Some of the multiple machine learning models may operate in parallel, rather than in a series or chain.
  • Each of the multiple machine learning models may have its own memory and/or have access to a common memory that is shared become some or all of the multiple machine learning models.
  • the multiple machine learning models may resemble parallel processing or parallel computer, such a computer architecture with multiple CPU cores which can be operated in parallel.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biotechnology (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • Bioethics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)

Abstract

La présente divulgation concerne l'analyse assistée de données d'échantillon biologique. Un procédé génère un texte de sortie en langage naturel qui caractérise des échantillons biologiques. Une entrée d'utilisateur est reçue en provenance d'un utilisateur indiquant un sous-ensemble sélectionné de points de données concernant le ou les échantillons biologiques, le sous-ensemble de points de données étant sélectionné sur une interface utilisateur qui présente graphiquement les points de données à l'utilisateur. Le procédé crée une invite en langage naturel pour un modèle d'apprentissage automatique entraîné pour générer un texte en langage naturel. L'invite en langage naturel comprend des données de mesure relatives au sous-ensemble sélectionné des points de données. Le procédé évalue le modèle d'apprentissage automatique sur l'invite en langage naturel pour générer le texte de sortie en langage naturel caractérisant le ou les échantillons biologiques. Le modèle génère une sortie de texte qui analyse les données de mesure, qui est facilement compréhensible par l'utilisateur et ne nécessite pas d'analyse numérique supplémentaire ni de calculs.
PCT/AU2025/050079 2024-02-05 2025-02-04 Analyse assistée de données d'échantillon biologique Pending WO2025166407A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2024900255A AU2024900255A0 (en) 2024-02-05 Assisted analysis of biological sample data
AU2024900255 2024-02-05

Publications (1)

Publication Number Publication Date
WO2025166407A1 true WO2025166407A1 (fr) 2025-08-14

Family

ID=96698725

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2025/050079 Pending WO2025166407A1 (fr) 2024-02-05 2025-02-04 Analyse assistée de données d'échantillon biologique

Country Status (1)

Country Link
WO (1) WO2025166407A1 (fr)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060142949A1 (en) * 2002-04-26 2006-06-29 Affymetrix, Inc. System, method, and computer program product for dynamic display, and analysis of biological sequence data
US20150324527A1 (en) * 2013-03-15 2015-11-12 Northrop Grumman Systems Corporation Learning health systems and methods
WO2019033098A2 (fr) * 2017-08-11 2019-02-14 Elucid Bioimaging Inc. Rapport d'imagerie médicale quantitative
US20210366106A1 (en) * 2018-11-21 2021-11-25 Enlitic, Inc. System with confidence-based retroactive discrepancy flagging and methods for use therewith
US20220011230A1 (en) * 2020-07-08 2022-01-13 Luminex Corporation User interface for a fluorescence assay
US20220051771A1 (en) * 2017-05-25 2022-02-17 Enlitic, Inc. Report generating system and methods for use therewith
US20230325725A1 (en) * 2022-04-12 2023-10-12 Google Llc Parameter Efficient Prompt Tuning for Efficient Models at Scale

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060142949A1 (en) * 2002-04-26 2006-06-29 Affymetrix, Inc. System, method, and computer program product for dynamic display, and analysis of biological sequence data
US20150324527A1 (en) * 2013-03-15 2015-11-12 Northrop Grumman Systems Corporation Learning health systems and methods
US20220051771A1 (en) * 2017-05-25 2022-02-17 Enlitic, Inc. Report generating system and methods for use therewith
WO2019033098A2 (fr) * 2017-08-11 2019-02-14 Elucid Bioimaging Inc. Rapport d'imagerie médicale quantitative
US20210366106A1 (en) * 2018-11-21 2021-11-25 Enlitic, Inc. System with confidence-based retroactive discrepancy flagging and methods for use therewith
US20220011230A1 (en) * 2020-07-08 2022-01-13 Luminex Corporation User interface for a fluorescence assay
US20230325725A1 (en) * 2022-04-12 2023-10-12 Google Llc Parameter Efficient Prompt Tuning for Efficient Models at Scale

Similar Documents

Publication Publication Date Title
Amirhosseini et al. Machine learning approach to personality type prediction based on the myers–briggs type indicator®
List et al. Sequence comparison in computational historical linguistics
Hu et al. Evaluation of large language models for discovery of gene set function
US8594941B2 (en) System, method and apparatus for causal implication analysis in biological networks
CN110188272A (zh) 一种基于用户背景的社区问答网站标签推荐方法
Liang et al. Big Data science and its applications in health and medical research: Challenges and opportunities
WO2023035745A1 (fr) Procédé et appareil de criblage de récepteur olfactif, procédé et appareil d'entraînement de modèle, et procédé et appareil d'identification de produit de vin
US20110119221A1 (en) Method, system and software arrangement for reconstructing formal descriptive models of processes from functional/modal data using suitable ontology
Gillies et al. Theme and topic: How qualitative research and topic modeling can be brought together
Overall et al. Rtrack: a software package for reproducible automated water maze analysis
Geng et al. The impact of large language models in academia: from writing to speaking
Heintz-Buschart et al. A beginner’s guide to integrating multi-omics data from microbial communities
Wang et al. Feature-Enhanced Multi-Task Learning for Speech Emotion Recognition Using Decision Trees and LSTM
Talevi et al. Clustering of small molecules: new perspectives and their impact on natural product lead discovery
Akyol New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks
Baltsavia et al. scRNA-explorer: an end-user online tool for single cell RNA-seq data analysis featuring gene correlation and data filtering
Blekanov et al. Mapping opinion cumulation: topic modeling-based dynamic summarization of user discussions on social networks
Reis et al. A Conceptual Architecture for AI-based Big Data Analysis and Visualization Supporting Metagenomics Research.
WO2025166407A1 (fr) Analyse assistée de données d'échantillon biologique
Polamuri et al. Introduction to deep learning
Saihi et al. Automated and reproducible cell identification in mass cytometry using neural networks
Consens et al. Interpreting Attention Mechanisms in Genomic Transformer Models: A Framework for Biological Insights
Ofer et al. Whats next? Forecasting scientific research trends
Braun et al. The case for retaining natural language descriptions of phenotypes in plant databases and a web application as proof of concept
Casaletto et al. Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 25750951

Country of ref document: EP

Kind code of ref document: A1