Chifeng University Neuroimmune Gastrointestinal Disease Robot Dataset and Automated Research Pipeline Processing Robot Program Acquisition Method Dataset

Dataset 1 is an Excel file containing over 10000 samples, primarily used for studying health risk analysis and intervention strategies for susceptible populations. This dataset is based on epidemiology and covers multidimensional variables such as demographic information, disease history, lifestyle, psychological status, and environmental factors through comprehensive individual health survey data, providing solid data support for labeling susceptible populations.

In terms of health intervention, the dataset also includes individual health feedback and intervention result records, providing a basis for analyzing the role of communication models in the intervention process. Through effective communication models, the way doctors and patients communicate can be optimized, enhancing patients' acceptance and execution of health advice.

In addition, the dataset contains data on the application of music therapy, particularly clinical music therapy records based on the Mandala recommendation algorithm. The data of personalized music therapy includes patients' reactions to different music styles, emotional improvement records, and changes in physiological indicators. This provides a quantitative basis for the role of music therapy in relieving psychological stress and regulating emotions.

The dataset also includes motion model application data based on metaverse technology, which records in detail the motion behavior and health effects of patients in different virtual scenes. This part of the data supports the simulation and analysis of health behaviors, exploring how multidimensional scenarios can stimulate patient participation and their impact on the effectiveness of health interventions.

Overall, the diversity and refinement of this dataset provide important evidence for studying individual health risk markers, optimizing health intervention strategies, and integrating emerging technologies such as music therapy and metaverse. The scale and breadth of the dataset make it of significant research value in the field of chronic disease prevention and rehabilitation, providing data support for precision medicine and further promoting cross disciplinary integration and innovation in medicine, art, and technology.

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 Molecular biology robot is an efficient research aid tool that combines bioinformatics, molecular experimental technology, and artificial intelligence. It aims to help researchers simplify complex experimental processes and improve research efficiency. This system elevates traditional molecular biology research to a new technological level through automated operations and intelligent analysis, suitable for research institutions, university laboratories, and biotechnology companies.

Core functions and technical features

Efficient experimental design and optimization

The molecular biology robot is equipped with an algorithm module trained on massive data, which can automatically recommend the best experimental design based on user input experimental objectives, including primer design, PCR condition optimization, experimental group control settings, etc. The system can intelligently analyze experimental variables, provide optimization suggestions, and help researchers reduce trial and error time.

Automated experimental operation

The system can dock with common laboratory equipment to automatically complete nucleic acid extraction, PCR amplification, gel electrophoresis, flow cytometry and other operations. Through high-precision robotic arms and programmable control modules, the experimental process is standardized and highly reproducible, significantly reducing human error.

Data Processing and Intelligent Analysis

The embedded bioinformatics module supports the analysis and processing of various omics data such as genomics, proteomics, metabolomics, etc. Based on deep learning models, the system can quickly parse experimental data, provide multi-level results such as differential expression analysis, network construction, and functional annotation, and generate high-quality charts and reports.

Intelligent literature support

The system is equipped with real-time literature retrieval function, which can recommend relevant literature based on experimental content and extract key information from it, providing theoretical support for research. Users can upload custom databases to further enhance the targeting of the system knowledge base.

User friendly interface and flexibility

Molecular biology robots provide an intuitive user interface that supports multilingual operations, making it easy for users to get started without a programming background. Modular design allows users to adjust functions according to their needs and adapt to different experimental requirements.

application prospect 

Molecular biology robots are suitable for various research fields, such as gene editing, single-cell analysis, cancer biology, drug screening, etc. By integrating high-throughput experiments with intelligent analysis, it can not only improve laboratory efficiency but also accelerate the realization of new discoveries, providing support for the transformation of scientific research achievements.

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Computational Genomics Robots.rar

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Introduction to Computational Genomics Robots

 

Computational genomics robot is a comprehensive platform based on cutting-edge artificial intelligence technology and genomic data analysis tools, aimed at providing a one-stop solution for genomics researchers. This robot simplifies the complex process of genome data processing, analysis, and visualization by integrating multiple computational models and bioinformatics methods, making it particularly suitable for fields such as precision medicine, evolutionary biology, agricultural biology, and environmental genomics.

 

Core functions and technological advantages of the system

1. Efficient preprocessing of genomic data

The computational genomics robot integrates fully automated processing capabilities from raw sequence data to standardized input files.

 

Data cleaning and quality control

The built-in quality control module of the system can detect low-quality fragments, adapter sequences, and contaminants in the raw sequencing data, and provide detailed quality control reports, laying a reliable foundation for subsequent analysis.

Diversified data format support

Whether it is sequencing data generated by Illumina, PacBio, or Oxford Nanopore, the system can quickly recognize and convert formats, supporting multiple commonly used formats such as FASTA, FASTQ, BAM, VCF, etc.

2. Accurate gene annotation and functional prediction

Genome assembly and annotation

For the whole genome, transcriptome, and metabolome, robots can achieve de novo assembly and overlapping splicing, and perform high-precision gene annotation by cross referencing existing databases such as NCBI RefSeq, Ensembl, etc.

Function and pathway prediction

By modeling protein structures and comparing functional domains, the system can predict gene function and generate metabolic network and signaling pathway models, helping researchers quickly identify key biological processes.

3. Advanced Biostatistical Analysis

Population genetics analysis

The system can calculate indicators such as gene frequency, linkage disequilibrium, and population differentiation coefficient, providing in-depth statistical analysis for population evolution and genetic diversity research.

Gene association analysis (GWAS)

Integrating multiple algorithms (such as PLINK, BOLT-LMM), supporting multivariate analysis and data visualization, facilitates researchers to explore the genetic basis behind complex traits.

4. Machine Learning and Deep Learning Modules

Intelligent prediction and classification

The system uses machine learning technology to classify and predict gene expression data and variation data, and is widely used in fields such as cancer typing and disease risk assessment.

Deep learning modeling

Based on neural network-based deep learning models, robots can recognize complex gene regulatory networks, predict nonlinear relationships between genes and phenotypes, and provide strong support for systems biology research.

5. Visualization and report generation

Dynamic interactive visualization

Users can easily display research results through various forms of dynamic interactive charts such as genome maps, evolutionary trees, pathway maps, and heatmaps generated by the system.

Automatically generate research reports

The system can automatically generate structured reports based on user analysis tasks, including background introduction, method description, result summary, and chart presentation, meeting the needs of efficient scientific research and publication.

Technical architecture and operational characteristics

Multi module collaborative work

The robot adopts a modular design, covering multiple functional modules such as data preprocessing, analysis, visualization, etc. Users can flexibly combine and use them according to their needs.

Cloud computing support

The system supports local operation and cloud deployment, and can utilize high-performance computing clusters to process large-scale genomic data, significantly reducing analysis time.

User friendly interface

Equipped with an intuitive user interface and detailed documentation guidance, it helps users easily complete complex analysis tasks without the need for programming background.

Application scenarios and research value

1. Precision medicine

By analyzing individual genomic data, robots can quickly identify pathogenic mutation sites and predict drug responses, providing data support for personalized medicine.

 

2. Agricultural improvement

The system can finely analyze the genomes of crops and livestock, locate key genes related to traits such as yield and disease resistance, and provide guidance for molecular breeding.

 

3. Environmental genomics

In environmental microbiology research, robots can decipher complex microbial community structures and track the relationship between specific gene functions and ecological roles.

 

4. Evolutionary Biology

The molecular evolution analysis function provided by the system can help researchers explore the mechanisms of adaptive evolution of species and changes in gene function.

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Introduction to Medical Research Robot Data Processing Pipeline

 

The medical research robot data processing pipeline is a highly integrated and intelligent research tool designed specifically for medical data analysis and research work. This tool adopts advanced algorithm architecture and data processing technology, which can efficiently process and analyze medical big data, providing complete automation solutions for clinical research, precision medicine, epidemiology and other fields. The collaborative nature of its multiple modules greatly simplifies complex data analysis processes, greatly improving research efficiency and data accuracy.

 

Core functions and technical features

1. Data integration and cleaning

Multi source data import

Supports loading data from multiple sources, including Excel files, CSV files, databases (such as MySQL, MongoDB), text files, and cloud storage, making it easy to integrate multimodal data.

Intelligent data cleaning

Automatically detect missing values, outliers, and formatting errors in data, and provide cleaning and repair solutions to ensure the reliability of analysis results. It is based on a machine learning cleaning module that can generate intelligent prediction fillers for abnormal data points.

2. Multidimensional data analysis

Statistical analysis module

Provide a complete set of tools from descriptive statistics to advanced models such as Cox regression, logistic regression, and multivariate analysis, suitable for clinical and epidemiological research. Support the generation of standardized statistical reports to meet publishing requirements.

Time series analysis

Provide efficient time series modeling and prediction capabilities for dynamic medical data, such as patient course changes, drug response curves, etc.

Molecular data analysis

Built in genomics and proteomics data analysis modules can analyze variations, gene expression, and protein function, providing support for molecular mechanism research.

3. Artificial Intelligence and Deep Learning

Intelligent classification and prediction

By pre training deep learning models, the system can perform risk assessment, disease classification, and personalized treatment plan recommendations on patient data.

Image data analysis

The built-in medical image analysis module supports automatic segmentation, annotation, and lesion recognition of CT, MRI, ultrasound, and other images.

Multimodal learning

Combine clinical data, imaging data, and genomic data for analysis, explore potential correlations across modalities, and provide multidimensional support for precision medicine.

4. Automated workflow and report generation

One click assembly line

From data import, preprocessing, analysis to report generation, the system can achieve fully automated operations, significantly reducing manual intervention time. Users only need to set analysis goals, and the system can complete all processes.

Research report generation

Automatically generate formatted scientific research reports, covering research background, method description, result summary, chart display, and conclusion recommendations, in compliance with SCI paper publication standards.

5. Visualization and Interaction Design

High quality data visualization

Provide dynamic charts (such as heat maps, scatter plots, box plots, 3D surface plots, etc.), support interactive operations and real-time adjustments, and help researchers visually present research results.

User friendly interface

The system interface adopts modular design, providing detailed guidance documents and visual flowcharts, suitable for various users, and can be easily used without programming foundation.

System architecture and operational characteristics

1. Modular design

The system adopts a modular architecture, with each functional module running independently, and users can choose to activate the corresponding module according to their needs. Smooth data transfer between modules, supporting custom analysis processes.

 

2. High performance computing support

Through parallel computing and cloud acceleration, the system can efficiently process large-scale medical data. The built-in distributed computing framework enables it to run efficiently even in ordinary hardware environments.

 

3. Strong compatibility and scalability

The system supports operating systems such as Windows and Linux, and can seamlessly integrate with mainstream analysis tools such as SPSS, MATLAB, Python libraries, etc., making it convenient for researchers to integrate existing resources.

 

Application scenarios and value

1. Clinical research

Patient classification and risk assessment

The system can help researchers perform cluster analysis on large-scale patient data, identify high-risk patient populations, and predict disease course trends.

Drug efficacy evaluation

Quickly evaluate the efficacy and safety of drugs through time series analysis and multivariate modeling, providing data support for new drug development.

2. Precision medicine

Personalized treatment plan design

The system integrates patients' clinical data, genetic data, and lifestyle information to tailor treatment plans for each patient.

Disease risk prediction

Through deep learning models, identify key factors that affect disease occurrence and provide support for early diagnosis.

3. Public Health and Epidemiology

Epidemic Trend Prediction

Based on time series and machine learning techniques, the system can accurately model the transmission trend of infectious diseases and assist in formulating prevention and control strategies.

Evaluation of Health Intervention Effectiveness

The system evaluates the effectiveness of public health interventions through joint analysis of multidimensional data, providing scientific basis for policy-making.

4. Molecular medicine research

Analysis of Gene Function and Regulatory Network

The system can perform functional annotation on genomic data and generate gene regulatory networks, providing a new perspective for molecular mechanism research.

Multi omics integration analysis

Integrate and analyze genomic, epigenetic, transcriptomic, and metabolomic data to reveal the molecular mechanisms and key pathways of diseases.

Introduction to Gastrointestinal Electron Microscopy and Pathology Dataset

 

This dataset collects electron microscopy and pathological examination data from over 100 gastrointestinal related patients, providing valuable resources for studying the molecular pathological mechanisms of gastrointestinal diseases. The data includes high-resolution electron microscope images and detailed pathological descriptions of each patient, covering common pathological features such as inflammation, ulcers, and cancer, as well as clinical indicators such as age, gender, disease duration, and laboratory test results.

 

Core data content

Electron microscopy image

 

High quality subcellular structural images display the ultrastructural abnormalities of gastrointestinal tissue cells, such as mitochondrial swelling, endoplasmic reticulum swelling, and changes in intercellular spaces.

Support the analysis of pathological patterns in cell structure and reveal the microscopic pathological basis of diseases.

Pathological report

 

Detailed diagnosis including tissue sections, such as degree of inflammatory infiltration, lesion distribution, cell division index, and tumor grade.

Combined with HE staining images and immunohistochemical (IHC) markers, it provides multidimensional pathological evidence for researchers.

clinical data

 

Each patient is accompanied by relevant clinical information, including disease classification (such as ulcerative colitis, gastric cancer), treatment plan, and prognosis results, which facilitates the construction of a model to predict disease progression.

Application value

This dataset provides comprehensive support for basic research and clinical translation of gastrointestinal diseases. Researchers can use electron microscopy data to analyze cellular pathological changes and explore the molecular mechanisms of diseases in combination with pathological reports. Meanwhile, data can be used for the development and validation of machine learning models, such as predicting patient prognosis or drug response, providing new directions for precision medicine.

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This is an algorithm package designed for computer professionals to develop medical algorithms.

Introduction to Algorithm Package for Gastrointestinal Electron Microscopy and Pathology Dataset

 

This algorithm package is specifically developed for computer professionals to assist medical researchers in constructing efficient medical diagnostic algorithms using gastrointestinal electron microscopy images and pathological data. The dataset contains detailed pathological and electron microscopy image data of over 100 gastrointestinal related patients, covering different pathological states of gastrointestinal diseases such as inflammation, ulcers, tumors, etc. By combining these data, developers can use algorithms such as machine learning and deep learning to solve complex problems such as medical image analysis, pathological diagnosis, and disease prediction.

 

Core functions of algorithm package

Image Processing and Analysis

The algorithm package provides functional modules for preprocessing gastrointestinal electron microscopy images, including denoising, image enhancement, feature extraction, and other operations. Developers can extract cellular substructure information from electron microscopy images based on these features, such as cell membranes, mitochondria, nuclei, etc., and analyze lesion sites in conjunction with pathological data. By using deep learning models, developers can train image classification networks to identify normal and diseased tissues, further improving the accuracy of diagnosis.

 

Pathological image analysis

The algorithm package supports automated processing and analysis of pathological images, including segmentation of tissue slices, feature extraction, and lesion detection. By combining image segmentation algorithms and convolutional neural networks (CNN), the system can automatically identify and label lesion areas, thereby helping pathologists improve image interpretation efficiency. In addition, using image classification and object detection techniques, pathological samples can be automatically graded to assist doctors in accurate diagnosis.

 

Clinical data association and disease prediction

In addition to image data, the algorithm package can also process patients' clinical data by combining clinical indicators (such as age, gender, disease type) with electron microscopy and pathological image data through data fusion technology. Developers can use machine learning models such as random forests, support vector machines, etc. for disease prediction and prognosis analysis, further providing data support for personalized treatment.

 

Multimodal data fusion

This algorithm package provides a framework for multimodal data fusion, allowing developers to combine electron microscopy images, pathological images, and clinical data for comprehensive analysis. Through multimodal networks in deep learning, researchers can explore the correlations between different data sources, thereby improving the accuracy and predictive ability of disease diagnosis.

 

application prospect

This algorithm package provides a powerful tool for the field of medical image analysis, which can help computer professionals develop algorithms with medical diagnostic assistance capabilities. With the continuous development of artificial intelligence in the medical field, developers can make breakthroughs in automated diagnosis, prognosis prediction, personalized treatment plan recommendation, and other aspects of gastrointestinal diseases based on this algorithm package, providing intelligent support for medical clinical practice. In addition, this package can also be applied to other types of medical image analysis, laying the foundation for precise diagnosis and treatment of various diseases.

Introduction to Medical Automation Robots

 

Medical automation robot is an intelligent system aimed at improving the efficiency of medical services, reducing human errors, and providing precise medical solutions. Through deep integration with hospital databases and medical equipment, the robot is able to automate the processing of medical data, analyze patient medical records, assist in diagnosis, and generate personalized treatment plans. With the rapid development of artificial intelligence technology and machine learning, medical automation robots will become important tools in the medical industry, especially in the early diagnosis of complex diseases, optimization of treatment plans, and patient management.

 

Core functions

Automated data processing and management

Medical automation robots can extract patient medical record information from hospital databases, including various clinical data such as historical symptoms, examination results, and drug allergy history. Through natural language processing technology, robots can also automatically recognize key information in patient medical records and perform data cleaning and organization, providing reliable data support for subsequent analysis work.

 

Intelligent diagnostic support

Robots, combined with big data and deep learning technologies, can identify potential lesion areas from medical images (such as CT, MRI, X-ray, etc.) and pathological images for automated disease diagnosis. For example, by analyzing electron microscopy images, robots can detect tumors, inflammation, or other lesions and provide preliminary diagnostic results for doctors to refer to. In addition, robots can generate possible diagnostic conclusions based on patients' clinical data and existing medical knowledge bases to assist doctors in making decisions.

 

Personalized treatment plan recommendation

Based on the specific condition of the patient, the robot can automatically recommend personalized treatment plans by combining a large amount of medical literature, guidelines, and other case data. The system will recommend the most suitable treatment plan based on factors such as the patient's disease type, constitution, drug sensitivity, etc., to help doctors choose the optimal treatment path and reduce the incidence of medical errors.

 

Patient management and follow-up

Medical automation robots can also assist doctors in follow-up management through remote monitoring and regular tracking after patients are discharged. Robots can automatically remind doctors to perform necessary examinations based on the patient's treatment progress and recovery status, ensuring timely tracking and adjustment of various indicators during the patient's rehabilitation process.

 

Acquisition method

The development and implementation of medical automation robots require a powerful database support. If you need to obtain the robot and customize its development, you can contact members of the hospital's database development team. By working closely with the development team, you can access the latest system features, perform data integration, and optimize processes. In addition, team members will provide system deployment and maintenance support based on the specific needs of the hospital, ensuring that the robot can be smoothly integrated into the existing medical environment and achieve maximum efficiency.

 

Medical automation robots will bring tremendous changes to the healthcare industry, not only improving diagnosis and treatment efficiency, but also providing more accurate and personalized medical services for patients.

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Addendum: Introduction to Brain Computer Interface Data

Brain Computer Interface (BCI) is a cutting-edge technology aimed at enabling direct communication between the brain and external devices. By capturing and decoding brain activity, brain computer interfaces can provide humans with new ways of information processing and interaction. This technology is widely used in fields such as healthcare, neuroscience, artificial intelligence, and intelligent control, especially in helping paralyzed patients recover function, improving the treatment of neurodegenerative diseases, and enhancing human cognitive abilities, demonstrating enormous potential.

 

Background of Brain Computer Interface Technology

Brain computer interface obtains electrophysiological signals of the brain through various sensing devices, which usually come from technologies such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), near-infrared spectroscopy (NIRS), etc. These signals undergo amplification, filtering, feature extraction, and decoding processing, ultimately achieving interaction between the brain and computers or external devices. Through brain computer interfaces, users can use their thinking to control devices such as robotic arms, computer cursor, wheelchairs, etc., thereby overcoming physical movement barriers.

 

The foundation of BCI technology relies on precise interpretation and transmission of brain waves, which involves interdisciplinary applications including neuroscience, computer science, signal processing, and machine learning. With the rapid development of neuroscience and artificial intelligence, the accuracy and real-time performance of brain computer interface systems have greatly improved, promoting the practical application of this technology in multiple fields.

 

Source and Processing of Brain Computer Interface Data

The data sources of brain computer interfaces usually include electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), invasive electrode signals (such as ECoG and spinal cord electrodes), etc. Different types of EEG signals provide different data dimensions and information content for BCI systems.

Connection for obtaining clinical data:

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