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1		-=== **Overview** ===
	1	+Here is the updated **Methodology** section for the EBRAINS Wiki, incorporating the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)** for **biomarker classification across all neurodegenerative diseases**.
2	2
3		-This section describes the step-by-step process used in the **Neurodiagnoses** project to develop a novel diagnostic framework for neurological diseases. The methodology integrates artificial intelligence (AI), biomedical ontologies, and computational neuroscience to create a structured, interpretable, and scalable diagnostic system.
4		-
5	5	----
6	6
7		-=== **1. Data Integration** ===
	5	+== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
8	8
9		-==== **Data Sources** ====
	7	+=== **Project Overview** ===
10	10
11		-* **Biomedical Ontologies**:
12		-** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
13		-** Gene Ontology (GO) for molecular and cellular processes.
14		-* **Neuroimaging Datasets**:
15		-** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
16		-* **Clinical and Biomarker Data**:
17		-** Anonymized clinical reports, molecular biomarkers, and test results.
	9	+Neurodiagnoses AI implements **AI-driven diagnostic and prognostic models** for central nervous system (CNS) disorders, expanding the **Florey Dementia Index (FDI) methodology** to a broader set of neurological conditions. The approach integrates **multimodal data sources** (EEG, neuroimaging, biomarkers, and genetics) and employs machine learning models to provide **explainable, real-time diagnostic insights**. This framework now incorporates **Neuromarker**, a **generalized biomarker ontology** that categorizes biomarkers across neurodegenerative diseases, enabling **standardized, cross-disease AI training**.
18	18
	11	+== **Neuromarker: Generalized Biomarker Ontology** ==
19	19
20		-==== **Data Preprocessing** ====
	13	+Neuromarker extends the **Common Alzheimer’s Disease Research Ontology (CADRO)** into a **cross-disease biomarker categorization framework** applicable to all neurodegenerative diseases (NDDs). It allows for **standardized classification, AI-based feature extraction, and multimodal integration**.
21	21
22		-1. **Standardization**: Ensure all data sources are normalized to a common format.
23		-1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
24		-1. **Data Cleaning**: Handle missing values and remove duplicates.
	15	+=== **Core Biomarker Categories** ===
25	25
	17	+The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
	18	+
	19	+|=**Category**|=**Description**
	20	+|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
	21	+|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
	22	+|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
	23	+|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
	24	+|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
	25	+|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
	26	+|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
	27	+|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
	28	+
26	26	----
27	27
28		-=== **2. AI-Based Analysis** ===
	31	+== **How to Use External Databases in Neurodiagnoses** ==
29	29
30		-==== **Model Development** ====
	33	+To enhance diagnostic accuracy, Neurodiagnoses AI integrates data from **multiple biomedical and neurological research databases**. Researchers can follow these steps to access, prepare, and integrate data into the Neurodiagnoses framework.
31	31
32		-* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
33		-* **Classification Models**:
34		-** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
35		-** Purpose: Predict the likelihood of specific neurological conditions based on input data.
	35	+=== **Potential Data Sources** ===
36	36
37		-==== **Dimensionality Reduction and Interpretability** ====
	37	+Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
38	38
39		-* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
40		-* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
	39	+* **ADNI**: Alzheimer's Disease Imaging & Biomarkers → [[ADNI>>url:https://adni.loni.usc.edu/]]
	40	+* **PPMI**: Parkinson’s Disease Imaging & Biospecimens → [[PPMI>>url:https://www.ppmi-info.org/]]
	41	+* **GP2**: Whole-Genome Sequencing for PD → [[GP2>>url:https://gp2.org/]]
	42	+* **Enroll-HD**: Huntington’s Disease Clinical & Genetic Data → [[Enroll-HD>>url:https://www.enroll-hd.org/]]
	43	+* **GAAIN**: Multi-Source Alzheimer’s Data Aggregation → [[GAAIN>>url:https://gaain.org/]]
	44	+* **UK Biobank**: Population-Wide Genetic, Imaging & Health Records → [[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
	45	+* **DPUK**: Dementia & Aging Data → [[DPUK>>url:https://www.dementiasplatform.uk/]]
	46	+* **PRION Registry**: Prion Diseases Clinical & Genetic Data → [[PRION Registry>>url:https://prionregistry.org/]]
	47	+* **DECIPHER**: Rare Genetic Disorder Genomic Variants → [[DECIPHER>>url:https://decipher.sanger.ac.uk/]]
41	41
42	42	----
43	43
44		-=== **3. Diagnostic Framework** ===
	51	+== **1. Register for Access** ==
45	45
46		-==== **Axes of Diagnosis** ====
	53	+* Each external database requires **individual registration and access approval**.
	54	+* Ensure compliance with **ethical approvals and data usage agreements** before integrating datasets into Neurodiagnoses.
	55	+* Some repositories may require a **Data Usage Agreement (DUA)** for sensitive medical data.
47	47
48		-The framework organizes diagnostic data into three axes:
	57	+----
49	49
50		-1. **Etiology**: Genetic and environmental risk factors.
51		-1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
52		-1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
	59	+== **2. Download & Prepare Data** ==
53	53
54		-==== **Recommendation System** ====
	61	+* Download datasets while adhering to **database usage policies**.
	62	+* Ensure files meet **Neurodiagnoses format requirements**:
55	55
56		-* Suggests additional tests or biomarkers if gaps are detected in the data.
57		-* Prioritizes tests based on clinical impact and cost-effectiveness.
	64	+|=**Data Type**|=**Accepted Formats**
	65	+|**Tabular Data**|.csv, .tsv
	66	+|**Neuroimaging**|.nii, .dcm
	67	+|**Genomic Data**|.fasta, .vcf
	68	+|**Clinical Metadata**|.json, .xml
58	58
	70	+* **Mandatory Fields for Integration**:
	71	+** **Subject ID**: Unique patient identifier
	72	+** **Diagnosis**: Standardized disease classification
	73	+** **Biomarkers**: CSF, plasma, or imaging biomarkers
	74	+** **Genetic Data**: Whole-genome or exome sequencing
	75	+** **Neuroimaging Metadata**: MRI/PET acquisition parameters
	76	+
59	59	----
60	60
61		-=== **4. Computational Workflow** ===
	79	+== **3. Upload Data to Neurodiagnoses** ==
62	62
63		-1. **Data Loading**: Import data from storage (Drive or Bucket).
64		-1. **Feature Engineering**: Generate derived features from the raw data.
65		-1. **Model Training**:
66		-1*. Split data into training, validation, and test sets.
67		-1*. Train models with cross-validation to ensure robustness.
68		-1. **Evaluation**:
69		-1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
70		-1*. Compare against baseline models and domain benchmarks.
	81	+=== **Option 1: Upload to EBRAINS Bucket** ===
71	71
	83	+* Location: **EBRAINS Neurodiagnoses Bucket**
	84	+* Ensure **correct metadata tagging** before submission.
	85	+
	86	+=== **Option 2: Contribute via GitHub Repository** ===
	87	+
	88	+* Location: **GitHub Data Repository**
	89	+* Create a **new folder under /data/** and include a **dataset description**.
	90	+* **For large datasets**, contact project administrators before uploading.
	91	+
72	72	----
73	73
74		-=== **5. Validation** ===
	94	+== **4. Integrate Data into AI Models** ==
75	75
76		-==== **Internal Validation** ====
	96	+* Open **Jupyter Notebooks** on EBRAINS to run **preprocessing scripts**.
	97	+* **Standardize neuroimaging and biomarker formats** using harmonization tools.
	98	+* Use **machine learning models** to handle **missing data** and **feature extraction**.
	99	+* Train AI models with **newly integrated patient cohorts**.
77	77
78		-* Test the system using simulated datasets and known clinical cases.
79		-* Fine-tune models based on validation results.
	101	+**Reference**: See docs/data_processing.md for detailed instructions.
80	80
81		-==== **External Validation** ====
	103	+----
82	82
83		-* Collaborate with research institutions and hospitals to test the system in real-world settings.
84		-* Use anonymized patient data to ensure privacy compliance.
	105	+== **AI-Driven Biomarker Categorization** ==
85	85
	107	+Neurodiagnoses employs **AI models** for biomarker classification:
	108	+
	109	+|=**Model Type**|=**Application**
	110	+|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
	111	+|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
	112	+|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
	113	+
86	86	----
87	87
88		-=== **6. Collaborative Development** ===
	116	+== **Collaboration & Partnerships** ==
89	89
90		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	118	+=== **Partnering with Data Providers** ===
91	91
92		-* **Jupyter Notebooks**: For data analysis and pipeline development.
93		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
94		-* **Wiki Pages**: For documenting methods and results.
95		-* **Drive and Bucket**: For sharing code, data, and outputs.
96		-* **Collaboration with related projects: **For instance: [[//Beyond the hype: AI in dementia – from early risk detection to disease treatment//>>https://www.lethe-project.eu/beyond-the-hype-ai-in-dementia-from-early-risk-detection-to-disease-treatment/]]
	120	+Neurodiagnoses seeks partnerships with data repositories to:
97	97
	122	+* Enable **API-based data integration** for real-time processing.
	123	+* Co-develop **harmonized AI-ready datasets** with standardized annotations.
	124	+* Secure **funding opportunities** through joint grant applications.
	125	+
	126	+**Interested in Partnering?**
	127	+
	128	+* If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
	129	+* **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	130	+
98	98	----
99	99
100		-=== **7. Tools and Technologies** ===
	133	+== **Final Notes** ==
101	101
102		-* **Programming Languages**: Python for AI and data processing.
103		-* **Frameworks**:
104		-** TensorFlow and PyTorch for machine learning.
105		-** Flask or FastAPI for backend services.
106		-* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
107		-* **EBRAINS Services**:
108		-** Collaboratory Lab for running Notebooks.
109		-** Buckets for storing large datasets.
	135	+Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
	136	+
	137	+**For additional technical documentation**:
	138	+
	139	+* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	140	+* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	141	+
	142	+**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
	143	+
	144	+----
	145	+
	146	+This **updated methodology** now incorporates [[https:~~/~~/github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/biomarker_ontology>>https://Neuromarker]] for standardized biomarker classification, enabling **cross-disease AI training** across neurodegenerative disorders.