The importance of Integration of AI, Brain Neuro-Imaging Machine Vision, Peripheral Blood Gene Expressions, and Genomics for Better Prognosis and Diagnosis Predictions of Alzheimer's Disease
Bibliographic record
Abstract
Many diseases, including cancer, have received much attention from their respective research communities in machine learning, deep learning, and bioinformatics tools for predictive models based on gene expressions and other biomarkers in the life sciences. Alzheimer's disease (AD) has not yet gotten the attention it deserves. This is due to what appears to be (a) the lack of relevant large enough data sets with biological samples throughout the different stages of the disease with the same patients, (b) other challenges such as noisy peripheral blood, (c) availability in many instances of human AD patients brain biological RNA, and mRNA tissue samples only post mortem, (d) some of the testing modalities are expensive, maybe intrusive, or available only in some specific clinics, or researchers, and not to the general community. In general, the analysis of peripheral blood mRNA gene expressions, among others, is a powerful tool for predicting AD biological progression and process phases related to mental and physiological changes in the disease for better diagnosis and prognosis predictions and the eventual identification of novel therapeutics and drug discovery. In this study and talk, we present some of the results and show and discuss the importance of integrating brain neuro-imaging & machine vision, peripheral blood gene expressions, genomics, and other AD biomarker modalities for better prognosis, diagnosis, and risk assessment predictions in the early stages of Alzheimer's disease. It also focuses on better data mining techniques for a better selection of biomarkers using the ADNI data sets and other related ones. Unlike some other known Alzheimer's biomarkers/tests, such as brain neuroimaging (CT, MRI, PET, Amyloid PET, Tau PET, Fluorodeoxyglucose (FDG) PET scans), Cerebrospinal fluid biomarkers (CSF), Blood tests (genetic testing), clinical, demographic data, genomics, gene expressions and differential gene expressions (DEGs), integrated with other neuro-imaging machine vision biomarkers , are uniquely tuned for eventual better disease prognosis and diagnosis predictions, and eventual drug discovery and novel therapeutics identification in early stages of Alzheimer's disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".