Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either "high risk" or "low risk" in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

What Threshold of Amyloid Reduction Is Necessary to Meaningfully Improve Cognitive Function in Transgenic Alzheimer's Disease Mice?

Background

Amyloid-β plaques (Aβ) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research.

Objective

The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE).

Methods

Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies.

Results

K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aβ or 50% "high reduction" of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aβ42 to Aβ40. Supervised learning with an 80% -20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R2 = 0.8 to 0.95).

Conclusions

Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition.

Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review

Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer's Disease

BACKGROUND

The complex and not yet fully understood etiology of Alzheimer's disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms.

OBJECTIVE

The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer's disease (AsymAD), and AD.

METHODS

Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning.

RESULTS

A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS.

CONCLUSION

Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

Alzheimer's disease classification using cluster-based labelling for graph neural network on heterogeneous data

Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data-driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau-positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non-linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re-labelled AD cases. The re-labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re-labelled data with a multiclass area-under-the-curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; p = 0.02). Overall, our work suggests that more objective cluster-based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.