DKT transfers biomarker information between neurodegenerative diseases.
problem Estimating biomarker trajectories in rare neurodegenerative diseases with limited data.
method DKT is a joint-disease generative model that transfers biomarker progressions from common neurodegenerative diseases to rare ones.
result DKT estimates plausible multimodal biomarker trajectories in rare diseases like PCA using only unimodal data.
Bayesian approach models neurodegenerative diseases without clinical labels.
problem Personalized, predictive modeling of neurodegenerative diseases.
method Probabilistic programmed deep kernel learning combining Gaussian processes and neural networks.
result Surpasses deep learning in accuracy and timeliness of predicting neurodegeneration.
New method predicts Parkinson's using deep neural network latent info.
problem Medical diagnosis of Parkinson's disease.
method Transfer learning, k-means clustering, k-Nearest Neighbour classification of DNN representations.
result Improved prediction of Parkinson's disease based on MRI and DaT Scan data.
A method to explain disease transformation using biomarker covariance matrices.
problem Understanding disease transformation from a healthy baseline.
method Modeling healthy and disease states of biomarker covariance matrices to characterize perturbations.
result Disease perturbs the biomarker covariance structure, allowing for mechanistic explanations and individual patient prognosis.
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular le…
CRBM generates digital twins for MS patients, aiding in disease progression analysis.
problem Characterizing and analyzing disease progression in MS patients.
method Unsupervised machine learning with Conditional Restricted Boltzmann Machines (CRBMs).
result Generated digital twins are statistically indistinguishable from actual subjects.
The paper generates future brain imaging sequences for Alzheimer's disease detection.
problem Understanding brain aging and neurodegenerative diseases through sequential image data.
method Formulated a min-max problem based on f-divergence to learn a time series generator using a deep neural network. result Generated image sequences converge to the latent truth under specific conditions, enhancing downstream tasks like Alzheimer's disease detection.
Enhances disease progression modeling using LLMs for complex brain connectivity.
problem Inaccurate predictions of disease spread due to oversimplified brain connectivity models.
method Uses LLMs to synthesize multi-modal relationships and learn disease trajectories from longitudinal data.
result Superior prediction accuracy and interpretability compared to traditional methods.
MAGIC uncovers disease heterogeneity across brain scales.
problem Understanding distinct subtypes of brain diseases at different spatial scales.
method Multi-scale Heterogeneity Analysis and Clustering (MAGIC) using semi-supervised clustering.
result Two main subtypes of AD identified with distinct atrophy patterns.
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease relate…
AI framework diagnoses Parkinson's disease with 100% accuracy.
problem Expertise-demanding medical imaging procedures for Parkinson's disease diagnosis.
method End-to-end, multi-modality diagnosis framework using T1-MRI and 11C-CFT PET.
result 100% accuracy in PD/NL classification.
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by…
Deep neural network classifies DaTscan SPECT images for Parkinson's Disease.
problem Early diagnosis of Parkinson's Disease through objective analysis of SPECT images.
method InceptionV3 architecture with custom binary classifier, 10-fold cross validation.
result Deep neural network achieves high accuracy in classifying DaTscan SPECT images.
DIVE models brain disease progression with high spatial resolution.
problem Reconstruct long-term brain pathology from short-term data.
method Clusters vertex-wise biomarker measurements, estimates average trajectories, and identifies disease-specific patterns.
result Reveals distinct patterns of pathology in different diseases and biomarker types.
Simple attention model outperforms complex sEMG classifiers.
problem Improving myoelectric control for robotic prosthetics.
method Attention-based model for sEMG signal classification.
result Simple model achieves benchmark results on multiple datasets.
Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature lear…
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies h…
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to f…
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
problem Assessing neurological state of Parkinson's disease patients using speech, handwriting, and gait signals.
method GMM-UBM and i-vectors applied to speech, handwriting, and gait signals.
result Different feature sets from each signal are crucial for assessing Parkinson's patients.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
Proposes a method to cluster fMRI data and estimate brain connectivity networks.
problem Clustering fMRI data to identify patient groups based on brain connectivity.
method Random covariance clustering model (RCCM) to cluster subjects and estimate individual and shared FC networks.
result RCCM outperforms other methods in clustering and FC network estimation, demonstrated through simulations and real data.
Paper introduces a framework for diagnosing Alzheimer's disease using higher-order topological features from fMRI.
problem Diagnosing Alzheimer's disease using brain network topology.
method Persistent homology to extract higher-order features (cycles, cavities) from fMRI data.
result Framework significantly outperforms existing methods in AD classification.
Project predicts Alzheimer's progression using neural networks and novel data processing.
problem Difficulty in early identification of Alzheimer's patients.
method Used machine learning, specifically neural networks, and a novel pre-processing technique.
result Neural network model accurately predicts AD progression with high accuracy.
Model predicts respiratory insufficiency in ALS patients with high accuracy.
problem Lack of insight into risk of error and optimal time for non-invasive ventilation.
method Combines Conformal Prediction and mixture experts to predict respiratory insufficiency and optimal time.
result Near 80% of predictions correctly identified, with confidence measures.
Study identifies five AD subtypes using graph diffusion and similarity learning.
problem Identifying homogeneous AD subtypes to improve diagnosis and treatment.
method Unsupervised clustering with graph diffusion and similarity learning.
result Five distinct AD subtypes identified with significant differences in biomarkers and clinical features.
Study improves LLMs for PPI analysis by addressing uncertainty.
problem Uncertainty in LLM predictions for PPIs.
method Fine-tuned LLaMA-3 and BioMedGPT models, LoRA ensembles, Bayesian LoRA for UQ.
result Competitive PPI identification performance across diverse disease contexts.
Develops a machine learning model to predict ALS progression and assistive device use.
problem Challenges in predicting clinically meaningful milestones in ALS.
method Integrates longitudinal ALSFRS-R trajectories with survival modeling.
result Generates individualized survival curves and predicts wheelchair-free survival.
DVNet efficiently segments large neurovascular datasets using skip connections.
problem Challenges in segmenting large neurovascular datasets from high-throughput microscopy data.
method A fully-convolutional, deep, and densely-connected encoder-decoder network with skip connections.
result DVNet achieves superior performance in semantic segmentation of neurovascular datasets.
CSTs improve stability in covariance spectrum analysis without training.
problem Stability and expressiveness in covariance spectrum analysis.
method Sequential application of covariance wavelet filters to input data.
result Stable and expressive hierarchical representations in low-data settings.
DPVis integrates HMMs into visualizations for disease progression analysis.
problem Challenges in interpreting HMMs for disease progression modeling.
method Design study with clinical experts, visualizations of HMM parameters and outcomes.
result DPVis successfully evaluates and summarizes disease progression models.
SS3M learns disease phenotypes from few labels.
problem Lack of supervised data for disease phenotyping.
method Semi-Supervised Mixed Membership Model (SS3M).
result SS3M learns interpretable disease phenotypes.
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
System recommends disease treatments based on big data and cloud computing.
problem Inaccurate disease classification and treatment recommendations due to complex symptoms and multi-pathogenesis.
method DPCA for disease-symptom clustering, Apriori for D-D and D-T rules, parallel Apache Spark implementation.
result Effective disease-symptom clustering and accurate treatment recommendations for inexperienced doctors.
Model learns disease self-representations for drug repositioning.
problem Drug repositioning for disease treatment.
method Enforces proximity in disease self-representations to preserve human phenome network structure.
result Method outperforms state-of-the-art approaches and produces biologically interpretable disease self-representations.
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
Paper uses GANs for efficient rare disease detection.
problem Efficient detection of rare diseases with limited labeled data.
method Semi-supervised learning with GANs.
result Best precision-recall scores compared to baseline techniques.
This research uses machine learning to identify Alzheimer's disease subtypes and predict progression.
problem Heterogeneity in Alzheimer's disease clinical manifestations and progression rate limit personalized care and treatment planning.
method Unsupervised and supervised machine learning approaches applied to ADNI data.
result Identification of patient subtypes and prediction of disease progression zones.
Bayesian meta-learning predicts Alzheimer's disease progression.
problem Predicting individual Alzheimer's disease progression from limited data.
method Bayesian meta-learning approach that dynamically predicts disease score distributions.
result Bayesian meta-learner outperforms single-task models and deterministic meta-learners, especially for long-term predictions.
VGAE learns gene-disease associations from networks, predicting disease-genes.
problem Predicting gene-disease associations from disease-gene networks.
method Introducing VGAE, a variational graph auto-encoder for disease-gene prediction.
result VGAE and C-VGAE outperform baseline methods in disease-gene prediction.
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-con…
Deep Belief Network predicts lncRNA-disease associations with high accuracy.
problem Accurately identifying lncRNA-disease associations to understand lncRNA functionality and disease mechanism.
method Proposes a DBN-based model using heterogeneous networks and DBN for feature learning.
result Obtained AUC of 0.96 and AUPR of 0.967 on standard dataset.
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an indivi…
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
Paper predicts multiple types of miRNA-disease associations using tensor decomposition.
problem Predicting miRNA-disease associations, especially multi-type ones.
method Represented miRNA-disease-type triplets as a tensor and used Tensor Decomposition methods.
result Tensor Decomposition methods improve a recent baseline by up to 38% in top-1 F1.
The technique of Formal Concept Analysis is applied to a dataset describing the traits of rodents, with the goal of identifying zoonotic disease carriers,or those species carrying infections that can spillover to cause human disease. The concepts identified among these species together provide rules-of-thumb about the …
Paper uses TDA for automated Parkinson's disease classification and severity assessment.
problem Manual diagnosis of neurological diseases is time-consuming and inaccurate.
method Combines Topological Data Analysis (TDA) with machine learning on postural shift data.
result Proposes a stable and accurate method for classifying Parkinson's disease.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
Graph network predicts circRNA-disease associations using multi-source similarity features.
problem Identifying circRNA-disease associations is challenging and time-consuming.
method Proposes a graph convolution network framework using multi-source similarity information.
result Framework predicts circRNA-disease associations with promising results and outperforms existing methods.