DGCNN improves graph CNNs by handling irregular graphs.
problem Handling structural information loss and redundancy in graph CNNs.
method Proposes DGCNN using DGCL with mixed Gaussian model to handle irregular graphs.
result DGCNN outperforms state-of-the-art methods in graph classification and retrieval.
PR-GNN identifies salient brain regions for ASD biomarkers.
problem Identifying brain regions associated with neurological disorders.
method Pooling Regularized Graph Neural Network (PR-GNN) with novel salient region selection.
result PR-GNN outperforms baseline methods in ASD classification accuracy.
We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ graph kernels that define a kernel dot product between two graphs. This enables us to take advantage of spatio-temporal information to captu…
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …
Combining brain structure and function for ASD diagnosis.
problem Identifying neuropathological bases of Autism Spectrum Disorder.
method Modeling brain structure as a graph, using rs-fMRI signals, and applying Graph Signal Processing.
result Decision tree outperforms state-of-the-art methods in diagnosing ASD.
Graph Convolutional Networks improve disease prediction accuracy.
problem Improving disease prediction accuracy using graph-based methods.
method Graph Convolutional Networks (GCNs) for disease prediction.
result Improved disease prediction accuracy on ABIDE and ADNI datasets.
Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work has shown that it can be beneficial to incorporate a wide variety …
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
M2E embeds multi-view multi-graph brain networks for better clustering.
problem Clustering brain networks from multiple views to understand disease mechanisms.
method Stack multi-graphs into tensors, use tensor techniques to leverage multi-view and multi-graph interactions.
result M2E outperforms existing methods on clustering brain networks.
Deep networks show less disorder and more monotonic behavior.
problem Understanding the disorder and monotonicity in deep neural networks.
method Computing frustration and analyzing near-monotonicity in signed graphs of deep networks.
result Deep networks exhibit less disorder and more monotonic behavior than expected.
Graph Neural Network identifies ASD biomarkers from fMRI data.
problem Finding biomarkers for Autism Spectrum Disorder (ASD).
method Graph Neural Network (GNN) for analyzing task-fMRI brain networks, 2-stage pipeline to interpret feature importance.
result GNN achieves high accuracy in identifying ASD biomarkers and reveals their association with social behaviors.
FuDGE estimates differences between functional graphs in high-dimensional settings.
problem Estimating differences between two undirected functional graphical models with shared structures.
method FuDGE: A method that directly estimates the functional differential graph without first estimating individual graphs.
result FuDGE consistently estimates the functional differential graph in high-dimensional settings.
Generative model predicts multiple brain graphs from one, preserving topology.
problem Predicting multiple brain graphs from a single one, preserving topology.
method MultiGraphGAN architecture, graph adversarial auto-encoder, cluster-specific decoders, topological loss.
result Significantly outperformed variants in multi-view brain graph generation.
Machine learning identifies distinctive mood patterns in bipolar and borderline personality disorders.
problem Challenges in diagnosing bipolar and borderline personality disorders using retrospective mood recall.
method Signature-based machine learning model using daily mood ratings from smartphone apps.
result The model effectively separates participants into three groups with high accuracy.
Cheap model diagnoses vocal disorders accurately.
problem Diagnosing vocal disorders without expensive equipment.
method Used Mel-Cepstrum vectors and Support Vector Machine.
result Accurately diagnosed three vocal disorders.
We propose some kinetic models of wealth exchange and investigate their behavior on directed networks though numerical simulations. We observe that network topology and directedness yields a variety of interesting features in these models. The nature of asset distribution in such directed networks show varied results, …
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.
method Causal inference, graph analysis, temporal complexity measures, machine learning.
result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.
Study uses interviews to automatically detect BD and BPD with good accuracy.
problem Challenges in distinguishing BD and BPD from clinical interviews.
method Developed a multi-modal dataset and used a linear classifier with selected features from interviews.
result Different sets of features characterize BD and BPD, providing insights into their differences.
LSTM model detects voice disorders with high accuracy.
problem Automated detection of voice disorders is challenging due to continuous audio data.
method Used Long Short Term Memory (LSTM) model for feature extraction and classification of voice disorders.
result 22% sensitivity, 97% specificity, 56% unweighted average recall.
Hi-RES framework extracts medical relations from articles and EHRs.
problem Manual annotation bottleneck in relation extraction.
method Labeling sentences, creating improved negative samples, using pretrained language models, and combining EHR embeddings.
result Significant accuracy increases in relation extraction, up to 0.998 for disorder-location relations.
Deep model diagnoses psychiatric disorders from fMRI data.
problem Diagnosing psychiatric disorders from brain imaging data.
method Deep neural generative model using Bayes' rule.
result Improved diagnostic accuracy over competitive models.
ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
Study on a pinning model with random walk increments, showing convergence to a critical disordered pinning measure.
problem Understanding the critical behavior of a disordered pinning model.
method Analyzing a disordered pinning model induced by a random walk with specific moment conditions, showing convergence to a limiting measure.
result Convergence of point-to-point partition functions to the critical disordered pinning measure in the critical window.
Framework learns structural and functional brain network embeddings while preserving their properties.
problem Joint learning of structural and functional brain networks while preserving their intrinsic properties.
method Siamese community-preserving graph convolutional network (SCP-GCN) that learns from both structural and functional connectivity.
result Superior performance in neurological disorder analysis compared to existing methods.
New minimal surfaces show stacking disorder in periodic structures.
problem Reproducing experimental twinning defects in periodic minimal surfaces.
method Constructing non-periodic minimal surfaces that lift to disordered stacking in 3D.
result Reproduced twinning defects in periodic minimal surfaces as stacking disorder.
Proposes deep learning method for GCI detection from pathological speech.
problem Detecting glottal closure instants (GCI) in pathological acoustic speech.
method Convolutional neural network with fused deep acoustic speech and linear prediction residual features.
result Significantly better than state-of-the-art methods in GCI detection.
Deep learning predicts mental disorders from audio and text samples.
problem Predicting mental disorders from speech samples.
method Multimodal deep learning structure using various pre-trained models for audio and text embeddings, transfer learning, and auxiliary corpora.
result Acceptable accuracy in predicting mental disorders through multimodal analysis.
Paper presents a method to diagnose schizophrenia using fMRI dynamics from healthy controls.
problem Early diagnosis of mental disorders like schizophrenia using fMRI.
method Self-supervised pre-training on fMRI dynamics of healthy controls for transfer learning.
result Effective classification of schizophrenia using fMRI dynamics with small datasets.
CI-GNN uses GNNs to diagnose psychiatric disorders by identifying causally relevant brain regions.
problem Leveraging GNNs for psychiatric diagnosis requires interpretable models to understand decision-making.
method CI-GNN integrates Granger causality into GNNs to identify causally relevant subgraphs.
result CI-GNN provides more reliable and concise explanations of psychiatric diagnoses.
Study identifies diverse health states of opioid users to improve policy.
problem Diverse health states of opioid users lead to ineffective policy interventions.
method Probabilistic topic modeling of medical histories.
result Learned phenotypes predict future opioid use and prescription variability.
Whole MILC learns brain disorder dynamics from unlabeled data.
problem Learning spatio-temporal brain disorder dynamics from unlabeled data.
method Self-supervised pre-training of whole MILC on unlabeled healthy control data.
result Whole MILC outperforms existing methods and provides diagnostic insights.
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
problem Improving mental health conditions through social support analysis.
method Text embedding techniques and RNNs for predicting emotional tone shifts.
result Users' emotional states can improve due to social support, as evidenced by positive comments following negative posts.
Research uses activity analysis to identify mental health symptoms.
problem Identifying mental health symptoms using objective activity metrics.
method Proposes a framework for mHealth monitoring of psychiatric patients based on physical activity time series.
result Identifies distinct behavioural phenotypes and measures for mood assessment.
Framework predicts clinical severity from rs-fMRI data using network optimization.
problem Predicting clinical severity from rs-fMRI data.
method Joint network optimization framework combining sparse subnetworks and linear regression.
result Framework outperforms standard methods and identifies clinically relevant ASD networks.
New algorithm optimizes matrix reordering for noisy disordered matrices.
problem Optimizing matrix reordering for noisy disordered matrices in single-cell biology and metagenomics.
method Proposed a polynomial-time adaptive sorting algorithm to improve upon spectral seriation.
result Our algorithm achieves superior performance compared to existing methods in real datasets.
The study of Morse functions on 3-manifolds and their Reeb graphs.
problem Existence of Morse functions with high Betti numbers on 3-manifolds.
method Analysis of Reeb graphs and Heegaard splittings.
result Calculation of a new invariant for 3-manifold groups.
Study uses machine learning to detect sleep disorders by identifying brain patterns.
problem Detecting sleep disorders through EEG patterns in NREM sleep cycles.
method Feature engineering and machine learning model for predicting Cyclic Alternating Patterns (CAP).
result The model accurately predicts CAP sequences associated with sleep disorders.
Deep-learning model detects ASD from MRI data with high accuracy.
problem Challenges in diagnosing ASD due to subjective behavioral assessments and informant biases.
method Integrates deep-learning and SVM techniques to classify ASD brain scans.
result Highly accurate classification of ASD brain scans from neurotypical scans.
Detecting early signs of mood episodes in bipolar disorder patients.
problem Early identification of mood episodes in bipolar disorder patients for timely treatment.
method Signature-based model derived from stochastic analysis applied to real-time mood data.
result The signature method can identify the onset of mood episodes in bipolar disorder patients.
EEG measures brain activity to diagnose ASD more efficiently.
problem Lack of objective measures for early ASD diagnosis.
method Use EEG to classify ASD using machine learning.
result EEG can be a biomarker for ASD.
Model predicts cannabis use disorder risk for adolescents and young adults.
problem Predicting cannabis use disorder progression in adolescents and young adults.
method Bayesian machine learning model trained on longitudinal data.
result Model provides personalized risk assessment with AUC of 0.68-0.75 and E/O ratio of 0.95-1.
The paper proposes a semi-parametric Bayesian network model using Gaussian Processes and Horseshoe priors.
problem Learning semi-parametric relationships in Expert Bayesian Networks with minimal nonlinear components.
method Uses Gaussian Processes and Horseshoe priors to model relationships, prioritizes modifying expert graphs, and generates diverse graphs.
result Models outperform state-of-the-art semi-parametric Bayesian Network models in synthetic and real-world datasets.
Deep learning predicts opioid use disorder risk in patients.
problem Identifying patients at high risk of opioid use disorder.
method Applied LSTM models to analyze electronic health records of opioid users.
result LSTM model outperformed other methods with F1 score of 0.8023 and AUCROC of 0.9369.
Investigates stochastic networks on disordered lattices, converging to Brownian web in 2D.
problem Stochastic networks on disordered lattices.
method Directed spanning forests on randomly perturbed lattices.
result DSF converges to Brownian web in 2D under diffusive scaling.
Artificial neural networks map quantum phases of disordered topological superconductors.
problem Classifying quantum phases of disordered topological superconductors.
method Supervised artificial neural network trained on ensemble averages of quasiparticle distributions.
result Artificial neural networks can classify quantum phases with high confidence, identifying unknown phases.
The paper studies the graph geometry of finite groups, creating a dataset and analyzing its properties.
problem Understanding how group-theoretic structure is reflected in Cayley graph observables.
method Construction of a dataset of Cayley graphs for groups of order up to 767, analysis of graph statistics, and comparison of model performance.
result Graph statistics are highly informative for predicting group properties, and GNNs can recover substantial structural signal.
ASD-DiagNet uses fMRI data to improve ASD diagnosis accuracy.
problem Difficult diagnosis of Autism Spectrum Disorder (ASD) due to symptom observation.
method Hybrid learning approach combining autoencoder and single layer perceptron.
result Improved classification accuracy up to 80% with 20% increase over state-of-the-art methods.
Machine learning predicts electron correlations in disordered materials.
problem Predicting electron correlations in disordered materials.
method Combining neural networks with many-body techniques to learn electron behavior in the Anderson-Hubbard model.
result A neural network accurately predicts electron correlation properties in disordered systems.