Novel method classifies HIV patients based on viral load patterns.
problem Limited methods classify patients by viral load patterns, often specific to study design.
method Four features, centroid-based classification algorithm, radial normalization classification.
result Classifies 1,576 HIV positive clinic patients into five viral load patterns.
New method infers viral load from pooled tests.
problem Inefficient viral load inference in pooled testing.
method Message passing algorithm with PCR noise function.
result Accurate viral load inference possible.
Develops a method to analyze SARS-CoV-2 viral load vs. age, finding a significant increase.
problem Determining if SARS-CoV-2 viral load depends on patient age.
method Flexible, non-parametric, hierarchical, Bayesian, causal model.
result Statistically significant increase in viral load with age, but only for one dataset.
The study introduces a high-dimensional tail index model for viral post analysis.
problem Empirical observation of power-law distributions in viral posts.
method High-dimensional tail index regression model, regularized estimator, debiasing for inference.
result Consistency and asymptotic normality of debiased estimator.
Algorithm predicts zoonotic virus emergence with high accuracy.
problem Precise spatio-temporal prediction of zoonotic virus emergence.
method Machine inference using protein sequence databases.
result Quantitative indicators of jump risk from genotypic changes.
The study analyzes meta-data of viral tweets to distinguish fake news from real ones.
problem Identifying political fake news on Twitter using metadata.
method Analysis of 1.5M tweets from the 2016 US election, focusing on viral tweets.
result Significant differences found in followers, URLs, and user verification.
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
Mathematician summarizes protein geometry and mutation effects.
problem Understanding how proteins mutate and their structure-function relationship.
method Mathematical analysis of protein structures and functions, focusing on hydrogen bonds and secondary structure.
result Protein secondary structure regulates mutation by stabilizing or destabilizing regions.
Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.
problem Lack of a risk score to guide ECMO planning for critically ill patients.
method Leverages machine learning to develop the PEER score.
result PEER score predicts mortality and decompensation in patients eligible for ECMO.
Study assesses linear classifiers for virus genotyping and subtyping.
problem Challenges in classifying viral sequences, especially in alignment-free methods.
method Comprehensive evaluation of linear classifiers on HCV genomes, varying parameters and sequence lengths.
result Several classifiers perform well under specific conditions, providing robust assessment.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
Machine learning identifies potential drugs for COVID-19.
problem Finding effective treatments for COVID-19.
method Trained neural network models on virus protein sequences and antiviral drugs.
result Identified potential drug candidates for treating COVID-19.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
The study finds that many social media content cascades recur with bursts of popularity over time.
problem Understanding the recurrence of content cascades on social media.
method Large-scale analysis of cascades on Facebook over extended time periods, using a simple model to simulate recurrence.
result Content virality drives recurrence, but beyond a certain popularity, the rate of recurrence decreases.
Machine learning model diagnoses COVID-19 from routine blood tests.
problem Difficulty in diagnosing COVID-19 due to inconsistent blood parameter changes.
method Constructed a machine learning model using 5,333 patients with various infections and 160 COVID-19-positive patients.
result Cross-validated AUC of 0.97, sensitivity of 81.9%, specificity of 97.9%.
The paper examines how sampling data affects the performance of submodular maximization.
problem Performance loss due to probability sampling in submodular maximization.
method Examines a simple probability sampling method where each data point is selected with probability at least r.
result The sampling gap is both upper and lower bounded by 1/r for policywise submodular utility functions.
The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) often faces more con…
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
problem Designing efficient drugs for novel viral proteins.
method End-to-end framework combining VAE, controlled sampling, and predictors.
result Highly selective and affinity molecules for SARS-CoV-2 targets.
New method targets vaccines for new variants using Thompson sampling.
problem Deciding how to prioritize vaccines for new variants with delayed feedback.
method Partial likelihood Thompson sampling, updating beliefs with partial likelihood.
result Method effectively prioritizes vaccines based on real-world data.
Develops a variational method for ultrametric phylogenetic trees.
problem Accurate and efficient approximation of posterior distributions over trees in Bayesian phylogenetics.
method Variational Bayesian approach based on coalescent times of a single-linkage clustering.
result Achieves competitive accuracy with significantly fewer gradient evaluations.
Electrical engineer's AI journey due to deep learning convergence.
problem Separate development of AI and pattern recognition.
method Exploration of AI's historical trajectory and intersection with electrical engineering.
result Convergence of AI and electrical engineering due to deep learning.
Efficiently models agent dependencies in large social networks.
problem Challenges in incorporating inter-agent dependencies in social reinforcement learning models.
method Clusters users based on payoff and contribution, combines cluster-level policies with personalized agent-level policies, and uses dynamic clustering.
result Our approach learns more accurate policy estimates and converges more quickly than existing methods.
Researchers use clustering to differentiate COVID-19 lung scans.
problem Identifying infected individuals with COVID-19.
method Applied unsupervised clustering techniques using PCA, K-Means++, and RCC.
result KM++ and RCC algorithms improved in clustering COVID-19 lung scans.
Noisy Pooled PCR tests large groups more efficiently.
problem Efficiently test large populations for viral infections.
method Converts group testing to a linear inverse problem with a message passing algorithm.
result Estimates patient illness status with fewer pooled measurements.
Framework predicts mortality risk in MAFLD subjects.
problem Lack of mortality prediction methods for MAFLD subjects.
method Artificial Intelligence-based framework MAFUS using ML algorithms.
result Support Vector Machines (SVM) is the best model for mortality prediction.
Algorithm uncovers two main patterns of online content popularity: bursty and steady.
problem Understanding how online content gains popularity over time.
method Multi-faceted temporal analysis using dipm-SC algorithm.
result Two main patterns of popularity: bursty and steady temporal behaviors.
Paper proposes HGAT for detecting fake news on HIN.
problem Viral spread of fake news causing social harm.
method Hierarchical Graph Attention Network (HGAT) for node representation learning in HIN.
result HGAT outperforms text-based and network-based models.
A dynamic keyword selection model for topic modeling of tweets.
problem Adjusting keywords dynamically to mimic past topics with novelty.
method Generative process selects keywords and documents, trained with variational lower bound and stochastic gradient optimization.
result Keyword-based topic model outperforms a sophisticated baseline model by 67%.
Study shows social influence significantly impacts mobile app adoption in African app stores.
problem Understanding social influence on mobile app choices in app stores.
method Developed a structural model combining diffusion and choice models, used Bayesian estimation.
result Customer choices explained better by offline social influence than online density.
New method for fair influence maximization in social networks.
problem Maximizing influence while ensuring fairness across sensitive attributes.
method Co-training an auto-encoder and discriminator to create fair graph embeddings.
result Our method reduces disparity while maintaining competitive influence maximization performance.
Deep neural networks predict B-cell epitopes for SARS-CoV and SARS-CoV-2.
problem Accurate prediction of B-cell epitopes for vaccine design.
method Deep neural network model with regularization techniques and key features analysis.
result Overall accuracy of 82% in predicting COVID-19 cases.
Method finds optimal binary classification rules under weighted misclassification loss.
problem Optimal binary classification rules for resource-limited settings with cost-sensitive decisions.
method Ensemble learning to derive prediction scores and associated thresholds minimizing weighted misclassification loss.
result Jointly derived score and threshold outperforms methods that derive score first and threshold second.
New algorithm maximizes adoption of multiple products in social networks with limited resources.
problem Maximizing adoption of multiple products in social networks with user attention, budget, and time constraints.
method Formulated as submodular maximization task in continuous-time diffusion model under matroid and multiple knapsack constraints. Proposed randomized algorithm estimating user influence and adaptive threshold greedy algorithm achieving good approximation factor.
result Achieves state-of-the-art effectiveness and scalability in maximizing adoption of multiple products.
Study uses machine learning to detect early COVID-19 from CT images.
problem Early detection of COVID-19 from CT images.
method Machine learning methods applied to patches of CT images, feature extraction (GLCM, LDP, GLRLM, GLSZM, DWT), SVM classification.
result Best classification accuracy of 99.68% with 10-fold cross-validation and GLSZM feature extraction.
Paper tackles reinforcement learning for asynchronous events.
problem Designing online interventions for complex asynchronous environments.
method Deep reinforcement learning of marked temporal point processes.
result Method can find effective interventions for diverse applications.
New Hawkes processes model spatiotemporal events with triggering and clustering.
problem Modeling self-excitatory behavior in spatiotemporal data.
method Developed a new class of spatiotemporal Hawkes processes with efficient inference method.
result Efficiently modeled and inferred spatiotemporal events with triggering and clustering.
Generative Distribution Embeddings learn multiscale representations of distributions.
problem Learning representations of entire distributions for multiscale reasoning.
method Introducing GDE framework that lifts autoencoders to the space of distributions, using conditional generative models and distributional invariance.
result GDEs learn predictive sufficient statistics embedded in Wasserstein space, recovering distances and trajectories for Gaussian and Gaussian mixture distributions.
Deep learning ensembles improve COVID-19 detection from chest X-rays.
problem Detecting COVID-19 from chest X-rays using machine learning.
method Custom CNN and ImageNet models, transfer learning, iterative pruning, ensemble learning.
result 99.01% accuracy in detecting COVID-19 from chest X-rays.
Deep learning models learn chaotic system dynamics from real and simulated data.
problem Training deep learning models for chaotic systems requires big data.
method Jointly train deep neural networks on real and simulated data, enforcing physical laws.
result Proposes knowledge-based deep learning (KDL) for accurate forecasting of chaotic systems.
Framework uses machine learning to distinguish major COVID-19 variants.
problem Discriminate and visualize associations between major COVID-19 variants based on genome sequences.
method Unsupervised machine learning methods, including k-mer analysis, PCA, t-SNE, UMAP, and agglomerative hierarchical clustering.
result Framework effectively distinguishes between major variants and identifies emerging variants.
Study reveals widespread manipulation of meme coins, leading to significant economic losses.
problem Widespread manipulation of meme coins leading to economic losses.
method Cross-chain analysis of 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base.
result 82.8% of high-return tokens show evidence of artificial growth strategies.
Estimates support in distributions with sampling artifacts and errors.
problem Support estimation in the presence of sampling artifacts and errors.
method Regularized weighted Chebyshev approximations with Touchard polynomials, discretized semi-infinte programming.
result Significant improvements over noiseless support estimation methods.
Study develops large margin machine learning models for predicting host-pathogen protein interactions.
problem Identifying host-pathogen interactions to develop new drugs for infectious diseases.
method Large margin machine learning models, specifically SVM with weighted negative sampling and distance-based weight assignment.
result Proposed and validated a new method for predicting host-pathogen protein interactions.
Network metrics form a fundamental part of the network analysis toolbox. Used to quantitatively measure different aspects of the network, these metrics can give insights into the underlying network structure and function. In this work, we connect network metrics to modern probabilistic machine learning. We focus on the…
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
problem Manual, time-consuming pneumonia diagnosis with high inter-observer variability.
method Multi-level ensemble classification system using Bayesian Deep Learning.
result Accuracy of 98.06% in differentiating four pathologies.
Network medicine predicts repurposable drugs for COVID-19.
problem Identifying effective drugs for SARS-CoV-2 infections quickly.
method Artificial intelligence, network diffusion, and network proximity algorithms.
result A multimodal approach combining predictions from multiple algorithms outperforms individual methods.
Proposes a new model to accurately describe random series of events.
problem Accurately and parsimoniously characterize random series of events (RSEs).
method Burstiness Scale (BuSca) model, which views RSEs as a mix of Poissonian and self-exciting processes.
result BuSca accurately describes RSEs in diverse systems, even with only two parameters.
Algorithm estimates clock in network cascades to improve performance.
problem Temporal distortion in cascade observation leads to performance degradation.
method Formulated clock estimation problem, developed FastClock algorithm.
result FastClock algorithm outperforms state-of-the-art in terms of accuracy and speed.