arXiv research

Mathematical models help keep vaccine prices low.

UK's rapid vaccine rollout linked to reduced COVID-19 mortality.

Background. In Italy, in recent years, vaccination coverage for key immunizations as MMR has been declining to worryingly low levels. In 2017, the Italian Gov't expanded the number of mandatory immunizations introducing penalties to unvaccinated children's families. During the 2018 general elections campaign, immunizat…

A new method improves estimation of COVID-19 vaccine effectiveness.

Estimates vaccine effectiveness and immune correlates in TND studies with missing data.

Study examines how COVID-19 vaccine companies' popularity affects their stock prices.

In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237 on gender prediction and compared favorably with the state-of-the-art performance in a recent author profiling task. We further leveraged t…

The study uses neural networks to classify and predict coronavirus data.

New method targets vaccines for new variants using Thompson sampling.

Deep neural networks predict B-cell epitopes for SARS-CoV and SARS-CoV-2.

Combines TSP and SC to solve real-world vaccine distribution.

Estimates the effect of time-varying treatments using machine learning.

Study relaxes identification assumptions for natural direct effects in non-randomized settings.

Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from m…

Study assesses weakly-supervised methods for rare outcomes in medical records.

Deep learning predicts RNA degradation from crowdsourced data.

New method removes interference bias in causal models.

The paper explores fairness, welfare, and equity in personalized pricing across various applications.

Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been paid to understanding the fairness implications of different treatment strategies -- that is, how might such strategies distribute the expec…

Estimates Mozambique's population using remote sensing and microcensus data.

Many immunization strategies have been proposed to prevent infectious viruses from spreading through a network. In this study, we propose efficient immunization strategies to prevent a default contagion that might occur in a financial network. An essential difference from the previous studies on immunization strategy i…

Hierarchical NMF organizes COVID-19 literature into a searchable tree.

Generalizes causal inference to high-dimensional outcomes.

Recent studies have demonstrated that machine learning approaches like deep neural networks (DNNs) are easily fooled by adversarial attacks. Subtle and imperceptible perturbations of the data are able to change the result of deep neural networks. Leveraging vulnerable machine learning methods raises many concerns espec…

New method learns diverse protein scaffolds for motif design.

AdvImmune improves certifiable robustness of GNNs against adversarial attacks.

Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalizat…

EGR refines and assesses protein complex structures.

Heterophily affects GNN robustness; separating ego- and neighbor-embeddings improves defense.

Paper proposes Adaptive Pareto Exploration for identifying Pareto optimal arms in multi-objective scenarios.

Study examines how social media sentiment impacts biotech stocks.

In the supervised high dimensional settings with a large number of variables and a low number of individuals, one objective is to select the relevant variables and thus to reduce the dimension. That subspace selection is often managed with supervised tools. However, some data can be missing, compromising the validity o…

New method for fair influence maximization in social networks.

New algorithm optimizes interventions under network interference, scaling to large networks.

Statistical models outperform mechanistic models in short-term COVID-19 incidence forecasts.

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We …

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, res…

Research shows a significant increase in stay lengths for digital nomads in the U.S. during and after the pandemic.

A new method reduces bias in adaptive Lasso estimates.

Emergenet predicts animal influenza strain emergence, outperforming current methods.

Study optimal bidding strategies for digital ads targeting purchases and health campaigns.

Reviews spams are prevalent in e-commerce to manipulate product ranking and customers decisions maliciously. While spams generated based on simple spamming strategy can be detected effectively, hardened spammers can evade regular detectors via more advanced spamming strategies. Previous work gave more attention to evas…

SCIENCE improves prediction intervals for individual causal effects.

Mathematical model audits social media algorithms to prevent bias.

Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.

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…

DeepRC uses Hopfield networks and attention to classify immune repertoires.

Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables to identify cell sub-types, and to determine their relative proportions. Improvements of this technology allow to describe millions of individual cells from a blood samp…