COVID-19

Visualization of Age Distributions as Elements of Medical Data-Stories

In various fields, including medicine, age distributions are crucial. Despite widespread media cov...

Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professiona...

Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach

The recent advancements in artificial intelligence (AI), with the release of several large models ...

Active learning for energy-based antibody optimization and enhanced screening

Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeuti...

Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally...

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immun...

Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding

Rank aggregation combines multiple ranked lists into a consensus ranking. In fields like biomedica...

AI reverse-engineers antibodies.

A tailored machine learning approach for mortality prediction in severe COVID-19 treated with glucocorticoids.

The impact of severe COVID-19 pneumonia on healthcare systems highligh...

Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions

The transformer architecture has revolutionized bioinformatics and driven progress in the understa...

Residual-based Adaptive Huber Loss (RAHL) -- Design of an improved Huber loss for CQI prediction in 5G networks

The Channel Quality Indicator (CQI) plays a pivotal role in 5G networks, optimizing infrastructure...

Deep Learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data.

Due to the importance of COVID-19 control, innovative methods for predicting cases using social netw...

Predicting the genetic component of gene expression using gene regulatory networks

Gene expression prediction plays a vital role in transcriptome-wide association studies (TWAS), wh...

Improving Adversarial Transferability with Neighbourhood Gradient Information

Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to signif...

A Backbone for Long-Horizon Robot Task Understanding

End-to-end robot learning, particularly for long-horizon tasks, often results in unpredictable out...

Leaf Angle Estimation using Mask R-CNN and LETR Vision Transformer

Modern day studies show a high degree of correlation between high yielding crop varieties and plan...

Refinement of genetic variants needs attention

Variant calling refinement is crucial for distinguishing true genetic variants from technical arti...

Are gene-by-environment interactions leveraged in multi-modality neural networks for breast cancer prediction?

Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined...

A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning

The integration of knowledge graphs and graph machine learning (GML) in genomic data analysis offe...

High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models

Ensuring fault tolerance of Highly Automated Vehicles (HAVs) is crucial for their safety due to th...

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our abili...