COVID-19

Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenome...

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

The recent advancements in artificial intelligence (AI), with the release of several large models having only query access, make a strong case for explainability of deep models in a post-hoc gradient free manner. In this paper, we propose a framework, named distillation aided explainability (DAX), that attempts to generate a saliency-based explanation in a model agnostic gradient free applicatio...

Active learning for energy-based antibody optimization and enhanced screening

Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-...

Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-...

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 immune responses when presented to autologous T cells b...

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 biomedical data sharing, rankings may be distributed and re...

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 highlighted the need for accurate predictions to improve p...

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

The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolec...

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 dynamically to ensure high Quality of Service (Qo...

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 network data are increasingly under attention. This st...

Predicting the genetic component of gene expression using gene regulatory networks

Gene expression prediction plays a vital role in transcriptome-wide association studies (TWAS), which seek to establish associations between tissue ...

Improving Adversarial Transferability with Neighbourhood Gradient Information

Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to significant performance degradation. In black-box attack...

A Backbone for Long-Horizon Robot Task Understanding

End-to-end robot learning, particularly for long-horizon tasks, often results in unpredictable outcomes and poor generalization. To address these ch...

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 plants with upright leaf angles. It is observed that p...

Refinement of genetic variants needs attention

Variant calling refinement is crucial for distinguishing true genetic variants from technical artifacts in high-throughput sequencing data. Manual r...

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 with clinical risk factor data. While many studie...

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 offers several opportunities for understanding complex...

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 the presence of potentially severe faults. Hence, Fa...

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 ability to unravel the intricacies of gene regulatory p...