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Found 8 papers, 2 papers with code
Sequence-Based Nanobody-Antigen Binding Prediction
We curated a comprehensive dataset of Nanobody-Antigen binding and nonbinding data and devised an embedding method based on gapped k-mers to predict binding based only on sequences of nanobody and antigen.
CAMP: A Context-Aware Cricket Players Performance Metric
We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome.
Robust Brain Age Estimation via Regression Models and MRI-derived Features
The proposed BAE framework provides a new approach for estimating brain age, which has important implications for the understanding of neurological disorders and age-related brain changes.
Virus2Vec: Viral Sequence Classification Using Machine Learning
Understanding the host-specificity of different families of viruses sheds light on the origin of, e. g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans.
BioSequence2Vec: Efficient Embedding Generation For Biological Sequences
Kernel-based methods, e. g., SVM, are a proven efficient and useful alternative for several machine learning (ML) tasks such as sequence classification.
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning
This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification.
Efficient Approximate Kernel Based Spike Sequence Classification
More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e. g., Alpha, Beta, Gamma).
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification
Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences.
