Search Results for author:
Found 10 papers, 3 papers with code
Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings.
A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis
In this work, we present a novel approach based on the compression-based Model, motivated from \cite{jiang2023low}, which combines the simplicity of basic compression algorithms like Gzip and Bz2, with Normalized Compression Distance (NCD) algorithm to achieve better performance on classification tasks without relying on handcrafted features or pre-trained models.
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification
Sparse coding is a popular technique in machine learning that enables the representation of data with a set of informative features and can capture complex relationships between amino acids and identify subtle patterns in the sequence that might be missed by low-dimensional methods.
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.
ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation
In this work, we propose \emph{ViralVectors}, a compact feature vector generation from virome sequencing data that allows effective downstream analysis.
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.
Anderson Acceleration For Bioinformatics-Based Machine Learning
Anderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms, with applications in various fields including deep learning and optimization.
Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences
We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better.
Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data
As a result, new methods such as Pangolin, which can scale to the millions of samples of SARS-CoV-2 currently available, have appeared.
PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences
In coronaviruses, the surface (S) protein, or spike protein, is an important part of determining host specificity since it is the point of contact between the virus and the host cell membrane.
