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Found 24 papers, 7 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.
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.
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.
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.
PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host Classification
Therefore, the usage of only the spike protein, instead of the full genome, provides most of the essential information for performing analyses such as host classification.
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.
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.
Exploring The Potential Of GANs In Biological Sequence Analysis
New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences.
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.
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).
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting.
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.
Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants
We then show that with the appropriate feature selection, we can efficiently and effectively cluster the spike sequences based on the different variants.
Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models
Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity.
Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location
With the rapid spread of COVID-19 worldwide, viral genomic data is available in the order of millions of sequences on public databases such as GISAID.
Benchmarking Machine Learning Robustness in Covid-19 Spike Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 viral genome --- millions of sequences and counting.
Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences
Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc.
Effective and scalable clustering of SARS-CoV-2 sequences
Using a $k$-mer based feature vector generation and efficient feature selection methods, our approach is effective in identifying variants, as well as being efficient and scalable to millions of sequences.
A k-mer Based Approach for SARS-CoV-2 Variant Identification
With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2.
