# BioSequence2Vec: Efficient Embedding Generation For Biological Sequences

arXiv: https://arxiv.org/abs/2304.00291 · HTML: https://mlsanity.com/paper/2304.00291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 1 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [A k-mer Based Approach for SARS-CoV-2 Variant Identification](https://mlsanity.com/paper/2108.03465/) (2021)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [Efficient Approximation Algorithms for String Kernel Based Sequence Classification](https://mlsanity.com/paper/1712.04264/) (2017)
- [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022)
- [Learning Backward Compatible Embeddings](https://mlsanity.com/paper/2206.03040/) (2022)
- [Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal](https://mlsanity.com/paper/2002.00461/) (2020)
- [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://mlsanity.com/paper/2110.09622/) (2021)
- [GaKCo: a Fast GApped k-mer string Kernel using COunting](https://mlsanity.com/paper/1704.07468/) (2017)
- [Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models](https://mlsanity.com/paper/2110.09606/) (2021)
- [Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification](https://mlsanity.com/paper/2207.08898/) (2022)
- [Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences](https://mlsanity.com/paper/2211.09263/) (2022)
- [Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model](https://mlsanity.com/paper/2211.10546/) (2022)
- [Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location](https://mlsanity.com/paper/2110.00809/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis](https://mlsanity.com/paper/2402.08117/) (2024)

## Abstract (arXiv)

Representation learning is an important step in the machine learning pipeline. Given the current biological sequencing data volume, learning an explicit representation is prohibitive due to the dimensionality of the resulting feature vectors. Kernel-based methods, e.g., SVM, are a proven efficient and useful alternative for several machine learning (ML) tasks such as sequence classification. Three challenges with kernel methods are (i) the computation time, (ii) the memory usage (storing an n× n matrix), and (iii) the usage of kernel matrices limited to kernel-based ML methods (difficult to generalize on non-kernel classifiers). While (i) can be solved using approximate methods, challenge (ii) remains for typical kernel methods. Similarly, although non-kernel-based ML methods can be applied to kernel matrices by extracting principal components (kernel PCA), it may result in information loss, while being computationally expensive. In this paper, we propose a general-purpose representation learning approach that embodies kernel methods' qualities while avoiding computation, memory, and generalizability challenges. This involves computing a low-dimensional embedding of each sequence, using random projections of its k-mer frequency vectors, significantly reducing the computation needed to compute the dot product and the memory needed to store the resulting representation. Our proposed fast and alignment-free embedding method can be used as input to any distance (e.g., k nearest neighbors) and non-distance (e.g., decision tree) based ML method for classification and clustering tasks. Using different forms of biological sequences as input, we perform a variety of real-world classification tasks, such as SARS-CoV-2 lineage and gene family classification, outperforming several state-of-the-art embedding and kernel methods in predictive performance.