# Nearest Neighbor CCP-Based Molecular Sequence Analysis

arXiv: https://arxiv.org/abs/2409.04922 · HTML: https://mlsanity.com/paper/2409.04922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [Visualizing Large-scale and High-dimensional Data](https://mlsanity.com/paper/1602.00370/) (2016)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022)
- [CCP: Correlated Clustering and Projection for Dimensionality Reduction](https://mlsanity.com/paper/2206.04189/) (2022)
- [T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification](https://mlsanity.com/paper/2304.13145/) (2023)
- [Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE](https://mlsanity.com/paper/2306.13750/) (2023)
- [Hash2Vec, Feature Hashing for Word Embeddings](https://mlsanity.com/paper/1608.08940/) (2016)
- [diBELLA: Distributed Long Read to Long Read Alignment](https://mlsanity.com/paper/2001.10049/) (2020)
- [PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host Classification](https://mlsanity.com/paper/2304.06731/) (2023)

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

- [DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging](https://mlsanity.com/paper/2411.05173/) (2024)

## Abstract (arXiv)

Molecular sequence analysis is crucial for comprehending several biological processes, including protein-protein interactions, functional annotation, and disease classification. The large number of sequences and the inherently complicated nature of protein structures make it challenging to analyze such data. Finding patterns and enhancing subsequent research requires the use of dimensionality reduction and feature selection approaches. Recently, a method called Correlated Clustering and Projection (CCP) has been proposed as an effective method for biological sequencing data. The CCP technique is still costly to compute even though it is effective for sequence visualization. Furthermore, its utility for classifying molecular sequences is still uncertain. To solve these two problems, we present a Nearest Neighbor Correlated Clustering and Projection (CCP-NN)-based technique for efficiently preprocessing molecular sequence data. To group related molecular sequences and produce representative supersequences, CCP makes use of sequence-to-sequence correlations. As opposed to conventional methods, CCP doesn't rely on matrix diagonalization, therefore it can be applied to a range of machine-learning problems. We estimate the density map and compute the correlation using a nearest-neighbor search technique. We performed molecular sequence classification using CCP and CCP-NN representations to assess the efficacy of our proposed approach. Our findings show that CCP-NN considerably improves classification task accuracy as well as significantly outperforms CCP in terms of computational runtime.