Clustering Protein Sequences Given the Approximation Stability of the Min-Sum Objective Function

TL;DR

This paper introduces an efficient clustering algorithm for protein sequences that operates with limited distance information, leveraging approximation stability of the min-sum objective, and demonstrates its effectiveness against standard methods.

Contribution

The paper presents a novel clustering algorithm that uses few one-versus-all distance queries based on approximation stability assumptions, improving efficiency in protein sequence clustering.

Findings

Algorithm achieves accurate clustering with few queries.

Method outperforms established algorithms in empirical tests.

Effective in limited information settings for protein data.

Abstract

We study the problem of efficiently clustering protein sequences in a limited information setting. We assume that we do not know the distances between the sequences in advance, and must query them during the execution of the algorithm. Our goal is to find an accurate clustering using few queries. We model the problem as a point set $S$ with an unknown metric $d$ on $S$, and assume that we have access to \emph{one versus all} distance queries that given a point $s \in S$ return the distances between $s$ and all other points. Our one versus all query represents an efficient sequence database search program such as BLAST, which compares an input sequence to an entire data set. Given a natural assumption about the approximation stability of the \emph{min-sum} objective function for clustering, we design a provably accurate clustering algorithm that uses few one versus all queries. In our empirical study we show that our method compares favorably to well-established clustering algorithms when we compare computationally derived clusterings to gold-standard manual classifications.