BioKlustering: a web app for semi-supervised learning of maximally imbalanced genomic data

TL;DR

BioKlustering is an accessible web application designed for semi-supervised learning on highly imbalanced and unaligned genomic data, enabling phenotype prediction even with minimal or partial labels and small sample sizes.

Contribution

It introduces a novel web tool that handles maximally imbalanced and unaligned genomic data for semi-supervised learning, expanding applicability in biological research.

Findings

Supports maximally imbalanced label settings including single-class observations

Handles unaligned sequences for diverse genomic data

Effective with small sample sizes

Abstract

Summary: Accurate phenotype prediction from genomic sequences is a highly coveted task in biological and medical research. While machine-learning holds the key to accurate prediction in a variety of fields, the complexity of biological data can render many methodologies inapplicable. We introduce BioKlustering, a user-friendly open-source and publicly available web app for unsupervised and semi-supervised learning specialized for cases when sequence alignment and/or experimental phenotyping of all classes are not possible. Among its main advantages, BioKlustering 1) allows for maximally imbalanced settings of partially observed labels including cases when only one class is observed, which is currently prohibited in most semi-supervised methods, 2) takes unaligned sequences as input and thus, allows learning for widely diverse sequences (impossible to align) such as virus and bacteria, 3) is easy to use for anyone with little or no programming expertise, and 4) works well with small sample sizes. Availability and Implementation: BioKlustering (https://bioklustering.wid.wisc.edu) is a freely available web app implemented with Django, a Python-based framework, with all major browsers supported. The web app does not need any installation, and it is publicly available and open-source (https://github.com/solislemuslab/bioklustering).