# Crowdsourcing for Bioinformatics

arXiv: https://arxiv.org/abs/1302.6667 · HTML: https://mlsanity.com/paper/1302.6667/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2013.

## Built on by (most cited first, 3 of 3)

- [Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective](https://mlsanity.com/paper/2206.09315/) (2022)
- [Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging](https://mlsanity.com/paper/2304.07675/) (2023)
- [AI Competitions and Benchmarks: The life cycle of challenges and benchmarks](https://mlsanity.com/paper/2312.05296/) (2023)

## Abstract (arXiv)

Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base construction and protein structure determination all benefit from human input. In some cases people are needed in vast quantities while in others we need just a few with very rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains. Results: Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume 'microtasks' and systems for solving high-difficulty 'megatasks'. Within these classes, we discuss system types including: volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions.