Algorithm development is the process of designing step-by-step procedures that a computer can follow to solve a problem or carry out a task. A well-designed algorithm specifies the operations and their order so that a given input reliably produces the desired output, ideally in an efficient and reproducible way. Alg…
Algorithm development is the process of designing step-by-step procedures that a computer can follow to solve a problem or carry out a task. A well-designed algorithm specifies the operations and their order so that a given input reliably produces the desired output, ideally in an efficient and reproducible way. Algorithms underpin software engineering, data science, machine learning, and artificial intelligence, and in the life sciences they are essential for analyzing the large and complex datasets generated by genomics, proteomics, and other high-throughput technologies. In Systems Biology, algorithm development supports the modeling of biological networks and the interpretation of large-scale molecular data, helping researchers extract meaningful patterns from measurements that would be impossible to analyze by hand. Research published by an OpenAccessPub journal in this area includes work on ovarian cancer identification based on feature weighting for high-throughput mass spectrometry data, applying a computational method to select informative features and classify samples. Such studies illustrate how purpose-built algorithms turn raw biological measurements into useful predictions. This page gathers peer-reviewed, open-access research relevant to Systems Biology and computational analysis, providing context for the development of algorithms that advance the study of complex biological systems.
2 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 2 articles above have been cited 3 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
A sample of recent works citing this journal's research on Algorithm Development, linking to each citing work.