Introduction
AAanalysis is a Python framework designed for scientists and researchers focusing on interpretable sequence-based protein prediction. Ideal for comparing protein sequences using amino acid scales, this toolkit is versatile enough for any sequence analysis representable by numerical values.
Key Algorithms
CPP: Comparative Physicochemical Profiling, an interpretable feature engineering algorithm comparing two sets of protein sequences to identify the set of most distinctive features.
dPULearn: A deterministic Positive-Unlabeled (PU) Learning algorithm tailored for training on unbalanced and small datasets, enhancing predictive accuracy.
AAclust: A k-optimized clustering wrapper that selects redundancy-reduced sets of numerical scales, such as amino acid scales.
Purpose and Audience
Designed for (computational) biologists, AAanalysis facilitates the analysis and comparison of protein sequences. It enables the discovery of physicochemical signatures that underlie biological interactions and functions.
Overview of Documentation
Start your journey with AAanalysis by visiting our Contributing page for installation instructions and information on contribution. Delve into the core concepts behind our algorithms in the Usage Principles section to understand our design philosophy, equipping you with the mental models necessary for effective application. Our Evaluation strategies are detailed to facilitate an transparent and objective analysis of our algorithms´s outcome.
To get hands-on experience, explore our Tutorials. In addition to our detailed API documentation, we compiled various overview Tables that provide in-depth resources, including our AAontology scale classification and a variety of benchmark protein datasets. Discover the scientific foundation of AAanalysis in the References section.