Protein (multi-)location prediction: utilizing interdependencies via a generative model.

Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein's function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein.

No longer confidential: estimating the confidence of individual regression predictions.

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is-a fact that often reduces a user's trust into a computational method.

YLoc--an interpretable web server for predicting subcellular localization.

Predicting subcellular localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting subcellular localization.

Going from where to why--interpretable prediction of protein subcellular localization.

Motivation: Protein subcellular localization is pivotal in understanding a protein's function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations.

The role of phosphorylatable serine residues in the DNA-binding domain of Arabidopsis bZIP transcription factors.

Reversible phosphorylation plays a crucial role in regulating the activity of enzymes and other proteins in all living organisms. Particularly, the phosphorylation of transcription factors can modulate their capability to regulate downstream target genes. In plants, basic domain-containing leucine-zipper (bZIP) transcription factors have an important function in the regulation of many developmental processes and adaptive responses to the environment.

SherLoc2: a high-accuracy hybrid method for predicting subcellular localization of proteins.

SherLoc2 is a comprehensive high-accuracy subcellular localization prediction system. It is applicable to animal, fungal, and plant proteins and covers all main eukaryotic subcellular locations. SherLoc2 integrates several sequence-based features as well as text-based features.

MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction.

Background: Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy.

On optimal comparability editing with applications to molecular diagnostics.

Background: The COMPARABILITY EDITING problem appears in the context of hierarchical disease classification based on noisy data. We are given a directed graph G representing hierarchical relationships between patient subgroups. The task is to identify the minimum number of edge insertions or deletions to transform G into a transitive graph, that is, if edges (u, v) and (v, w) are present then edge (u, w) must be present, too.