SMISS: a protein function prediction server by integrating multiple sources

SMISS: A protein function prediction server by integrating multiple sources

SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network, and the rules mined from protein function associations. SMISS automatically switc...

Protein Function Prediction by Integrating Multiple Kernels

Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first optimize the weights on individual ker...

JAFA: a Protein Function Prediction Meta-Server

The deluge of genomic data is giving biologists their first post-genomic headache: what do all those genes do? Consequently, there has been a growing effort to computationally determine protein function. Several different methods have been implemented, each with its own strengths and weaknesses. However a biologist trying to predict a proteins function has to know and use many different tools t...

Integrating Information for Protein Function Prediction

LITMUS: Landslide detection by integrating multiple sources

Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to ha...