Lakesh Kansakar

TL;DR: The second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function, was conducted by as mentioned in this paper. But the results of the CAFA2 assessment are limited.

TL;DR: The second critical assessment of functional annotation (CAFA) conducted, a timed challenge to assess computational methods that automatically assign protein function, revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies.

TL;DR: The second Critical Assessment of Functional Annotation (CAFA) challenge as mentioned in this paper was the first attempt to assess computational methods that automatically assign protein function. And the results of CAFA2 showed that computational function prediction is improving.

TL;DR: The proposed method for learning how to aggregate AOD estimations from multiple satellite instruments into a more accurate estimation is semi-supervised, as it is able to learn from a small number of labeled data, where labels come from a few accurate and expensive ground-based instruments and a large number of unlabeled data.

TL;DR: A semi-supervised method for learning how to aggregate estimations from multiple satellite instruments into a more accurate estimate of Aerosol Optical Depth, where labels come from a small number of accurate and expensive ground-based instruments is presented.