Recently, World Artificial Intelligence Conference (WAIC) 2020 has conducted a final review of the “WAIC Youth Outstanding Paper” award. An article entitled “Calculable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning”, which was published in Nature Methods in 2019, won the award. The article was written by Prof. Deng Yue from the School of Astronautics, Beihang University and Beijing Big Data Brain Computing Center.
Focusing on the effective analysis of brain dataset, Prof. Deng's research comes up with a new self-supervised recurrent learning approach to explore heterogeneous biological states within large and noisy datasets of single-cell transcriptional profiles by utilizing a recurrent network layer to iteratively perform imputations on zero-valued entries of input scRNA-seq data. The approach makes it possible to process millions of data in only tens of minutes without loss of accuracy, improving the single-cell profiling for brain studies.
Human brains feature a large number of cells and complex functions. The analysis of cognitive models of human brain is important in taking a leap from artificial intelligence to brain-inspired intelligence and creating new-generation brain-inspired intelligent algorithms.
The WAIC is committed to the personal growth of youth talents in the field of artificial intelligence and takes efforts to encourage innovative researches on artificial intelligence. In the WAIC 2020, the “WAIC Youth Outstanding Paper” award was initiated. 10 outstanding papers were selected out of a total of 184 entries from more than 80 universities and institutes at home and abroad and won the award.
Reported by Shi Jieyu
Edited by Jia Aiping
Reviewed by Jin Rong
Translated by Xiong Ting