Name
Title | ||
|---|---|---|
Invited Talk: Developing Deep Multi-Source Intelligent Learning That Facilitates The Advancement Of Single Cell Genomics Research |
Abstract | ||
|---|---|---|
Thanks to recent advances in the field of genomics, it is now possible to create a comprehensive atlas of the basic units of life. cells. In this paper, we present a frame work for single cell genomics research which employs several new machine learning models such as convolutional neural networks, deep auto-encoder, recurrent neural networks etc. With these effective learning models on multi-source data, such as biomedical literatures and cell images, we can achieve novel cell types and functional gene sets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIBM.2017.8217749 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | DocType | ISSN |
Single Cell Genomics, High Dimensional and Sparse Matrix (HDSM) Computation, Deep Auto-Encoder (DAE), Intelligent Deep Learning | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Yang | 1 | 36 | 5.82 |
| Sherman Morton Weissman | 2 | 0 | 0.34 |
| Renchu Guan | 3 | 175 | 19.41 |
| Jialing Zhang | 4 | 2 | 1.32 |
| Allon Canaan | 5 | 0 | 0.34 |
| Mary Qu Yang | 6 | 933 | 191.35 |