DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | Genome Biology
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. DeepImpute is a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data.
Circulating Tumor DNA Analyses as Markers of Recurrence Risk and Benefit of Adjuvant Therapy for Stage III Colon Cancer | JAMA Oncology
Can serial analysis of circulating tumor DNA levels provide a real-time indication of adjuvant chemotherapy efficacy in patients with stage III colon cancer? In this multicenter cohort study of 96 patients with stage III colon cancer, a significant difference in 3-year recurrence-free interval was observed in patients with detectable vs undetectable levels of circulating tumor DNA after surgery (47% vs 76%) and after completion of chemotherapy (30% vs 77%).
Restoring ancient text using deep learning: a case study on Greek epigraphy | DeepMind Blog
Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, “inscriptions”, are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents PYTHIA, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle longterm context information, and deal efficiently with missing or corrupted character and word representations.