Search Results for author:
Found 4 papers, 1 papers with code
Novel Pipeline for Diagnosing Acute Lymphoblastic Leukemia Sensitive to Related Biomarkers
We introduced a novel pipeline for diagnosing ALL that approximates the process used by hematologists, is sensitive to disease biomarkers, and achieves an accuracy of 96. 15%, an F1-score of 94. 24%, a sensitivity of 97. 56%, and a specificity of 90. 91% on ALL IDB 1.
A Gradient-Based Approach to Neural Networks Structure Learning
Designing the architecture of deep neural networks (DNNs) requires human expertise and is a cumbersome task.
DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome
We employed this architecture on mRNA transcription profiles of 10787 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues.
Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks
Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes.
