1 citations · 2 across the 5 of their papers we have counts for
5 papers
BioCOMPASS: Integrating Biomarkers into Transformer-Based Immunotherapy Response Prediction
Sayed Hashim, Frank Soboczenski, Paul Cairns
Datasets used in immunotherapy response prediction are typically small in size, as well as diverse in cancer type, drug administered, and sequencer used. Models often drop in perfo…
Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data
Sayed Hashim, Karthik Nandakumar, Mohammad Yaqub
We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and m…
Survey on Self-supervised Representation Learning Using Image Transformations
Muhammad Ali, Sayed Hashim
Deep neural networks need huge amount of training data, while in real world there is a scarcity of data available for training purposes. To resolve these issues, self-supervised le…
TransformNet: Self-supervised representation learning through predicting geometric transformations
Sayed Hashim, Muhammad Ali
Deep neural networks need a big amount of training data, while in the real world there is a scarcity of data available for training purposes. To resolve this issue unsupervised met…
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
Sayed Hashim, Muhammad Ali, Karthik Nandakumar +1
For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features…