1 code implementation • 23 Jan 2023 • Piotr Keller, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
How similar are two images?
no code implementations • 23 Aug 2021 • Muhammad Dawood, Kim Branson, Nasir M. Rajpoot, Fayyaz ul Amir Afsar Minhas
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?"
1 code implementation • 18 Aug 2021 • Muhammad Dawood, Kim Branson, Nasir M. Rajpoot, Fayyaz ul Amir Afsar Minhas
Cellular composition prediction, i. e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc.
2 code implementations • 27 Dec 2019 • Fayyaz ul Amir Afsar Minhas, Amina Asif
An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output.
no code implementations • 3 Nov 2019 • Amina Asif, Fayyaz ul Amir Afsar Minhas
We have demonstrated the applicability and effectiveness of the method on synthetically generated data as well as benchmark datasets from UCI machine learning repository for both classification and regression problems.
1 code implementation • 6 May 2019 • Amina Asif, Fayyaz ul Amir Afsar Minhas
Multiple Instance Learning (MIL) is a weak supervision learning paradigm that allows modeling of machine learning problems in which labels are available only for groups of examples called bags.
no code implementations • 16 Nov 2018 • Amina Asif, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informative or privileged features which are available during training only to generate test predictions using input space features which are available both during training and testing.
no code implementations • 22 Nov 2017 • Wajid Arshad Abbasi, Fahad Ul Hassan, Adiba Yaseen, Fayyaz ul Amir Afsar Minhas
Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods.
no code implementations • 21 Nov 2017 • Abdul Hannan Basit, Wajid Arshad Abbasi, Amina Asif, Fayyaz ul Amir Afsar Minhas
We have also developed a web server for our HPI predictor called HoPItor (Host Pathogen Interaction predicTOR) that can predict interactions between human and viral proteins.
no code implementations • 14 Nov 2017 • Amina Asif, Wajid Arshad Abbasi, Farzeen Munir, Asa Ben-Hur, Fayyaz ul Amir Afsar Minhas
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences.