533 citations · 623 across the 14 of their papers we have counts for
14 papers
Integrate Any Omics: Towards genome-wide data integration for patient stratification
Shihao Ma, Andy G. X. Zeng, Benjamin Haibe-Kains +3
High-throughput omics profiling advancements have greatly enhanced cancer patient stratification. However, incomplete data in multi-omics integration presents a significant challen…
On the distribution of cosine similarity with application to biology
Ian Smith, Janosch Ortmann, Farnoosh Abbas-Aghababazadeh +2
Cosine similarity is an established similarity metric for computing associations on vectors, and it is commonly used to identify related samples from biological perturbational data…
Maintaining Stability and Plasticity for Predictive Churn Reduction
George Adam, Benjamin Haibe-Kains, Anna Goldenberg
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when mo…
FastCPH: Efficient Survival Analysis for Neural Networks
Xuelin Yang, Louis Abraham, Sejin Kim +4
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a lin…
Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
Michal Kazmierski, Benjamin Haibe-Kains
Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology. Machine learning (ML) offers a promising way to exp…
Evaluation of statistical approaches for association testing in noisy drug screening data
Petr Smirnov, Ian Smith, Zhaleh Safikhani +9
dentifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to…
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
Michal Kazmierski, Mattea Welch, Sejin Kim +12
Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider rang…
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
Sejin Kim, Michal Kazmierski, Benjamin Haibe-Kains
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been signific…
Learning across label confidence distributions using Filtered Transfer Learning
Seyed Ali Madani Tonekaboni, Andrew E. Brereton, Zhaleh Safikhani +3
Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. Transfer Learning (TL) models have been proposed to resolve th…
CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar +19
Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease a…
The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny +17
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines…
Reducing Adversarial Example Transferability Using Gradient Regularization
George Adam, Petr Smirnov, Benjamin Haibe-Kains +1
Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples…
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
George A. Adam, Petr Smirnov, David Duvenaud +2
Many deep learning algorithms can be easily fooled with simple adversarial examples. To address the limitations of existing defenses, we devised a probabilistic framework that can…
Dr.VAE: Drug Response Variational Autoencoder
Ladislav Rampasek, Daniel Hidru, Petr Smirnov +2
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and it…