Biologically informed deep neural networks provide ...

Biologically informed deep neural networks provide quantitative ...

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of recurrent glioblastoma. This study addresses the ...

Biologically-informed deep neural networks provide quantitative ...

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.

Biologically informed deep neural network for prostate cancer ...

... provide insights into the underlying biological processes to prompt functional investigation and therapeutic targeting. Linear models such ...

Biologically-informed deep neural networks provide quantitative ...

We develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules.

A systematic review of biologically-informed deep learning models ...

This paper provides a systematic review focused on omics-based DL models used in cancer biology highlighting the dialogue and convergence ...

Biologically-informed deep neural networks provide quantitative ...

BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to ...

(PDF) Biologically informed deep neural networks provide ...

PDF | On Oct 19, 2024, Hairong Wang and others published Biologically informed deep neural networks provide quantitative assessment of ...

Biologically Informed Neural Networks Predict Drug Responses

In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural networks with a hierarchical structure derived from the Gene Ontology.

(PDF) Biologically-informed deep neural networks provide ...

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogen... ... Intratumoral heterogeneity poses a ...

Biologically informed deep neural network for prostate cancer ...

Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general ...

Biologically Informed Neural Networks Predict Drug Responses

In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural networks with a hierarchical structure derived from the. Gene Ontology.

Abstract 1637: Biologically informed deep neural network for ...

Abstract. Background: Dissection of the molecular properties that distinguish primary and metastatic cancers may reveal new underlying ...

Biologically-informed neural networks guide mechanistic modeling ...

Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the ...

Biologically Informed Deep Neural Network for Prostate Cancer ...

Recorded on November 11, 2021 by the Stanford Center for Artificial Intelligence in Medicine and Imaging as part of the AIMI Journal Club ...

Biological informed graph neural network for tumor mutation burden ...

Linear models, such as logistic regression, tend to offer higher interpretability with less accuracy. Deep neural network methods can handle complex structures ...

A systematic review of biologically-informed deep learning models ...

... Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver ...

A systematic review of biologically-informed deep learning models ...

A critical outlook into contemporary methods for explainability and interpretability used in DL for cancer is provided and a concept of bio-centric ...

Biologically informed ML for cancer discovery | Broad Institute

Haitham Elmarakeby, Van Allen Lab, Dana-Farber Cancer Institute; Broad Institute Meeting: Biologically informed deep neural network for prostate cancer ...

Biologically informed deep neural network for prostate cancer ...

Haitham A. Elmarakeby, Justin Hwang, Rand Arafeh, Jett Crowdis, Sydney Gang, David Liu, Saud H. AlDubayan, Keyan Salari, Steven Kregel, Camden Richter, ...

Biologically informed deep neural networks provide quantitative ...

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma. https://doi.org ...