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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

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https://doi.org/10.1038/s41591-018-0177-5 > Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous...

gwaybio

Information Constraints on Auto-Encoding Variational Bayes

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>Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult...

stephenra

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

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> Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on...

evancofer

>Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we...

stephenra

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

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> Convolutional neural networks (CNN) have been successfully used to handle three-dimensional data and are a natural match for data with spatial structure such as 3D molecular structures. However, a...

danpol

https://doi.org/10.1021/acs.molpharmaceut.8b00839 > Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require...

agitter

Deep Reinforcement Learning for De-Novo Drug Design

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Published version: http://advances.sciencemag.org/content/4/7/eaap7885 Pre-print: https://arxiv.org/abs/1711.10907 > We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates...

agitter

druGAN: an advanced generative adversarial autoencoder model for denovo generation of new molecules with desired molecular properties in silico

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https://doi.org/10.1021/acs.molpharmaceut.7b00346 > Deep generative models are emerging technologies in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to...

alxndrkalinin

>High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in...

stephenra

> Motivation: The presence of missing values is a frequent problem encountered in genomic data analysis. Lost data can be an obstacle to downstream analyses that require complete data matrices....

evancofer