Discriminative bag-of-cells for imaging-genomics

Chidester, Benjamin; Minh, N.; Ma, Jian

doi:10.1142/9789813235533_0030

Cited by 7 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several papers have been published to address this patient stratification. For example, to classify the subtypes of PR, ER and HER2 with miRNA data ( Sherafatian, 2018 ; Liao et al, 2018 ), the status of the basal subtype through the analysis of images with deep learning algorithms ( Chidester, Do & Ma, 2018 ) and the different subtypes of BRCA by the expression of molecular pathways ( Graudenzi et al, 2017 ), mutation data ( Vural, Wang & Guda, 2016 ) or even the integration of expression and methylation data ( List et al, 2014 ). Cancer subtypes can be studied by unsupervised learning techniques and the integration of different data (expression, methylation, miRNA and CNV) ( Nguyen et al, 2017 ).…”

Section: Machine Learning As a Source Of New Knowledgementioning

confidence: 99%

Machine learning analysis of TCGA cancer data

Liñares-Blanco

Fernández-Lozano

2021

PeerJ Computer Science

View full text Add to dashboard Cite

In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.

show abstract

Section: Machine Learning As a Source Of New Knowledgementioning

confidence: 99%

Machine learning analysis of TCGA cancer data

Liñares-Blanco

Fernández-Lozano

2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…These abundances can be thought of as a continuous bag-of-features, which have been used in image-based models. 6,7 Standard scRNA-seq analysis often identifies cell populations using prior knowledge and marker genes. Similarly, in standard mixture model, the components (e.g.…”

Section: Overviewmentioning

confidence: 99%

CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq

Thomson

Subramaniam

et al. 2021

Preprint

View full text Add to dashboard Cite

Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, highresolution signatures to inform disease prognosis and precision medicine. This paper takes an important first step towards this goal by developing an interpretable machine learning algorithm, CloudPred, to predict individuals' disease phenotypes from their scRNA-seq data. Predicting phenotype from scRNA-seq is challenging for standard machine learning methods-the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous. Typical analysis creates pseudo-bulk samples which are biased toward prior annotations and also lose the single cell resolution. CloudPred addresses these challenges via a novel end-to-end differentiable learning algorithm which is coupled with a biologically informed mixture of cell types model. CloudPred automatically infers the cell subpopulation that are salient for the phenotype without prior annotations. We developed a systematic simulation platform to evaluate the performance of CloudPred and several alternative methods we propose, and find that CloudPred outperforms the alternative methods across several settings. We further validated CloudPred on a real scRNA-seq dataset of 142 lupus patients and controls. CloudPred achieves AUROC of 0.98 while identifying a specific subpopulation of CD4 T cells whose presence is highly indicative of lupus. CloudPred is a powerful new framework to predict clinical phenotypes from scRNA-seq data and to identify relevant cells.

show abstract

“…Other Applications of Representation Learning for Computational Pathology We consider unsupervised representation learning of random patches of digital slides related work: such representations facilitated the achievement of downstream tasks [21,[41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]. However, when supervision is possible, patch-based representation can be achieved in a end-to-end fashion in a multiple-instance-learning framework for a given task [17,47,55,59,[61][62][63][64][65][66][67]. In [16], the authors compared different latent variable models in order to compress WSIs and enable their processing in a single run, and investigated their potential on downstream tasks.…”

Section: Related Workmentioning

confidence: 99%

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology

Lafarge,

Pluim,

Veta

2020

Preprint

View full text Add to dashboard Cite

Unsupervised learning enables modeling complex images without the need for annotations. The representation learned by such models can facilitate any subsequent analysis of large image datasets.However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use. The orientation of imaged objects, for instance, is often arbitrary/irrelevant, thus it can be desired to learn a representation in which the orientation information is disentangled from all other factors.Here, we propose to extend the Variational Auto-Encoder framework by leveraging the group structure of rotationequivariant convolutional networks to learn orientation-wise disentangled generative factors of histopathology images. This way, we enforce a novel partitioning of the latent space, such that oriented and isotropic components get separated.We evaluated this structured representation on a dataset that consists of tissue regions for which nuclear pleomorphism and mitotic activity was assessed by expert pathologists. We show that the trained models efficiently disentangle the inherent orientation information of single-cell images. In comparison to classical approaches, the resulting aggregated representation of sub-populations of cells produces higher performances in subsequent tasks.

show abstract

Discriminative bag-of-cells for imaging-genomics

Cited by 7 publications

References 14 publications

Machine learning analysis of TCGA cancer data

Machine learning analysis of TCGA cancer data

CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology

Contact Info

Product

Resources

About