29 research outputs found
Histopathology Slide Indexing and Search: Are We There Yet?
The search and retrieval of digital histopathology slides is an important
task that has yet to be solved. In this case study, we investigate the clinical
readiness of three state-of-the-art histopathology slide search engines,
Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a
qualitative assessment of each model's performance in providing retrieval
results that are reliable and useful to pathologists. We found that all three
image search engines fail to produce consistently reliable results and have
difficulties in capturing granular and subtle features of malignancy, limiting
their diagnostic accuracy. Based on our findings, we also propose a minimal set
of requirements to further advance the development of accurate and reliable
histopathology image search engines for successful clinical adoption
The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
Artificial intelligence represents a new frontier in human medicine that
could save more lives and reduce the costs, thereby increasing accessibility.
As a consequence, the rate of advancement of AI in cancer medical imaging and
more particularly tissue pathology has exploded, opening it to ethical and
technical questions that could impede its adoption into existing systems. In
order to chart the path of AI in its application to cancer tissue imaging, we
review current work and identify how it can improve cancer pathology
diagnostics and research. In this review, we identify 5 core tasks that models
are developed for, including regression, classification, segmentation,
generation, and compression tasks. We address the benefits and challenges that
such methods face, and how they can be adapted for use in cancer prevention and
treatment. The studies looked at in this paper represent the beginning of this
field and future experiments will build on the foundations that we highlight
Reconstruction of ancient microbial genomes from the human gut
Loss of gut microbial diversity in industrial populations is associated with chronic diseases, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces
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Aether: leveraging linear programming for optimal cloud computing in genomics
Abstract Motivation Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results: Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users’ existing HPC pipelines. Availability and implementation Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online
A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.
The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 4 different types of metagenomic data. Using 1,570 samples from 300 infants, we fit 7,865 models for 6 host phenotypes. We demonstrate the dependence of accuracy on algorithm choice and feature definition in microbiome data and propose a framework for building microbiome-derived indicators of host phenotype. We additionally identify biological features predictive of age, sex, breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_predictions/
A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels
Here we present a structural similarity index measure (SSIM) guided
conditional Generative Adversarial Network (cGAN) that generatively performs
image-to-image (i2i) synthesis to generate photo-accurate protein channels in
multiplexed spatial proteomics images. This approach can be utilized to
accurately generate missing spatial proteomics channels that were not included
during experimental data collection either at the bench or the clinic.
Experimental spatial proteomic data from the Human BioMolecular Atlas Program
(HuBMAP) was used to generate spatial representations of missing proteins
through a U-Net based image synthesis pipeline. HuBMAP channels were
hierarchically clustered by the (SSIM) as a heuristic to obtain the minimal set
needed to recapitulate the underlying biology represented by the spatial
landscape of proteins. We subsequently prove that our SSIM based architecture
allows for scaling of generative image synthesis to slides with up to 100
channels, which is better than current state of the art algorithms which are
limited to data with 11 channels. We validate these claims by generating a new
experimental spatial proteomics data set from human lung adenocarcinoma tissue
sections and show that a model trained on HuBMAP can accurately synthesize
channels from our new data set. The ability to recapitulate experimental data
from sparsely stained multiplexed histological slides containing spatial
proteomic will have tremendous impact on medical diagnostics and drug
development, and also raises important questions on the medical ethics of
utilizing data produced by generative image synthesis in the clinical setting.
The algorithm that we present in this paper will allow researchers and
clinicians to save time and costs in proteomics based histological staining
while also increasing the amount of data that they can generate through their
experiments
HiGlass: web-based visual exploration and analysis of genome interaction maps
We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at
http://higlass.io
and is also available as a containerized application that can be run on any platform.National Institutes of Health (U.S.) (U01 CA200059)National Institutes of Health (U.S.) (R00 HG007583)National Institutes of Health (U.S.) (U54 HG007963