18,107 research outputs found
Interactions between species introduce spurious associations in microbiome studies
Microbiota contribute to many dimensions of host phenotype, including
disease. To link specific microbes to specific phenotypes, microbiome-wide
association studies compare microbial abundances between two groups of samples.
Abundance differences, however, reflect not only direct associations with the
phenotype, but also indirect effects due to microbial interactions. We found
that microbial interactions could easily generate a large number of spurious
associations that provide no mechanistic insight. Using techniques from
statistical physics, we developed a method to remove indirect associations and
applied it to the largest dataset on pediatric inflammatory bowel disease. Our
method corrected the inflation of p-values in standard association tests and
showed that only a small subset of associations is directly linked to the
disease. Direct associations had a much higher accuracy in separating cases
from controls and pointed to immunomodulation, butyrate production, and the
brain-gut axis as important factors in the inflammatory bowel disease.Comment: 4 main text figures, 15 supplementary figures (i.e appendix) and 6
supplementary tables. Overall 49 pages including reference
Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In this work, we devise a modern, simple and end-to-end fully
automated human spinal cord gray matter segmentation method using Deep
Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate
our method against six independently developed methods on a GM segmentation
challenge and report state-of-the-art results in 8 out of 10 different
evaluation metrics as well as major network parameter reduction when compared
to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure
Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes
Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Breast cancer is the most diagnosed cancer and the most predominant cause of
death in women worldwide. Imaging techniques such as the breast cancer
pathology helps in the diagnosis and monitoring of the disease. However
identification of malignant cells can be challenging given the high
heterogeneity in tissue absorbotion from staining agents. In this work, we
present a novel approach for Invasive Ductal Carcinoma (IDC) cells
discrimination in histopathology slides. We propose a model derived from the
Inception architecture, proposing a multi-level batch normalization module
between each convolutional steps. This module was used as a base block for the
feature extraction in a CNN architecture. We used the open IDC dataset in which
we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus
surpassing recent state of the art classification algorithms tested on this
public dataset.Comment: 4 pages, 5 figure
Cell line name recognition in support of the identification of synthetic lethality in cancer from text
Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus.
Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers
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