Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Ali Foroughi Pour"'
Autor:
Victor G. Wang, Zichao Liu, Jan Martinek, Ali Foroughi pour, Jie Zhou, Hannah Boruchov, Kelly Ray, Karolina Palucka, Jeffrey H. Chuang
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-17 (2024)
Abstract The tumor microenvironment (TME) and the cellular interactions within it can be critical to tumor progression and treatment response. Although technologies to generate multiplex images of the TME are advancing, the many ways in which TME ima
Externí odkaz:
https://doaj.org/article/1f4280d59b514f9a94da98669d1d6645
Publikováno v:
EBioMedicine, Vol 99, Iss , Pp 104908- (2024)
Summary: Background: Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural ne
Externí odkaz:
https://doaj.org/article/9f2037ddf6ee48899c4355a7191d993c
Autor:
Jie Zhou, Ali Foroughi pour, Hany Deirawan, Fayez Daaboul, Thazin Nwe Aung, Rafic Beydoun, Fahad Shabbir Ahmed, Jeffrey H. Chuang
Publikováno v:
EBioMedicine, Vol 94, Iss , Pp 104726- (2023)
Summary: Background: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable pati
Externí odkaz:
https://doaj.org/article/f775fc30c23c44cebfd96f29af3a464f
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNN
Externí odkaz:
https://doaj.org/article/04a03c26e0c54a459e756f99feec7e44
Autor:
Javad Noorbakhsh, Saman Farahmand, Ali Foroughi pour, Sandeep Namburi, Dennis Caruana, David Rimm, Mohammad Soltanieh-ha, Kourosh Zarringhalam, Jeffrey H. Chuang
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-14 (2020)
Histopathological images are a rich but incompletely explored data type for studying cancer. Here the authors show that convolutional neural networks can be systematically applied across cancer types, enabling comparisons to reveal shared spatial beh
Externí odkaz:
https://doaj.org/article/a6994645c7684614b0dfec100ac6d249
Autor:
Ali Foroughi pour, Maciej Pietrzak, Lara E. Sucheston-Campbell, Ezgi Karaesmen, Lori A. Dalton, Grzegorz A. Rempała
Publikováno v:
BMC Medical Genomics, Vol 13, Iss S9, Pp 1-22 (2020)
Abstract Background Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sa
Externí odkaz:
https://doaj.org/article/d6da352384784b5f8411ddfc07adac08
Publikováno v:
BMC Bioinformatics, Vol 21, Iss 1, Pp 1-27 (2020)
Abstract Background Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature
Externí odkaz:
https://doaj.org/article/1ced15e011974644812d310744bd54f6
Autor:
Ali Foroughi Pour, Lori A. Dalton
Publikováno v:
IEEE Access, Vol 7, Pp 127544-127563 (2019)
We present a novel Bayesian validation paradigm with several validation metrics tailored to biomarker discovery, including moments (the mean and variance) of the number of false discoveries, the number of missed discoveries, and the false discovery r
Externí odkaz:
https://doaj.org/article/1efe46b1ac5d4c5cb180ac842d73bdfb
Autor:
Ali Foroughi pour, Lori A. Dalton
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S3, Pp 5-19 (2018)
Abstract Background Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products
Externí odkaz:
https://doaj.org/article/61c0985ca1214ab7a76d4055adb16b97
Autor:
Jill C. Rubinstein, Ali Foroughi Pour, Jie Zhou, Todd B. Sheridan, Brian S. White, Jeffrey H. Chuang
Publikováno v:
Journal of Surgical Oncology. 127:426-433
Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylineosin (HE)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by thes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59d62ae9acd20250d09d224f6aeb11d6
https://doi.org/10.1002/jso.27118
https://doi.org/10.1002/jso.27118