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Article: Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry.

Lewis, Joshua E / Cooper, Lee A D / Jaye, David L / Pozdnyakova, Olga

bioRxiv : the preprint server for biology

2023

Abstract: Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses ...

Abstract	Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML
Language	English
Publishing date	2023-09-25
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.09.18.558289
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Article ; Online: Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus.

Goldstein, Jeffery A / Nateghi, Ramin / Irmakci, Ismail / Cooper, Lee A D

Placenta

2023 Volume 135, Page(s) 43–50

Abstract: Introduction: Placental parenchymal lesions are commonly encountered and carry significant clinical associations. However, they are frequently missed or misclassified by general practice pathologists. Interpretation of pathology slides has emerged as ... ...

Abstract	Introduction: Placental parenchymal lesions are commonly encountered and carry significant clinical associations. However, they are frequently missed or misclassified by general practice pathologists. Interpretation of pathology slides has emerged as one of the most successful applications of machine learning (ML) in medicine with applications ranging from cancer detection and prognostication to transplant medicine. The goal of this study was to use a whole-slide learning model to identify and classify placental parenchymal lesions including villous infarctions, intervillous thrombi (IVT), and perivillous fibrin deposition (PVFD). Methods: We generated whole slide images from placental discs examined at our institution with infarct, IVT, PVFD, or no macroscopic lesion. Slides were analyzed as a set of overlapping patches. We extracted feature vectors from each patch using a pretrained convolutional neural network (EfficientNetV2L). We trained a model to assign attention to each vector and used the attentions as weights to produce a pooled feature vector. The pooled vector was classified as normal or 1 of 3 lesions using a fully connected network. Patch attention was plotted to highlight informative areas of the slide. Results: Overall balanced accuracy in a test set of held-out slides was 0.86 with receiver-operator characteristic areas under the curve of 0.917-0.993. Cases of PVFD were frequently miscalled as normal or infarcts, the latter possibly due to the perivillous fibrin found at the periphery of infarctions. We used attention maps to further understand some errors, including one most likely due to poor tissue fixation and processing. Discussion: We used a whole-slide learning paradigm to train models to recognize three of the most common placental parenchymal lesions. We used attention maps to gain insight into model function, which differed from intuitive explanations.
MeSH term(s)	Pregnancy ; Female ; Humans ; Placenta/pathology ; Placenta Diseases/pathology ; Thrombosis/pathology ; Machine Learning ; Fibrin ; Infarction/pathology
Chemical Substances	Fibrin (9001-31-4)
Language	English
Publishing date	2023-03-15
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	603951-0
ISSN	1532-3102 ; 0143-4004
ISSN (online)	1532-3102
ISSN	0143-4004
DOI	10.1016/j.placenta.2023.03.003
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Article ; Online: Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry.

Lewis, Joshua E / Cooper, Lee A D / Jaye, David L / Pozdnyakova, Olga

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

2023 Volume 37, Issue 1, Page(s) 100373

Abstract: The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses ... ...

Abstract	The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.
MeSH term(s)	Humans ; Flow Cytometry/methods ; Deep Learning ; Leukemia, Myeloid, Acute/diagnosis ; Leukemia, Myeloid, Acute/genetics ; Leukemia, Myeloid, Acute/metabolism ; Acute Disease ; Cytogenetics
Language	English
Publishing date	2023-11-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	645073-8
ISSN	1530-0285 ; 0893-3952
ISSN (online)	1530-0285
ISSN	0893-3952
DOI	10.1016/j.modpat.2023.100373
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Article ; Online: Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

Tavolara, Thomas E / Niazi, M Khalid Khan / Feldman, Andrew L / Jaye, David L / Flowers, Christopher / Cooper, Lee A D / Gurcan, Metin N

Diagnostic pathology

2024 Volume 19, Issue 1, Page(s) 17

Abstract: Background: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification ...

Abstract	Background: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. Methods: Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. Results: In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). Conclusions: We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.
MeSH term(s)	Humans ; Prognosis ; Deep Learning ; Proto-Oncogene Proteins c-myc/metabolism ; Lymphoma, Large B-Cell, Diffuse ; Proto-Oncogene Proteins c-bcl-2/metabolism ; Antineoplastic Combined Chemotherapy Protocols
Chemical Substances	Proto-Oncogene Proteins c-myc ; Proto-Oncogene Proteins c-bcl-2 ; BCL2 protein, human
Language	English
Publishing date	2024-01-19
Publishing country	England
Document type	Journal Article
ZDB-ID	2210518-9
ISSN	1746-1596 ; 1746-1596
ISSN (online)	1746-1596
ISSN	1746-1596
DOI	10.1186/s13000-023-01425-6
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Article ; Online: GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

Mobadersany, Pooya / Cooper, Lee A D / Goldstein, Jeffery A

Laboratory investigation; a journal of technical methods and pathology

2021 Volume 101, Issue 7, Page(s) 942–951

Abstract: The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and ... ...

Abstract	The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and infant. Placental villi undergo a complex but reproducible sequence of maturation across the third-trimester. Abnormalities of villous maturation are a feature of gestational diabetes and preeclampsia, among others, but there is significant interobserver variability in their diagnosis. Machine learning has emerged as a powerful tool for research in pathology. To capture the volume of data and manage heterogeneity within the placenta, we developed GestaltNet, which emulates human attention to high-yield areas and aggregation across regions. We used this network to estimate the gestational age (GA) of scanned placental slides and compared it to a baseline model lacking the attention and aggregation functions. In the test set, GestaltNet showed a higher r
MeSH term(s)	Deep Learning ; Diabetes, Gestational/pathology ; Female ; Gestational Age ; Histocytochemistry ; Humans ; Image Interpretation, Computer-Assisted/methods ; Placenta/diagnostic imaging ; Placenta/pathology ; Pre-Eclampsia/pathology ; Pregnancy
Language	English
Publishing date	2021-03-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80178-1
ISSN	1530-0307 ; 0023-6837
ISSN (online)	1530-0307
ISSN	0023-6837
DOI	10.1038/s41374-021-00579-5
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Article ; Online: Learning from crowds for automated histopathological image segmentation.

López-Pérez, Miguel / Morales-Álvarez, Pablo / Cooper, Lee A D / Felicelli, Christopher / Goldstein, Jeffery / Vadasz, Brian / Molina, Rafael / Katsaggelos, Aggelos K

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

2024 Volume 112, Page(s) 102327

Abstract: Automated semantic segmentation of histopathological images is an essential task in Computational Pathology (CPATH). The main limitation of Deep Learning (DL) to address this task is the scarcity of expert annotations. Crowdsourcing (CR) has emerged as a ...

Abstract	Automated semantic segmentation of histopathological images is an essential task in Computational Pathology (CPATH). The main limitation of Deep Learning (DL) to address this task is the scarcity of expert annotations. Crowdsourcing (CR) has emerged as a promising solution to reduce the individual (expert) annotation cost by distributing the labeling effort among a group of (non-expert) annotators. Extracting knowledge in this scenario is challenging, as it involves noisy annotations. Jointly learning the underlying (expert) segmentation and the annotators' expertise is currently a commonly used approach. Unfortunately, this approach is frequently carried out by learning a different neural network for each annotator, which scales poorly when the number of annotators grows. For this reason, this strategy cannot be easily applied to real-world CPATH segmentation. This paper proposes a new family of methods for CR segmentation of histopathological images. Our approach consists of two coupled networks: a segmentation network (for learning the expert segmentation) and an annotator network (for learning the annotators' expertise). We propose to estimate the annotators' behavior with only one network that receives the annotator ID as input, achieving scalability on the number of annotators. Our family is composed of three different models for the annotator network. Within this family, we propose a novel modeling of the annotator network in the CR segmentation literature, which considers the global features of the image. We validate our methods on a real-world dataset of Triple Negative Breast Cancer images labeled by several medical students. Our new CR modeling achieves a Dice coefficient of 0.7827, outperforming the well-known STAPLE (0.7039) and being competitive with the supervised method with expert labels (0.7723). The code is available at https://github.com/wizmik12/CRowd_Seg.
MeSH term(s)	Humans ; Neural Networks, Computer ; Image Processing, Computer-Assisted
Language	English
Publishing date	2024-01-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	639451-6
ISSN	1879-0771 ; 0895-6111
ISSN (online)	1879-0771
ISSN	0895-6111
DOI	10.1016/j.compmedimag.2024.102327
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Article: Tissue contamination challenges the credibility of machine learning models in real world digital pathology.

Irmakci, Ismail / Nateghi, Ramin / Zhou, Rujoi / Ross, Ashley E / Yang, Ximing J / Cooper, Lee A D / Goldstein, Jeffery A

medRxiv : the preprint server for health sciences

2023

Abstract: Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, ... ...

Abstract	Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. While human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole slide models. Three operate in placenta for 1) detection of decidual arteriopathy (DA), 2) estimation of gestational age (GA), and 3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in T-distributed Stochastic Neighbor Embedding (tSNE) feature space. Every model showed performance degradation in response to one or more tissue contaminants. DA detection balanced accuracy decreased from 0.74 to 0.69 +/- 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant raised the mean absolute error in estimating gestation age from 1.626 weeks to 2.371 +/ 0.003 weeks. Blood, incorporated into placental sections, induced false negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033mm
Language	English
Publishing date	2023-05-02
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.28.23289287
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Article ; Online: Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology.

Irmakci, Ismail / Nateghi, Ramin / Zhou, Rujoi / Vescovo, Mariavittoria / Saft, Madeline / Ross, Ashley E / Yang, Ximing J / Cooper, Lee A D / Goldstein, Jeffery A

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

2024 Volume 37, Issue 3, Page(s) 100422

Abstract	Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. Although human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models. We trained 4 whole-slide models. Three operate in placenta for the following functions: (1) detection of decidual arteriopathy, (2) estimation of gestational age, and (3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in the t-distributed stochastic neighbor embedding feature space. Every model showed performance degradation in response to one or more tissue contaminants. Decidual arteriopathy detection--balanced accuracy decreased from 0.74 to 0.69 ± 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant, raised the mean absolute error in estimating gestational age from 1.626 weeks to 2.371 ± 0.003 weeks. Blood, incorporated into placental sections, induced false-negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033 mm
MeSH term(s)	Pregnancy ; Male ; Humans ; Female ; Infant, Newborn ; Placenta/pathology ; Machine Learning ; Biopsy, Needle ; Prostate/pathology ; Prostatic Neoplasms/pathology
Language	English
Publishing date	2024-01-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	645073-8
ISSN	1530-0285 ; 0893-3952
ISSN (online)	1530-0285
ISSN	0893-3952
DOI	10.1016/j.modpat.2024.100422
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Article ; Online: Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Farris, Alton B / Vizcarra, Juan / Amgad, Mohamed / Cooper, Lee A D / Gutman, David / Hogan, Julien

Histopathology

2021 Volume 78, Issue 6, Page(s) 791–804

Abstract: Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be ... ...

Abstract	Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.
MeSH term(s)	Allografts/pathology ; Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted ; Kidney/pathology ; Kidney Diseases/pathology ; Kidney Diseases/surgery ; Kidney Transplantation ; Machine Learning
Language	English
Publishing date	2021-03-08
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	131914-0
ISSN	1365-2559 ; 0309-0167
ISSN (online)	1365-2559
ISSN	0309-0167
DOI	10.1111/his.14304
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Article: IMAGING GENOMICS.

Shen, L I / Cooper, Lee A D

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

2017 Volume 22, Page(s) 51–57

Abstract: Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and ... ...

Abstract	Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventive approaches. The Imaging Genomics Session at PSB 2017 aims to encourage discussion on fundamental concepts, new methods and innovative applications in this young and rapidly evolving field.
Language	English
Publishing date	2017
Publishing country	United States
Document type	Journal Article
ISSN	2335-6936
ISSN	2335-6936
DOI	10.1142/9789813207813_0006
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