Challenges of international human biospecimen biobanking for biomedical research in the era of personalized medicine

2018 ◽  
Vol 08 ◽  
Author(s):  
Olga Potapova
Keyword(s):  
Personalized Medicine ◽  
Biomedical Research ◽  
International Human

scholarly journals CauseMap: Fast inference of causality from complex time series

2015 ◽  
Author(s):  
M. Cyrus Maher ◽  
Ryan D. Hernandez
Keyword(s):  
Time Series ◽  
Personalized Medicine ◽  
Open Source ◽  
Biomedical Research ◽  
Time Series Data ◽  
Series Data ◽  
Dimensional System ◽  
Single Individual ◽  
Causal Relationships ◽  
Causal System

Background: Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results: Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (> ~25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If the reconstructed shadows can predict points from the opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g. a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions: CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

scholarly journals The Challenge of Bringing iPSCs to the Patient

2019 ◽  
Vol 20 (24) ◽  
pp. 6305 ◽  
Author(s):  
María del Carmen Ortuño-Costela ◽  
Victoria Cerrada ◽  
Marta García-López ◽  
M. Esther Gallardo
Keyword(s):  
Stem Cells ◽  
Clinical Trials ◽  
Personalized Medicine ◽  
Induced Pluripotent Stem Cells ◽  
Biomedical Research ◽  
Drug Screening ◽  
Pluripotent Stem Cells ◽  
Promising Area ◽  
Induced Pluripotent

The implementation of induced pluripotent stem cells (iPSCs) in biomedical research more than a decade ago, resulted in a huge leap forward in the highly promising area of personalized medicine. Nowadays, we are even closer to the patient than ever. To date, there are multiple examples of iPSCs applications in clinical trials and drug screening. However, there are still many obstacles to overcome. In this review, we will focus our attention on the advantages of implementing induced pluripotent stem cells technology into the clinics but also commenting on all the current drawbacks that could hinder this promising path towards the patient.

scholarly journals Institutional Profile: Golden Helix Institute of Biomedical Research: interdisciplinary research and educational activities in pharmacogenomics and personalized medicine

2012 ◽  
Vol 13 (4) ◽  
pp. 387-392 ◽  
Author(s):  
Konstantinos Mitropoulos ◽  
Federico Innocenti ◽  
Ron H van Schaik ◽  
Alexander Lezhava ◽  
Giannis Tzimas ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Biomedical Research ◽  
Interdisciplinary Research ◽  
Educational Activities ◽  
Institutional Profile ◽  
And Personalized Medicine

scholarly journals CauseMap: Fast inference of causality from complex time series

2015 ◽  
Author(s):  
M. Cyrus Maher ◽  
Ryan D. Hernandez
Keyword(s):  
Time Series ◽  
Personalized Medicine ◽  
Open Source ◽  
Biomedical Research ◽  
Time Series Data ◽  
Series Data ◽  
Dimensional System ◽  
Single Individual ◽  
Causal Relationships ◽  
Causal System

Background: Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results: Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (> ~25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If the reconstructed shadows can predict points from the opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g. a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions: CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

scholarly journals CauseMap: Fast inference of causality from complex time series

2014 ◽  
Author(s):  
M. Cyrus Maher ◽  
Ryan D. Hernandez
Keyword(s):  
Time Series ◽  
Personalized Medicine ◽  
Open Source ◽  
Biomedical Research ◽  
Time Series Data ◽  
Series Data ◽  
Dimensional System ◽  
Single Individual ◽  
Causal Relationships ◽  
Causal System

Background: Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results: Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (> ~25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If the reconstructed shadows can predict points from the opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g. a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions: CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

Racial Science Now: Histories of Race and Science in the Age of Personalized Medicine

2007 ◽  
Vol 29 (3) ◽  
pp. 157-162 ◽  
Author(s):  
BRIAN BEATON
Keyword(s):  
Personalized Medicine ◽  
Drug Development ◽  
Biomedical Research ◽  
Scientific Research ◽  
Current Debate ◽  
Racial Science ◽  
Critical Attention ◽  
History Of ◽  
History Of Race

The revitalization of race-based science and medicine at the very moment in which the history of “race” in science gained such widespread critical attention forces difficult questions regarding the success of the field. This article outlines the current debate over race in contemporary biomedical research and offers a case study of the RaceSci: A History of Race in Science Web project. One of the earliest electronic resources devoted to the history of race in science, RaceSci was relaunched in early 2007 to expand its focus on the present. To date, historians are generally absent from the academic and public dialogue on the “return” of racial science. In response, RaceSci aims to better engage historians with the raced-based organization of current scientific research, particularly in genetics, drug development, and the rise of so-called “personalized” medicine.

Applications of Scanning Microscopy in Biomedical Research

1968 ◽  
Vol 26 ◽  
pp. 354-355
Author(s):  
T. L. Hayes
Keyword(s):  
Information Content ◽  
Biomedical Research ◽  
Biomedical Applications ◽  
Three Dimensional ◽  
Dental Enamel ◽  
Resolving Power ◽  
Whole Mount ◽  
Two Parameters ◽  
Content Information ◽  
Sectioned Tissue

Biomedical applications of the scanning electron microscope (SEM) have increased in number quite rapidly over the last several years. Studies have been made of cells, whole mount tissue, sectioned tissue, particles, human chromosomes, microorganisms, dental enamel and skeletal material. Many of the advantages of using this instrument for such investigations come from its ability to produce images that are high in information content. Information about the chemical make-up of the specimen, its electrical properties and its three dimensional architecture all may be represented in such images. Since the biological system is distinctive in its chemistry and often spatially scaled to the resolving power of the SEM, these images are particularly useful in biomedical research.In any form of microscopy there are two parameters that together determine the usefulness of the image. One parameter is the size of the volume being studied or resolving power of the instrument and the other is the amount of information about this volume that is displayed in the image. Both parameters are important in describing the performance of a microscope. The light microscope image, for example, is rich in information content (chemical, spatial, living specimen, etc.) but is very limited in resolving power.

Ultrastructure and analytical microprobe analysis of simian lung mite pigments

1986 ◽  
Vol 44 ◽  
pp. 324-325
Author(s):  
R. W. Cole ◽  
J. C. Kim
Keyword(s):  
Biomedical Research ◽  
Tissue Damage ◽  
Microprobe Analysis ◽  
Old World Monkeys ◽  
Old World ◽  
Experimental Animals ◽  
The Past ◽  
Lung Mite ◽  
Mite Infestations ◽  
Cardiopulmonary System

In recent years, non-human primates have become indispensable as experimental animals in many fields of biomedical research. Pharmaceutical and related industries alone use about 2000,000 primates a year. Respiratory mite infestations in lungs of old world monkeys are of particular concern because the resulting tissue damage can directly effect experimental results, especially in those studies involving the cardiopulmonary system. There has been increasing documentation of primate parasitology in the past twenty years.

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