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Search Results: 1 - 10 of 326334 matches for " Isaac S Kohane "
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No small matter: qualitatively distinct challenges of pediatric genomic studies
Isaac S Kohane
Genome Medicine , 2011, DOI: 10.1186/gm278
Abstract: There is more here than just the usual underfunding of pediatric projects relative to adult projects, although this certainly may be an important factor [1]. In many ways the barriers mirror some of those that cause under-representation of historically under-represented and underserved minorities in genetic studies as outlined by Francis Collins and colleagues [2]. One important consideration is that it is just much harder to perform genetic studies with children. To start with, there is the matter of consent and assent. Children are not children forever and therefore the parental consent most likely has to be eventually replaced by childhood assent and then full consent as they reach maturity [3]. This already imposes significantly more in terms of overheads for consent management than those incurred by adult prospective studies.Then there is the challenge of obtaining the biological sample. In the judgment of many parents, most children, and a few institutional review boards, the pain and small risks of venipuncture for blood samples outweigh potential benefits, particularly for healthy children. The alternative (for example, obtaining saliva as a source of DNA) often results in suboptimal genomic analyses due to difficulties in obtaining an adequate quantity of sample in young children.In addition, most pediatric care is delivered in small practices, and much of this care and ancillary measurements are not documented in the electronic health records that are mostly found in larger healthcare systems. This makes identification of cases and controls largely an expensive and manual operation. Moreover, the transition to adulthood almost always entails a change in healthcare provider and healthcare delivery system and therefore a discontinuity in record keeping (electronic and/or paper). This results in loss of follow-up information that is essential for genomic studies that address long-term outcomes.Perhaps most challenging is that there is not one population of ch
The twin questions of personalized medicine: who are you and whom do you most resemble?
Isaac S Kohane
Genome Medicine , 2009, DOI: 10.1186/gm4
Abstract: Wikipedia [1] defines personalized medicine as the "use of information and data from a patient's genotype, or level of gene expression to stratify disease, select a medication, provide a therapy, or initiate a preventative measure that is particularly suited to that patient at the time of administration." Other data types are then mentioned as being equally important. A more conventionally authoritative source [2] defines personalized medicine as "The use of genetic susceptibility or pharmacogenetic testing to tailor an individual's preventive care or drug therapy." This apparent primacy of molecular or genetic measurements obscures the fact that they are both only one of many clinical characterizations, and often not the most important one.An alternative definition, arising from more than 50 years of clinical decision science [3], holds that personalized medicine is the practice of clinical decision-making such that the decisions made maximize the outcomes that the patient most cares about and minimizes those that the patient fears the most, on the basis of as much knowledge about the individual's state as is available. To be able to contemplate such a personalized medicine practice, two fundamental questions have to be answered. First, what are the relevant patient characteristics? Second, which clinically distinct subgroup of patients does this patient most resemble?The second question defines the knowledge that we have about how a group of clinically relevant patients are likely to respond to a given intervention or what the accuracy and specificity of a particular test are when applied to that subgroup. The first question is important because the deeper our understanding of who the patient is, the more accurately we can identify which subgroup or subgroups (s)he might belong to, and the more accurately we can assess the level of confidence that the match to that group is relevant. Stated differently, information about the patient is of very limited utility with
GenePING: secure, scalable management of personal genomic data
Ben Adida, Isaac S Kohane
BMC Genomics , 2006, DOI: 10.1186/1471-2164-7-93
Abstract: We present the design and implementation of GenePING, an extension to the PING personal health record system that supports secure storage of large, genome-sized datasets, as well as efficient sharing and retrieval of individual datapoints (e.g. SNPs, rare mutations, gene expression levels). Even with full access to the raw GenePING storage, an attacker cannot discover any stored genomic datapoint on any single patient. Given a large-enough number of patient records, an attacker cannot discover which data corresponds to which patient, or even the size of a given patient's record. The computational overhead of GenePING's security features is a small constant, making the system usable, even in emergency care, on today's hardware.GenePING is the first personal health record management system to support the efficient and secure storage and sharing of large genomic datasets. GenePING is available online at http://ping.chip.org/genepinghtml webcite, licensed under the LGPL.Genomic data are becoming a routine component of clinical diagnosis and treatment. Prospective parents with familial or ethnic history of genetic disease have long been encouraged to undergo genetic counseling, including genotyping for disease alleles such as Tay-Sachs and Cystic Fibrosis [1,2]. Recent research [3], demonstrating that several treatment responses are conditional on genomic profile, promises to usher in the long-awaited era of personalized medicine, all based on the patient's gene sequence or gene expression signature.Clinically pertinent genomic data extends far beyond the patient's somatic genome sequence. Advanced cancer treatment options include genetic testing of cancer cells for specific markers, e.g. estrogen receptors in breast cancer or the Philadelphia chromosome in CML [4]. This type of diagnostic will likely expand into full genomic profiling of cancer cells to help determine appropriate treatment [5]. In addition, much recent literature has uncovered correlations between gene
A SNP-centric database for the investigation of the human genome
Alberto Riva, Isaac S Kohane
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-33
Abstract: SNPper is a web-based application designed to facilitate the retrieval and use of human SNPs for high-throughput research purposes. It provides a rich local database generated by combining SNP data with the Human Genome sequence and with several other data sources, and offers the user a variety of querying, visualization and data export tools. In this paper we describe the structure and organization of the SNPper database, we review the available data export and visualization options, and we describe how the architecture of SNPper and its specialized data structures support high-volume SNP analysis.The rich annotation database and the powerful data manipulation and presentation facilities it offers make SNPper a very useful online resource for SNP research. Its success proves the great need for integrated and interoperable resources in the field of computational biology, and shows how such systems may play a critical role in supporting the large-scale computational analysis of our genome.Single Nucleotide Polymorphisms (SNPs) are an increasingly important tool for the study of the structure and history of our genome [1]. The most common application of SNPs is in association studies, that look for a statistically significant association between SNP alleles and phenotypes (usually diseases), in order to pinpoint candidate causative genes [2]. The power of association studies is a function of the number of SNPs used, and of their quality (defined here as the likelihood of the SNP locus actually being polymorphic in the population under study). For this reason, large databases of well-annotated SNPs have been developed, and are growing at an ever increasing rate.In order to take advantage of the mass of known SNPs, now numbering almost five millions for the human genome alone, researchers need tools to easily and efficiently locate the desired SNPs, to evaluate their annotations, and to export them in formats suitable for subsequent analysis. This, in turn, requires lar
Lower expression of genes near microRNA in C. elegans germline
Hidenori Inaoka, Yutaka Fukuoka, Isaac S Kohane
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-112
Abstract: We analyzed gene expression levels around the 84 of 113 know miRNAs for which there are nearby gene that were measured in the data in two independent C. elegans expression data sets. The expression levels are lower for genes in the vicinity of 59 of 84 (71%) miRNAs as compared to genes far from such miRNAs. Analysis of the genes with lower expression in proximity to the miRNAs reveals increased frequency matching of the 7 nucleotide "seed"s of these miRNAs.We found decreased messenger RNA (mRNA) abundance, localized within a 10 kb of chromosomal distance of some miRNAs, in C. elegans germline. The increased frequency of seed matching near miRNA can explain, in part, the localized effects.MicroRNAs (miRNAs) are short (~22 nt-long) non-protein-coding RNAs. In metazoans, miRNA initially thought to be primarily involved in post-transcriptional control [1] have now been shown to have profound and tissue-specific effects on mRNA transcript abundance across significant fractions of the transcriptome [2]. Concurrently it has been demonstrated that miRNAs have a central role in development and organogenesis [3,4]. In the context of the apparent interactions between miRNA and transcriptional control and mounting evidence for the localized component of transcriptional control [5,6], we performed a genome-wide study of C. elegans to determine a) if there were localized effects of miRNA on transcription and b) if previously identified "seed matching" between miRNA and their gene targets could explain, in part, the observed decrease in expression around some miRNAs.First, the 113 known miRNAs in C. elegans were mapped to the worm genome and genes near each miRNAs were sought. Ninety-six miRNAs were found with at least one gene within 10 kb and 31 miRNA were found within the introns of protein-coding genes. Detailed information about the miRNAs is found in supplemental data. Two experiments of genome-wide expression profiling in C. elegans were analyzed. The first dataset by Kim e
Inter-species differences of co-expression of neighboring genes in eukaryotic genomes
Yutaka Fukuoka, Hidenori Inaoka, Isaac S Kohane
BMC Genomics , 2004, DOI: 10.1186/1471-2164-5-4
Abstract: We analyzed 24 sets of expression data from the six species. Highly co-expressed pairs were sorted into bins of equal sized intervals of CD, and a co-expression rate (CoER) in each bin was calculated. In all datasets, a higher CoER was obtained in a short CD range than a long distance range. These results show that across all studied species, there was a consistent effect of CD on co-expression. However, the results using the ND show more diversity. Intra- and inter-species comparisons of CoER reveal that there are significant differences in the co-expression rates of neighboring genes among the species. A pair-wise BLAST analysis finds 8 – 30 % of the highly co-expressed pairs are duplic ated genes.We confirmed that in the six eukaryotic species, there was a consistent tendency that neighboring genes are likely to be co-expressed. Results of pair-wised BLAST indicate a significant effect of non-duplicated pairs on co-expression. A comparison of CD and ND suggests the dominant effect of CD.As a consequence of DNA sequencing activities, whole-genome sequences for many microbial organisms as well as eukaryotic species are available in publicly accessible databases. DNA microarray technology makes it possible to simultaneously monitor expression patterns of thousand of genes. Expression profiles combined with whole-genome information, especially map information, enable us to investigate a relationship between co-expression of genes and a chromosomal distance (CD).In the pioneering work in this field, Cohen et al. (2000) and Kruglyak and Tang (2000) independently showed that in yeast (Saccharomyces cerevisiae), adjacent pairs of genes show correlated expression [1,2]. In the nematode worm (Caenorhabditis elegans), a study of the relationship between physical distance and expression similarity found many co-expressed pairs of neighboring genes within a distance range of 20 kbp [3]. Clustering of co-expressed genes has been found in humans (Homo sapiens) [4], worm [5] and
Extracting Physician Group Intelligence from Electronic Health Records to Support Evidence Based Medicine
Griffin M. Weber, Isaac S. Kohane
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0064933
Abstract: Evidence-based medicine employs expert opinion and clinical data to inform clinical decision making. The objective of this study is to determine whether it is possible to complement these sources of evidence with information about physician “group intelligence” that exists in electronic health records. Specifically, we measured laboratory test “repeat intervals”, defined as the amount of time it takes for a physician to repeat a test that was previously ordered for the same patient. Our assumption is that while the result of a test is a direct measure of one marker of a patient's health, the physician's decision to order the test is based on multiple factors including past experience, available treatment options, and information about the patient that might not be coded in the electronic health record. By examining repeat intervals in aggregate over large numbers of patients, we show that it is possible to 1) determine what laboratory test results physicians consider “normal”, 2) identify subpopulations of patients that deviate from the norm, and 3) identify situations where laboratory tests are over-ordered. We used laboratory tests as just one example of how physician group intelligence can be used to support evidence based medicine in a way that is automated and continually updated.
Predicting Survival within the Lung Cancer Histopathological Hierarchy Using a Multi-Scale Genomic Model of Development
Hongye Liu ,Alvin T Kho,Isaac S Kohane,Yao Sun
PLOS Medicine , 2006, DOI: 10.1371/journal.pmed.0030232
Abstract: Background The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis—spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance. Methods and Findings Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan–Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis. Conclusions From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.
An Epidemiological Network Model for Disease Outbreak Detection
Ben Y Reis ,Isaac S Kohane,Kenneth D Mandl
PLOS Medicine , 2007, DOI: 10.1371/journal.pmed.0040210
Abstract: Background Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most. Methods and Findings To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach. Conclusions The integrated network models of epidemiological data streams and their
MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes
Voichita D Marinescu, Isaac S Kohane, Alberto Riva
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-79
Abstract: We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method.The search engine, available at http://mapper.chip.org webcite, allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.Identifying the combinatorial logic of transcriptional regulation is key for understanding the mechanisms of development, cell commitment and differentiation and the way in which external and internal signals are converted into specific patterns of gene expression. Transcriptional regulation is accomplished by the coordinated activity of specific regulatory proteins that recognize and bind regulatory elements – short DNA motifs located in the untranscribed regions of the genes [1]. Regulatory elements such as TFBSs, enhancers, and silencers, are commonly located in the promoter region of genes, while others, such as spl
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