Tradeoffs in the utility of learned knowledge

Planning systems which make use of domain theories can produce more accurate plans and achieve more goals as the quality of their domain knowledge improves. MTR, a multi-strategy learning system, was designed to learn from system failures and improve domain knowledge used in planning. However, augmented domain knowledge can decrease planning efficiency. We describe how improved knowledge that becomes expensive to use can be approximated to yield calculated tradeoffs in accuracy and efficiency

Challenges of open innovation: the paradox of firm investment in open-source software

Open innovation is a powerful framework encompassing the generation, capture, and employment of intellectual property at the firm level. We identify three fundamental challenges for firms in applying the concept of open innovation: finding creative ways to exploit internal innovation, incorporating external innovation into internal development, and motivating outsiders to supply an ongoing stream of external innovations. This latter challenge involves a paradox, why would firms spend money on R&D efforts if the results of these efforts are available to rival firms? To explore these challenges, we examine the activity of firms in opensource software to support their innovation strategies. Firms involved in open-source software often make investments that will be shared with real and potential rivals. We identify four strategies firms employ – pooled R&D/product development, spinouts, selling complements and attracting donated complements – and discuss how they address the three key challenges of open innovation. We conclude with suggestions for how similar strategies may apply in other industries and offer some possible avenues for future research on open innovation

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly

Severe aortic and arterial aneurysms associated with a TGFBR2 mutation.

BACKGROUND: A 24-year-old man presented with previously diagnosed Marfan\u27s syndrome. Since the age of 9 years, he had undergone eight cardiovascular procedures to treat rapidly progressive aneurysms, dissection and tortuous vascular disease involving the aortic root and arch, the thoracoabdominal aorta, and brachiocephalic, vertebral, internal thoracic and superior mesenteric arteries. Throughout this extensive series of cardiovascular surgical repairs, he recovered without stroke, paraplegia or renal impairment. INVESTIGATIONS: CT scans, arteriogram, genetic mutation screening of transforming growth factor beta receptors 1 and 2. DIAGNOSIS: Diffuse and rapidly progressing vascular disease in a patient who met the diagnostic criteria for Marfan\u27s syndrome, but was later rediagnosed with Loeys-Dietz syndrome. Genetic testing also revealed a de novo mutation in transforming growth factor beta receptor 2. MANAGEMENT: Regular cardiovascular surveillance for aneurysms and dissections, and aggressive surgical treatment of vascular disease

Clinical Rounds With Nutrition Support Services

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68406/2/10.1177_011542659400900273.pd

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

<p>Abstract</p> <p>Background</p> <p>Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases.</p> <p>Results</p> <p>We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases.</p> <p>Conclusions</p> <p>ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at <url>http://cbio.ensmp.fr/prodige</url>.</p

Combinations of newly confirmed Glioma-Associated loci link regions on chromosomes 1 and 9 to increased disease risk

<p>Abstract</p> <p>Background</p> <p>Glioblastoma multiforme (GBM) tends to occur between the ages of 45 and 70. This relatively early onset and its poor prognosis make the impact of GBM on public health far greater than would be suggested by its relatively low frequency. Tissue and blood samples have now been collected for a number of populations, and predisposing alleles have been sought by several different genome-wide association (GWA) studies. The Cancer Genome Atlas (TCGA) at NIH has also collected a considerable amount of data. Because of the low concordance between the results obtained using different populations, only 14 predisposing single nucleotide polymorphism (SNP) candidates in five genomic regions have been replicated in two or more studies. The purpose of this paper is to present an improved approach to biomarker identification.</p> <p>Methods</p> <p>Association analysis was performed with control of population stratifications using the EIGENSTRAT package, under the null hypothesis of "no association between GBM and control SNP genotypes," based on an additive inheritance model. Genes that are strongly correlated with identified SNPs were determined by linkage disequilibrium (LD) or expression quantitative trait locus (eQTL) analysis. A new approach that combines meta-analysis and pathway enrichment analysis identified additional genes.</p> <p>Results</p> <p>(i) A meta-analysis of SNP data from TCGA and the Adult Glioma Study identifies 12 predisposing SNP candidates, seven of which are reported for the first time. These SNPs fall in five genomic regions (5p15.33, 9p21.3, 1p21.2, 3q26.2 and 7p15.3), three of which have not been previously reported. (ii) 25 genes are strongly correlated with these 12 SNPs, eight of which are known to be cancer-associated. (iii) The relative risk for GBM is highest for risk allele combinations on chromosomes 1 and 9. (iv) A combined meta-analysis/pathway analysis identified an additional four genes. All of these have been identified as cancer-related, but have not been previously associated with glioma. (v) Some SNPs that do not occur reproducibly across populations are in reproducible (invariant) pathways, suggesting that they affect the same biological process, and that population discordance can be partially resolved by evaluating processes rather than genes.</p> <p>Conclusion</p> <p>We have uncovered 29 glioma-associated gene candidates; 12 of them known to be cancer related (<it>p </it>= 1. 4 × 10^-6), providing additional statistical support for the relevance of the new candidates. This additional information on risk loci is potentially important for identifying Caucasian individuals at risk for glioma, and for assessing relative risk.</p

A population-specific reference panel empowers genetic studies of Anabaptist populations

Genotype imputation is a powerful strategy for achieving the large sample sizes required for identification of variants underlying complex phenotypes, but imputation of rare variants remains problematic. Genetically isolated populations offer one solution, however population-specific reference panels are needed to assure optimal imputation accuracy and allele frequency estimation. Here we report the Anabaptist Genome Reference Panel (AGRP), the first whole-genome catalogue of variants and phased haplotypes in people of Amish and Mennonite ancestry. Based on high-depth whole-genome sequence (WGS) from 265 individuals, the AGRP contains >12 M high-confidence single nucleotide variants and short indels, of which ~12.5% are novel. These Anabaptist-specific variants were more deleterious than variants with comparable frequencies observed in the 1000 Genomes panel. About 43,000 variants showed enriched allele frequencies in AGRP, consistent with drift. When combined with the 1000 Genomes Project reference panel, the AGRP substantially improved imputation, especially for rarer variants. The AGRP is freely available to researchers through an imputation server

Human genetics in troubled times and places

Abstract The development of human genetics world-wide during the twentieth century, especially across Europe, has occurred against a background of repeated catastrophes, including two world wars and the ideological problems and repression posed by Nazism and Communism. The published scientific literature gives few hints of these problems and there is a danger that they will be forgotten. The First World War was largely indiscriminate in its carnage, but World War 2 and the preceding years of fascism were associated with widespread migration, especially of Jewish workers expelled from Germany, and of their children, a number of whom would become major contributors to the post-war generation of human and medical geneticists in Britain and America. In Germany itself, eminent geneticists were also involved in the abuses carried out in the name of ‘eugenics’ and ‘race biology’. However, geneticists in America, Britain and the rest of Europe were largely responsible for the ideological foundations of these abuses. In the Soviet Union, geneticists and genetics itself became the object of persecution from the 1930s till as late as the mid 1960s, with an almost complete destruction of the field during this time; this extended also to Eastern Europe and China as part of the influence of Russian communism. Most recently, at the end of the twentieth century, China saw a renewal of government sponsored eugenics programmes, now mostly discarded. During the post-world war 2 decades, human genetics research benefited greatly from recognition of the genetic dangers posed by exposure to radiation, following the atomic bomb explosions in Japan, atmospheric testing and successive accidental nuclear disasters in Russia. Documenting and remembering these traumatic events, now largely forgotten among younger workers, is essential if we are to fully understand the history of human genetics and avoid the repetition of similar disasters in the future. The power of modern human genetic and genomic techniques now gives a greater potential for abuse as well as for beneficial use than has ever been seen in the past