Cancer survival prediction tools ayres,best survival rescue knife victorinox,emergency survival food supply canada review - Test Out

admin | Category: What Cause Ed | 14.05.2014
Researchers at CNRS, INSERM, Aix-Marseille University and AP-HM have just created a virtual brain that can reconstitute the brain of a person affected by epilepsy for the first time. This video shows Taiwanese beautician Je Lin pulling oil out of clogged pores, a technique she claims is a painless way to treat acne. University of Dundee scientists found a way of measuring the activity of disease-causing mutations in our genes.
If you walked into a cancer clinic ten years ago as a newly diagnosed patient, you'd likely get "standard of care" treatment based on the location of the cancer in your body and its stage. BrunchNews connects you to the latest and trending news from the best news websites around the world. The clinical prediction calculators on this web site, sometimes called "nomograms," can make individualized estimates of prognosis for specific patients.
Disclaimer: The information provided from these clinical prediction calculators should not be used as the sole basis for treatment decisions. ChemoFx® is supported by published peer-reviewed clinical studies, ongoing trials with cooperative groups, and a commitment to additional research. Progression-free interval in ovarian cancer and predictive value of an ex vivo chemoresponse assay. ChemoFx® results were evaluated to determine the association between prediction of response to platinum therapy and overall survival after first-line platinum-based chemotherapy in patients with advanced-stage primary ovarian cancer.
The "threshold" tumor mass of 70 resulted from the fact that the linear fit of actual log-log transfromed data, using two linear relationships, is statistically significantly better than fitting the same data by just one linear function. Worksheet implementation of this personalized HCC survival prognosis, as is described in the paper is avaliable for download. They are designed to be used by physicians and their patients as an aid to help make treatment or surveillance decisions.
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As you'll discover in the charts below, in vitro response correlates with clinical outcomes, so you can have increased confidence that your patients are likely to respond to the treatments you choose.


As we found that all HCC patients have the same mean total disease duration (OVSC~1045 days), survival prognosis for every one of the 15 possible clinical statuses of any patient can be determined simply, as is shown below.
Some of the calculators on this site can estimate prognosis under different possible treatment scenarios and can help determine if a specific patient would benefit from a certain therapy.
Complete mathematical derivation of the determination of the individual times to the onset of hepatocellular cancer growth, using the tumor masses (as determined for respective patients by CT imaging at the "baseline" clinical examination) can be found in the text, linked HERE.
All of the annotations contain hyperlinks that the players can follow for more information.
A search interface allows the player to find genes on the board based on the text in their related annotations.
View this figurePurposeThe purpose of The Cure is to translate the knowledge of the players, along with their ability to process textual information, into a ranked list of genes for use in the development of predictors for breast cancer prognosis.
That intuition may be based on their knowledge, on inferences drawn from gene annotation information, or solely on random speculation.
By aggregating the data collected from many different players across many different games, we tried to eliminate the noise from random clicking and reveal the community consensus with regard to predictive genes.Given a set of recorded games, our gene ranking method is as follows. Some genes appear on multiple boards, multiple players play all boards, and all occurrences are counted. S(g) is the number of times the gene was selected by the human player.We then empirically calculated a one-tailed P value for each value of F given O through simulations of random game play. The P values indicate the chances of observing a value of S or greater given O, assuming that all gene selections were random. Importantly, they allow for comparisons between genes with different numbers of occurrences.
Our simulations stopped at 10,000 iterations per value of O, hence P values below .0001 cannot be reported.
Given any collection of played games, we generate gene rankings based on the estimated P values for each value of F.
Given a set of genes with annotations such as Gene Ontology or Disease Ontology associations, these tests estimate the annotation terms that are overrepresented in the gene set. For example, a typical high-throughput experiment may identify a set of 100 or more active genes in a given condition.


An enrichment analysis can be used to detect if genes related to a functional category, such as the immune response or a disease group such as cancer, are represented in that set of 100 genes more than they would be expected to by chance. By applying enrichment analysis to the gene sets produced by The Cure player community, we can identify whether genes annotated with terms related to breast cancer or other related diseases or processes are being preferentially selected, as we would expect if the players are not choosing randomly.
In principle, it could also unearth interesting new categories of genes selected by the player community.Classifier AccuracyFinally, we measure the value of the gene sets by using them to construct machine-learning-based classifiers that predict 10 year survival.
Given a particular dataset, we eliminate measurements from all genes outside of the set in question, and use the remaining measurements to train and test a predictive model. For the experiments conducted here, we trained support vector machine (SVM) classifiers on gene expression derived datasets, and tested them on independent test sets. We compare the accuracy of the predictors produced using gene sets derived from the game and gene sets used in published survival predictors.ResultsData From One Year of Game PlayThe results presented here are derived from games played between September 7, 2012 and September 5, 2013. In that time, 1077 player accounts were created and a total of 15,669 games were played (including training games).
All collected data except personal player information is available, see Multimedia Appendix 2.Players and Games PlayedBased on the self-reported data collected during registration, the player population was mixed in terms of education, orientation as a biologist, and declared knowledge of cancer. The first coincided with the launch of the game, its presentation at Genome Informatics 2012, and its advertisement as part of the Sage Bionetworks DREAM7 breast cancer prognosis challenge. The second, in May of 2013, is likely related to a posting on the popular website i09, which occurred on May 1, 2013 [21].The total number of games played roughly followed the trends observed for new player registrations. The most games played on a single day were 550, on May 2, immediately after the i09 posting.The number of games played per player followed a power law, consistent with most studies of the quantity of voluntary contributions in open environments (eg, Wikipedia contributions) [22]. This reduces the chances that individual players who repeatedly play the same board, either through cheating or by repeatedly losing to Barney, can introduce bias based on overfitting that board.
The complete set of game-derived gene rankings is available in Multimedia Appendix 3.There was one gene, CASP1, which appeared in all three sets.



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