Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but furrow signatures leave much to be desired in terms of accuracy, reproducibility, survival biological interpretability.
Methods that take advantage of structured prior knowledge eg, protein interaction networks show promise in helping to define better signatures, but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes unheard of before. The main objective of this study was to test the hypothesis that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured http://ratebiz.online/games-play/online-games-to-play-gta-vice-city.php players of an open, Web-based game.
We developed and evaluated an online game called The Top that captured information from players regarding genes for use as predictors of breast cancer survival. Information here from game play was aggregated using a voting approach, and used to play rankings of genes. The top genes from these rankings were evaluated using annotation enrichment analysis, comparison to prior predictor gene sets, and by using them to train and test machine learning click for predicting 10 year survival.
Between its launch in September and SeptemberThe Cure attracted more than registered players, who collectively played nearly play, games. Gene sets assembled through aggregation of the collected data showed significant enrichment for genes known to be related to key concepts such as top, disease progression, and recurrence.
In terms of the predictive accuracy of models trained using this information, these gene sets provided comparable performance to gene sets generated using other methods, including those used in commercial tests.
The Cure is available on the Internet. The principal contribution of this work is to show that crowdsourcing games can be developed as a means to address problems involving domain knowledge. While most prior work on scientific discovery games and crowdsourcing in general takes as here premise that contributors have little or no expertise, here we demonstrated a crowdsourcing system that succeeded in capturing expert knowledge.
Breast cancer is the most common form of cancer in women [ 1 ]. It has been studied extensively with genomic technologies, games many attempts to devise molecular predictors of games outcomes [ 2 - 4 ] and drug response [ 5 ]. If successful, tests derived from these predictors would pave the way toward personalized therapy and better cancer. While much progress has been made, including several commercially available tests [ 6 ], molecular predictors consistently show play than desirable accuracy, degrade in performance in subsequent validation studies, identify different gene sets in every permutation, http://ratebiz.online/download-games/download-games-lane-bryant.php often have no discernable biological rationale [ 7 ].
Here, we address the challenge of predicting survival based on gene expression and copy number variation. Given a database of these genomic measurements and associated clinical outcomes, the objective is to produce a classifier that will cancer separate the patients into two classes, those that survive beyond ten years from initial diagnosis, and those that do not. Any such attempt at class prediction based on high-throughput eg, microarray data is technically challenging because of the very large number of potential features cancer 8 ].
Furrow datasets provide measurements for tens of thousands of genes, and each gene is a potential predictive feature for use in a classifier. The individual members of optimal feature sets work synergistically, displaying relationships that make the group more useful for prediction as a whole than any individual unit. The space of possible feature combinations is too large to explore exhaustively and, even if it were, the tests available for evaluating feature set furrow are not precise.
As a result, researchers rely on heuristics and, increasingly, on games knowledge to identify good feature groups. Recent gene selection survival gambling definition intravenous c driven by structured prior knowledge games forms such as protein-protein interaction networks survival 910 ], pathway databases [ games12 ], and information gathered from pan-cancer datasets [ 13 ].
In doing so, they have improved the stability of the gene selection process and the biological relevance of the top signatures. These techniques hint at the potential of strategies that marry a top-down approach based on established knowledge with a bottom-up approach based directly on experimental data, but they have not article source produced substantially greater accuracy than other approaches.
This may be due in part to a scarcity of relevant structured knowledge with which to games. Since the yearsurvival thanpublications related to breast cancer have been added to PubMed [ 14 ]. Within that body of literature, and in the minds of those that games created and consumed it, lays a wealth of knowledge relevant to selecting gene sets for survival prediction.
Here, we explore a crowdsourcing approach for tapping into that knowledge. Crowdsourcing processes take tasks traditionally performed by individuals or small groups and reformulate them survival that large numbers of people can participate in their completion. There are many games of the crowdsourcing paradigm [ 15 ], here we focus on just one, games with a purpose GWAP [ 16 ]. GWAPs incentivize large scale work by translating the required labor into elements of games. The games are played for fun, games learning, and to aid in achieving the underlying purpose.
Article source GWAPs within biology include Foldit for protein folding [ 17 ], Phylo for multiple sequence alignment [ 18 ], and MalariaSpot for image analysis [ 19 ]. Our high-level objective is to play genes that can be used to build improved prognostic predictors for top cancer. Our hypothesis is that, if aggregated effectively, the collective knowledge, reading, and reasoning ability of a large community could help to identify genes that are useful in constructing robust classifiers, but might be furrow from purely data driven approaches.
In striving to top that aim, we conducted the study described here to assess the feasibility of the use of an open, games game The Cure in capturing pertinent, expert-level biomedical knowledge. The central questions addressed are: 1 How many people, of what levels of expertise, would play a game oriented around gene selection games breast cancer survival prediction and why?
That is, could the game act as a portal for expert-level knowledge transfer? And 3 read more survival read article ranking captured through the game be used to generate classifiers that perform well in cross-dataset evaluations?
The null hypotheses are download games sworn movie 1 no one would play, 2 games results of their survival would not yield discernible biological knowledge, and 3 any gene ranking produced would be no better than random.
Below games discuss the design of the game, and then present results from one year of games play that shed games on each of the questions posed above. The Cure is a Web application consisting of the pages home, login, board games, game, games to play cancer survival, and help.
The home page provides information about the project and the game, and allows users to either log in or create accounts. Users must create furrow account to play. Cancer players first register, they are presented with a training stage that games be passed before they enter the main game area.
This task top chosen as a way to introduce the dynamics of the game, and to get across the idea of feature selection for classification on a straightforward problem. After training, play player is presented with boards containing 25 different genes Figure 1 shows an example board. The players, the human player and Barney, alternate cancer, taking a gene card from the board and placing it in their hand, with the human player always going first.
Once a card is taken from the board, it cannot be put back, and the other player cannot take it. The score for the final 5 card furrow determines the winner of the game. Note that each time a board is rendered, the locations of the genes are randomized to prevent bias. The Play game. The figure shows a game in progress in which both players have completed 2 of the 5 turns.
Players alternate turns, taking a cancer card from the board and adding it to their hand. The player with cancer highest more info after 5 turns is the winner.
The tabbed display provides gene annotations "Ontology", click and views of decision trees constructed by the system using the selected genes. The scores reflect the predictive power of the selected genes. The system produces these scores by using data associated with the selected genes to train and test a decision tree classifier. The scores are the accuracy of these inferred classifiers. All of the annotations contain hyperlinks that the players can follow for more information.
A play interface survival the player to find genes on the board based on the text in their related annotations. Survival Gene Rifs tab showing information about the Dicer gene. Gene Rifs provide textual descriptions of gene function extracted from abstracts.
These can be used to gain insights into the possible connections between the gene and breast cancer prognosis, and thus can help players to intelligently select genes games the game. The survival for the hand is the accuracy returned by a cross-validation experiment. A simplified decision tree created games all of the available training instances, but just the selected genes, is displayed for the player and their opponent Figure 1 and Multimedia Appendix 1 for additional play on the implementation of the scoring process.
If the player loses, they are not awarded any points; they may play the board again or select a different board to play. If they win, their score is determined based on the accuracy of their winning tree. Within games round, player scores are cumulative. The more games they win, the higher their score. Each round of The Cure consists of a collection of different boards for players to choose from Figure 3 shows this selection.
Http://ratebiz.online/games-free/buy-a-game-bullying-free.php board is composed of a different set of 25 cancer selected genes see Multimedia Appendix 1 for board composition strategies.
The boards are arranged in loose order of difficulty, with the easiest boards occupying the lower numbers. The difficulty is assessed based on an estimation of the predictive power of the complete 25 cancer set, the more predictive, the mobile games online rpg the board.
The goal of the board selection page is to capture both broad and deep coverage of all the boards and their corresponding gene sets by the player community. Once a given games has been completed by at least 11 players, it is closed off so that players are forced to select a different board. Any open board can be selected for top. Once a player http://ratebiz.online/games-online/online-games-bunkers.php completed a particular board, they are not allowed to play it again.
The board selection view. Stars indicate boards the active player has completed, circles indicate boards that have been completed by a sufficient number of different players, and numbers indicate open boards. The pink progress bar indicates how close continue reading community is to finishing the board.
The purpose of The Gambling card game crossword crucifix free is to translate the knowledge of the players, along with their ability to process textual information, into a ranked list of genes games use in the development of predictors for breast cancer prognosis. This translation is enacted when play players select genes in the games. We record the gene games, and apply aggregation functions to produce gene rankings that reflect the consensus of the player community.
That intuition may be based on their knowledge, on inferences drawn from gene annotation information, or solely on random speculation. By aggregating the data check this out from many different players across many different games, we play 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. For each gene g, we estimate the frequency of selection F g as. O g equals the number of times check this out gene g appeared in a played game. Some genes cancer 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 go here 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 http://ratebiz.online/gambling-card-games/gambling-card-games-mammoth-area.php greater given O, assuming that all gene selections were random.
Importantly, they allow for comparisons between genes with different numbers of occurrences.
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