Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/720176
Title: Detecting Meaningful Relationships in Large Data Sets
Authors: Mitzenmacher, Michael;Adams, Ryan;Price, Alkes;Doshi-Velez, Finale
subject: Computer Science;Biology, Genetics;Statistics
Description: As data sets grow and algorithms scale, two questions have become central to data-rich science. The first is the exploration question: how can we avoid only testing hypotheses consistent with current models and instead find new, unanticipated types of relationships that will extend our understanding? The second is the interpretation question: given a robust relationship that has been identified, how can we know whether it proves our hypothesis or whether there are other confounders that are responsible for what we see? In this thesis, we develop a set of tools and theory centered around these two questions. We begin with the exploration question, considering a common scenario in which researchers compute some statistic on every pair of variables in a high-dimensional data set, rank the variable pairs by their scores, and then examine the top of the resulting list. We formulate a theoretical framework for codifying which properties the statistic in question should have in order for this approach to successfully identify new, interesting relationships. We then introduce a suite of tools aimed at achieving these properties, show through theoretical analysis and simulations that they do so, and demonstrate their practical utility by using them to discover robust, novel relationships in a data set of social, political, and economic indicators collected by the World Health Organization about every country in the world. We then turn to the interpretation question, specifically in the context of genome-wide association study (GWAS) data. Interpretation of GWAS data is notoriously difficult because tight correlations between nearby genetic variants, along with the multiple biological functions of each individual variant, mean that identified associations are consistent with many different hypotheses about disease mechanism. We posit a new type of genome-wide pattern that, when present, points to a relatively specific set of biological explanations and is therefore highly scientifically informative. We develop a statistic for confidently identifying this type of pattern, show in simulations that it indeed does so, and apply it to GWAS data spanning tens of diseases and complex traits, identifying both known and novel disease genes across a range of human diseases and traits.
Engineering and Applied Sciences - Computer Science
Measures of dependence; equitability; power; high-dimensional; feature selection; genetics; statistical genetics; gene regulation; functional genomics; transcription factors
URI: http://nrs.harvard.edu/urn-3:HUL.InstRepos:40049997
http://dlib.scu.ac.ir/handle/Hannan/720176
Appears in Collections:Faculty of Arts and Sciences

Files in This Item:
Click on the URI links for accessing contents.
Title: Detecting Meaningful Relationships in Large Data Sets
Authors: Mitzenmacher, Michael;Adams, Ryan;Price, Alkes;Doshi-Velez, Finale
subject: Computer Science;Biology, Genetics;Statistics
Description: As data sets grow and algorithms scale, two questions have become central to data-rich science. The first is the exploration question: how can we avoid only testing hypotheses consistent with current models and instead find new, unanticipated types of relationships that will extend our understanding? The second is the interpretation question: given a robust relationship that has been identified, how can we know whether it proves our hypothesis or whether there are other confounders that are responsible for what we see? In this thesis, we develop a set of tools and theory centered around these two questions. We begin with the exploration question, considering a common scenario in which researchers compute some statistic on every pair of variables in a high-dimensional data set, rank the variable pairs by their scores, and then examine the top of the resulting list. We formulate a theoretical framework for codifying which properties the statistic in question should have in order for this approach to successfully identify new, interesting relationships. We then introduce a suite of tools aimed at achieving these properties, show through theoretical analysis and simulations that they do so, and demonstrate their practical utility by using them to discover robust, novel relationships in a data set of social, political, and economic indicators collected by the World Health Organization about every country in the world. We then turn to the interpretation question, specifically in the context of genome-wide association study (GWAS) data. Interpretation of GWAS data is notoriously difficult because tight correlations between nearby genetic variants, along with the multiple biological functions of each individual variant, mean that identified associations are consistent with many different hypotheses about disease mechanism. We posit a new type of genome-wide pattern that, when present, points to a relatively specific set of biological explanations and is therefore highly scientifically informative. We develop a statistic for confidently identifying this type of pattern, show in simulations that it indeed does so, and apply it to GWAS data spanning tens of diseases and complex traits, identifying both known and novel disease genes across a range of human diseases and traits.
Engineering and Applied Sciences - Computer Science
Measures of dependence; equitability; power; high-dimensional; feature selection; genetics; statistical genetics; gene regulation; functional genomics; transcription factors
URI: http://nrs.harvard.edu/urn-3:HUL.InstRepos:40049997
http://dlib.scu.ac.ir/handle/Hannan/720176
Appears in Collections:Faculty of Arts and Sciences

Files in This Item:
Click on the URI links for accessing contents.
Title: Detecting Meaningful Relationships in Large Data Sets
Authors: Mitzenmacher, Michael;Adams, Ryan;Price, Alkes;Doshi-Velez, Finale
subject: Computer Science;Biology, Genetics;Statistics
Description: As data sets grow and algorithms scale, two questions have become central to data-rich science. The first is the exploration question: how can we avoid only testing hypotheses consistent with current models and instead find new, unanticipated types of relationships that will extend our understanding? The second is the interpretation question: given a robust relationship that has been identified, how can we know whether it proves our hypothesis or whether there are other confounders that are responsible for what we see? In this thesis, we develop a set of tools and theory centered around these two questions. We begin with the exploration question, considering a common scenario in which researchers compute some statistic on every pair of variables in a high-dimensional data set, rank the variable pairs by their scores, and then examine the top of the resulting list. We formulate a theoretical framework for codifying which properties the statistic in question should have in order for this approach to successfully identify new, interesting relationships. We then introduce a suite of tools aimed at achieving these properties, show through theoretical analysis and simulations that they do so, and demonstrate their practical utility by using them to discover robust, novel relationships in a data set of social, political, and economic indicators collected by the World Health Organization about every country in the world. We then turn to the interpretation question, specifically in the context of genome-wide association study (GWAS) data. Interpretation of GWAS data is notoriously difficult because tight correlations between nearby genetic variants, along with the multiple biological functions of each individual variant, mean that identified associations are consistent with many different hypotheses about disease mechanism. We posit a new type of genome-wide pattern that, when present, points to a relatively specific set of biological explanations and is therefore highly scientifically informative. We develop a statistic for confidently identifying this type of pattern, show in simulations that it indeed does so, and apply it to GWAS data spanning tens of diseases and complex traits, identifying both known and novel disease genes across a range of human diseases and traits.
Engineering and Applied Sciences - Computer Science
Measures of dependence; equitability; power; high-dimensional; feature selection; genetics; statistical genetics; gene regulation; functional genomics; transcription factors
URI: http://nrs.harvard.edu/urn-3:HUL.InstRepos:40049997
http://dlib.scu.ac.ir/handle/Hannan/720176
Appears in Collections:Faculty of Arts and Sciences

Files in This Item:
Click on the URI links for accessing contents.