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Article ; Online: Letter to the editor: on the stability and ranking of predictors from random forest variable importance measures.

Nicodemus, Kristin K

2011 Volume 12, Issue 4, Page(s) 369–373

Abstract: A recent study examined the stability of rankings from random forests using two variable importance measures (mean decrease accuracy (MDA) and mean decrease Gini (MDG)) and concluded that rankings based on the MDG were more robust than MDA. However, ... ...

Abstract	A recent study examined the stability of rankings from random forests using two variable importance measures (mean decrease accuracy (MDA) and mean decrease Gini (MDG)) and concluded that rankings based on the MDG were more robust than MDA. However, studies examining data-specific characteristics on ranking stability have been few. Rankings based on the MDG measure showed sensitivity to within-predictor correlation and differences in category frequencies, even when the number of categories was held constant, and thus may produce spurious results. The MDA measure was robust to these data characteristics. Further, under strong within-predictor correlation, MDG rankings were less stable than those using MDA.
MeSH term(s)	Artificial Intelligence
Language	English
Publishing date	2011-04-15
Publishing country	England
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbr016
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Article ; Online: A review of neuroeconomic gameplay in psychiatric disorders.

Robson, Siân E / Repetto, Linda / Gountouna, Viktoria-Eleni / Nicodemus, Kristin K

Molecular psychiatry

2019 Volume 25, Issue 1, Page(s) 67–81

Abstract: Abnormalities in social interaction are a common feature of several psychiatric disorders, aligning with the recent move towards using Research Domain Criteria (RDoC) to describe disorders in terms of observable behaviours rather than using specific ... ...

Abstract	Abnormalities in social interaction are a common feature of several psychiatric disorders, aligning with the recent move towards using Research Domain Criteria (RDoC) to describe disorders in terms of observable behaviours rather than using specific diagnoses. Neuroeconomic games are an effective measure of social decision-making that can be adapted for use in neuroimaging, allowing investigation of the biological basis for behaviour. This review summarises findings of neuroeconomic gameplay studies in Axis 1 psychiatric disorders and advocates the use of these games as measures of the RDoC Affiliation and Attachment, Reward Responsiveness, Reward Learning and Reward Valuation constructs. Although research on neuroeconomic gameplay is in its infancy, consistencies have been observed across disorders, particularly in terms of impaired integration of social and cognitive information, avoidance of negative social interactions and reduced reward sensitivity, as well as a reduction in activity in brain regions associated with processing and responding to social information.
MeSH term(s)	Brain/metabolism ; Decision Making/physiology ; Game Theory ; Games, Experimental ; Humans ; Interpersonal Relations ; Learning ; Mental Disorders/psychology ; Motivation ; Neuroimaging/methods ; Reward
Language	English
Publishing date	2019-04-30
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1330655-8
ISSN	1476-5578 ; 1359-4184
ISSN (online)	1476-5578
ISSN	1359-4184
DOI	10.1038/s41380-019-0405-5
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Article ; Online: Catmap: case-control and TDT meta-analysis package.

Nicodemus, Kristin K

BMC bioinformatics

2008 Volume 9, Page(s) 130

Abstract: Background: Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect ... ...

Abstract	Background: Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect sizes are modest. Although many software options are available for meta-analysis of genetic case-control data, no currently available software implements the method described by Kazeem and Farrall (2005), which combines data from independent family-based and case-control studies. Results: I introduce the package catmap for the R statistical computing environment that implements fixed- and random-effects pooled estimates for case-control and transmission disequilibrium methods, allowing for the use of genetic association data across study types. In addition, catmap may be used to create forest and funnel plots and to perform sensitivity analysis and cumulative meta-analysis. catmap is available from the Comprehensive R Archive Network http://www.r-project.org. Conclusion: catmap allows researchers to synthesize data to assess evidence for association in studies of genetic polymorphisms, facilitating the use of pooled data analyses which may increase power to detect moderate genetic associations.
MeSH term(s)	Algorithms ; Biometry/methods ; Case-Control Studies ; Data Interpretation, Statistical ; Epidemiologic Methods ; Genetic Predisposition to Disease/epidemiology ; Genetic Predisposition to Disease/genetics ; Humans ; Meta-Analysis as Topic ; Software
Language	English
Publishing date	2008-02-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/1471-2105-9-130
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Article ; Online: The role of polygenic risk score gene-set analysis in the context of the omnigenic model of schizophrenia.

Rammos, Alexandros / Gonzalez, Lara A Neira / Weinberger, Daniel R / Mitchell, Kevin J / Nicodemus, Kristin K

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology

2019 Volume 44, Issue 9, Page(s) 1562–1569

Abstract: A recent development in the genetic architecture of schizophrenia suggested that an omnigenic model may underlie the risk for this disorder. The aim of our study was to use polygenic profile scoring to quantitatively assess whether a number of ... ...

Abstract	A recent development in the genetic architecture of schizophrenia suggested that an omnigenic model may underlie the risk for this disorder. The aim of our study was to use polygenic profile scoring to quantitatively assess whether a number of experimentally derived sets would contribute to the disorder above and beyond the omnigenic effect. Using the PGC2 secondary analysis schizophrenia case-control cohort (N = 29,125 cases and 34,836 controls), a robust polygenic signal was observed from gene sets based on TCF4, FMR1, upregulation from MIR137 and downregulation from CHD8. Additional analyses revealed a constant floor effect in the amount of variance explained, consistent with the omnigenic model. Thus, we report that putative core gene sets showed a significant effect above and beyond the floor effect that might be linked with the underlying omnigenic background. In addition, we demonstrate a method to quantify the contribution of specific gene sets within the omnigenic context.
MeSH term(s)	Case-Control Studies ; DNA-Binding Proteins/genetics ; Fragile X Mental Retardation Protein/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; MicroRNAs/genetics ; Models, Genetic ; Multifactorial Inheritance ; Polymorphism, Single Nucleotide ; Risk Assessment ; Schizophrenia/genetics ; Transcription Factor 4/genetics ; Transcription Factors/genetics
Chemical Substances	CHD8 protein, human ; DNA-Binding Proteins ; FMR1 protein, human ; MIRN137 microRNA, human ; MicroRNAs ; TCF4 protein, human ; Transcription Factor 4 ; Transcription Factors ; Fragile X Mental Retardation Protein (139135-51-6)
Language	English
Publishing date	2019-05-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	639471-1
ISSN	1740-634X ; 0893-133X
ISSN (online)	1740-634X
ISSN	0893-133X
DOI	10.1038/s41386-019-0410-z
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Article ; Online: A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Quinn, Thomas P / Hess, Jonathan L / Marshe, Victoria S / Barnett, Michelle M / Hauschild, Anne-Christin / Maciukiewicz, Malgorzata / Elsheikh, Samar S M / Men, Xiaoyu / Schwarz, Emanuel / Trakadis, Yannis J / Breen, Michael S / Barnett, Eric J / Zhang-James, Yanli / Ahsen, Mehmet Eren / Cao, Han / Chen, Junfang / Hou, Jiahui / Salekin, Asif / Lin, Ping-I /

Nicodemus, Kristin K / Meyer-Lindenberg, Andreas / Bichindaritz, Isabelle / Faraone, Stephen V / Cairns, Murray J / Pandey, Gaurav / Müller, Daniel J / Glatt, Stephen J

Molecular psychiatry

2024

Abstract: Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of ... ...

Abstract	Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
Language	English
Publishing date	2024-01-04
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1330655-8
ISSN	1476-5578 ; 1359-4184
ISSN (online)	1476-5578
ISSN	1359-4184
DOI	10.1038/s41380-023-02334-2
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Article ; Online: catmap

Nicodemus Kristin K

BMC Bioinformatics, Vol 9, Iss 1, p

C ase-control A nd T DT M eta- A nalysis P ackage

2008 Volume 130

Abstract: Abstract Background Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when ... ...

Abstract	Abstract Background Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect sizes are modest. Although many software options are available for meta-analysis of genetic case-control data, no currently available software implements the method described by Kazeem and Farrall (2005), which combines data from independent family-based and case-control studies. Results I introduce the package catmap for the R statistical computing environment that implements fixed- and random-effects pooled estimates for case-control and transmission disequilibrium methods, allowing for the use of genetic association data across study types. In addition, catmap may be used to create forest and funnel plots and to perform sensitivity analysis and cumulative meta-analysis. catmap is available from the Comprehensive R Archive Network http://www.r-project.org . Conclusion catmap allows researchers to synthesize data to assess evidence for association in studies of genetic polymorphisms, facilitating the use of pooled data analyses which may increase power to detect moderate genetic associations.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2008-02-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
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Article ; Online: Predictor correlation impacts machine learning algorithms: implications for genomic studies.

Nicodemus, Kristin K / Malley, James D

Bioinformatics (Oxford, England)

2009 Volume 25, Issue 15, Page(s) 1884–1890

Abstract: Motivation: The advent of high-throughput genomics has produced studies with large numbers of predictors (e.g. genome-wide association, microarray studies). Machine learning algorithms (MLAs) are a computationally efficient way to identify phenotype- ... ...

Abstract	Motivation: The advent of high-throughput genomics has produced studies with large numbers of predictors (e.g. genome-wide association, microarray studies). Machine learning algorithms (MLAs) are a computationally efficient way to identify phenotype-associated variables in high-dimensional data. There are important results from mathematical theory and numerous practical results documenting their value. One attractive feature of MLAs is that many operate in a fully multivariate environment, allowing for small-importance variables to be included when they act cooperatively. However, certain properties of MLAs under conditions common in genomic-related data have not been well-studied--in particular, correlations among predictors pose a problem. Results: Using extensive simulation, we showed considering correlation within predictors is crucial in making valid inferences using variable importance measures (VIMs) from three MLAs: random forest (RF), conditional inference forest (CIF) and Monte Carlo logic regression (MCLR). Using a case-control illustration, we showed that the RF VIMs--even permutation-based--were less able to detect association than other algorithms at effect sizes encountered in complex disease studies. This reduction occurred when 'causal' predictors were correlated with other predictors, and was sharpest when RF tree building used the Gini index. Indeed, RF Gini VIMs are biased under correlation, dependent on predictor correlation strength/number and over-trained to random fluctuations in data when tree terminal node size was small. Permutation-based VIM distributions were less variable for correlated predictors and are unbiased, thus may be preferred when predictors are correlated. MLAs are a powerful tool for high-dimensional data analysis, but well-considered use of algorithms is necessary to draw valid conclusions. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Genome ; Genome-Wide Association Study ; Genomics/methods ; Oligonucleotide Array Sequence Analysis ; Phenotype ; Polymorphism, Single Nucleotide
Language	English
Publishing date	2009-08-01
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btp331
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Article ; Online: Phenotypic and genetic analysis of cognitive performance in Major Depressive Disorder in the Generation Scotland: Scottish Family Health Study.

Meijsen, Joeri J / Campbell, Archie / Hayward, Caroline / Porteous, David J / Deary, Ian J / Marioni, Riccardo E / Nicodemus, Kristin K

Translational psychiatry

2018 Volume 8, Issue 1, Page(s) 63

Abstract: Lower performances in cognitive ability in individuals with Major Depressive Disorder (MDD) have been observed on multiple occasions. Understanding cognitive performance in MDD could provide a wider insight in the aetiology of MDD as a whole. Using a ... ...

Abstract	Lower performances in cognitive ability in individuals with Major Depressive Disorder (MDD) have been observed on multiple occasions. Understanding cognitive performance in MDD could provide a wider insight in the aetiology of MDD as a whole. Using a large, well characterised cohort (N = 7012), we tested for: differences in cognitive performance by MDD status and a gene (single SNP or polygenic score) by MDD interaction effect on cognitive performance. Linear regression was used to assess the association between cognitive performance and MDD status in a case-control, single-episode-recurrent MDD and control-recurrent MDD study design. Test scores on verbal declarative memory, executive functioning, vocabulary, and processing speed were examined. Cognitive performance measures showing a significant difference between groups were subsequently analysed for genetic associations. Those with recurrent MDD have lower processing speed versus controls and single-episode MDD (β = -2.44, p = 3.6 × 10
MeSH term(s)	Adult ; Case-Control Studies ; Cognitive Dysfunction/epidemiology ; Cognitive Dysfunction/etiology ; Cognitive Dysfunction/genetics ; Cognitive Dysfunction/physiopathology ; Cohort Studies ; Depressive Disorder, Major/complications ; Depressive Disorder, Major/epidemiology ; Depressive Disorder, Major/genetics ; Depressive Disorder, Major/physiopathology ; Executive Function/physiology ; Female ; Genome-Wide Association Study ; Humans ; Language ; Male ; Memory/physiology ; Middle Aged ; Multifactorial Inheritance/genetics ; Polymorphism, Single Nucleotide ; Psychomotor Performance/physiology ; Recurrence ; Scotland/epidemiology
Language	English
Publishing date	2018-03-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2609311-X
ISSN	2158-3188 ; 2158-3188
ISSN (online)	2158-3188
ISSN	2158-3188
DOI	10.1038/s41398-018-0111-0
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Article ; Online: Using tree-based methods for detection of gene-gene interactions in the presence of a polygenic signal: simulation study with application to educational attainment in the Generation Scotland Cohort Study.

Meijsen, Joeri J / Rammos, Alexandros / Campbell, Archie / Hayward, Caroline / Porteous, David J / Deary, Ian J / Marioni, Riccardo E / Nicodemus, Kristin K

Bioinformatics (Oxford, England)

2018 Volume 35, Issue 2, Page(s) 181–188

Abstract: Motivation: The genomic architecture of human complex diseases is thought to be attributable to single markers, polygenic components and epistatic components. No study has examined the ability of tree-based methods to detect epistasis in the presence of ...

Abstract	Motivation: The genomic architecture of human complex diseases is thought to be attributable to single markers, polygenic components and epistatic components. No study has examined the ability of tree-based methods to detect epistasis in the presence of a polygenic signal. We sought to apply decision tree-based methods, C5.0 and logic regression, to detect epistasis under several simulated conditions, varying strength of interaction and linkage disequilibrium (LD) structure. We then applied the same methods to the phenotype of educational attainment in a large population cohort. Results: LD pruning improved the power and reduced the type I error. C5.0 had a conservative type I error rate whereas logic regression had a type I error rate that exceeded 5%. Despite the more conservative type I error, C5.0 was observed to have higher power than logic regression across several conditions. In the presence of a polygenic signal, power was generally reduced. Applying both methods on educational attainment in a large population cohort yielded numerous interacting SNPs; notably a SNP in RCAN3 which is associated with reading and spelling and a SNP in NPAS3, a neurodevelopmental gene. Availability and implementation: All methods used are implemented and freely available in R. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Adaptor Proteins, Signal Transducing/genetics ; Basic Helix-Loop-Helix Transcription Factors ; Cohort Studies ; Computational Biology ; Decision Trees ; Epistasis, Genetic ; Genetic Markers ; Genetics, Population/methods ; Humans ; Linkage Disequilibrium ; Multifactorial Inheritance ; Nerve Tissue Proteins/genetics ; Polymorphism, Single Nucleotide ; Scotland ; Software ; Transcription Factors/genetics
Chemical Substances	Adaptor Proteins, Signal Transducing ; Basic Helix-Loop-Helix Transcription Factors ; Genetic Markers ; NPAS3 protein, human ; Nerve Tissue Proteins ; RCAN3 protein, human ; Transcription Factors
Language	English
Publishing date	2018-06-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty462
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Article ; Online: snp.plotter: an R-based SNP/haplotype association and linkage disequilibrium plotting package.

Luna, Augustin / Nicodemus, Kristin K

Bioinformatics (Oxford, England)

2007 Volume 23, Issue 6, Page(s) 774–776

Abstract: ... package and example datasets are available at http://cbdb.nimh.nih.gov/~kristin/snp.plotter.html and http ...

Abstract	Unlabelled: snp.plotter is a newly developed R package which produces high-quality plots of results from genetic association studies. The main features of the package include options to display a linkage disequilibrium (LD) plot below the P-value plot using either the r2 or D' LD metric, to set the X-axis to equal spacing or to use the physical map of markers, and to specify plot labels, colors, symbols and LD heatmap color scheme. snp.plotter can plot single SNP and/or haplotype data and simultaneously plot multiple sets of results. R is a free software environment for statistical computing and graphics available for most platforms. The proposed package provides a simple way to convey both association and LD information in a single appealing graphic for genetic association studies. Availability: Downloadable R package and example datasets are available at http://cbdb.nimh.nih.gov/~kristin/snp.plotter.html and http://www.r-project.org.
MeSH term(s)	Algorithms ; Chromosome Mapping/methods ; Computer Graphics ; DNA Mutational Analysis/methods ; Haplotypes/genetics ; Linkage Disequilibrium/genetics ; Polymorphism, Single Nucleotide/genetics ; Programming Languages ; Software ; User-Computer Interface
Language	English
Publishing date	2007-03-15
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btl657
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