Predictive Models of Gene Regulation: Application of Regression Methods to Microarray Data

2007 ◽  
pp. 95-110
Author(s):  
Debopriya Das ◽  
Michael Q. Zhang
Author(s):  
Dileep Kumar G.

Tree-based learning techniques are considered to be one of the best and most used supervised learning methods. Tree-based methods empower predictive models with high accuracy, stability, and ease of interpretation. Unlike linear models, they map non-linear relationships pretty well. These methods are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being widely used in all kinds of machine learning and data science problems. Hence, for every data analyst, it is important to learn these algorithms and use them for modeling. This chapter guide the learner to learn tree-based modeling techniques from scratch.


2005 ◽  
Vol 97 (24) ◽  
pp. 1852-1853
Author(s):  
James F. Reid ◽  
Lara Lusa ◽  
Loris De Cecco ◽  
Danila Coradini ◽  
Silvia Veneroni ◽  
...  

2009 ◽  
Vol 13 (6) ◽  
pp. 1075-1082 ◽  
Author(s):  
Dong-Guk Shin ◽  
S.A. Kazmi ◽  
Baikang Pei ◽  
Yoo-Ah Kim ◽  
J. Maddox ◽  
...  

2005 ◽  
Vol 97 (12) ◽  
pp. 927-930 ◽  
Author(s):  
James F. Reid ◽  
Lara Lusa ◽  
Loris De Cecco ◽  
Danila Coradini ◽  
Silvia Veneroni ◽  
...  

2019 ◽  
Vol 116 (50) ◽  
pp. 25186-25195 ◽  
Author(s):  
Teng Fei ◽  
Wei Li ◽  
Jingyu Peng ◽  
Tengfei Xiao ◽  
Chen-Hao Chen ◽  
...  

Although millions of transcription factor binding sites, or cistromes, have been identified across the human genome, defining which of these sites is functional in a given condition remains challenging. Using CRISPR/Cas9 knockout screens and gene essentiality or fitness as the readout, we systematically investigated the essentiality of over 10,000 FOXA1 and CTCF binding sites in breast and prostate cancer cells. We found that essential FOXA1 binding sites act as enhancers to orchestrate the expression of nearby essential genes through the binding of lineage-specific transcription factors. In contrast, CRISPR screens of the CTCF cistrome revealed 2 classes of essential binding sites. The first class of essential CTCF binding sites act like FOXA1 sites as enhancers to regulate the expression of nearby essential genes, while a second class of essential CTCF binding sites was identified at topologically associated domain (TAD) boundaries and display distinct characteristics. Using regression methods trained on our screening data and public epigenetic profiles, we developed a model to predict essential cis-elements with high accuracy. The model for FOXA1 essentiality correctly predicts noncoding variants associated with cancer risk and progression. Taken together, CRISPR screens of cis-regulatory elements can define the essential cistrome of a given factor and can inform the development of predictive models of cistrome function.


2018 ◽  
Author(s):  
Anwar O. Nunez-Elizalde ◽  
Alexander G. Huth ◽  
Jack L. Gallant

AbstractPredictive models for neural or fMRI data are often fit using regression methods that employ priors on the model parameters. One widely used method is ridge regression, which employs a spherical Gaussian prior that assumes equal and independent variance for all parameters. However, a spherical prior is not always optimal or appropriate. There are many cases where expert knowledge or hypotheses about the structure of the model parameters could be used to construct a better prior. In these cases, non-spherical Gaussian priors can be employed using a generalized form of ridge known as Tikhonov regression. Yet Tikhonov regression is only rarely used in neuroscience. In this paper we discuss the theoretical basis for Tikhonov regression, demonstrate a computationally efficient method for its application, and show several examples of how Tikhonov regression can improve predictive models for fMRI data. We also show that many earlier studies have implicitly used Tikhonov regression by linearly transforming the regressors before performing ridge regression.


2005 ◽  
Vol 97 (24) ◽  
pp. 1851-1852 ◽  
Author(s):  
Maurice P. H. M. Jansen ◽  
John A. Foekens ◽  
Jan G. M. Klijn ◽  
Els M. J. J. Berns

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