mcharrison95/RF_for_Yeast_GenSpec: Random forest code for y1000+ paper

Hittinger, Chris Todd; Rokas, Antonis

doi:10.5281/zenodo.10711059

Title: mcharrison95/RF_for_Yeast_GenSpec: Random forest code for y1000+ paper

Dataset
Other Related Research

Abstract

Random forest code for predicting generalists and specialists from gene annotations for the paper "Genomic and ecological factors shaping specialism and generalism across an entire subphylum"

Authors:

Hittinger, Chris Todd; Rokas, Antonis

GLBRC - University of Wisconsin-Madison
Department of Biological SciencesVanderbilt University Nashville TN 37235 USA

Publication Date:: Sun Feb 25 23:00:00 EST 2024

DOE Contract Number:: SC0018409

Research Org.:: Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Subject:: Carbon/metabolism; Fungal nitrogen/metabolism; Genome; ascomycota/genetics/metabolism; metabolic networks and pathways/genetics

OSTI Identifier:: 3003583

DOI:: https://doi.org/10.5281/zenodo.10711059

Citation Formats


                    Hittinger, Chris Todd, and Rokas, Antonis. mcharrison95/RF_for_Yeast_GenSpec: Random forest code for y1000+ paper.  United States: N. p., 2024. 
        Web.  doi:10.5281/zenodo.10711059.

Copy to clipboard


                    Hittinger, Chris Todd, & Rokas, Antonis. mcharrison95/RF_for_Yeast_GenSpec: Random forest code for y1000+ paper.  United States.  doi:https://doi.org/10.5281/zenodo.10711059

Copy to clipboard


                    Hittinger, Chris Todd, and Rokas, Antonis. 2024.  
"mcharrison95/RF_for_Yeast_GenSpec: Random forest code for y1000+ paper".  United States.  doi:https://doi.org/10.5281/zenodo.10711059.  https://www.osti.gov/servlets/purl/3003583. Pub date:Sun Feb 25 23:00:00 EST 2024

Copy to clipboard


                    
@article{osti_3003583,

  title        = {mcharrison95/RF_for_Yeast_GenSpec: Random forest code for y1000+ paper},

  author       = {Hittinger, Chris Todd and Rokas, Antonis},

  abstractNote = {Random forest code for predicting generalists and specialists from gene annotations for the paper "Genomic and ecological factors shaping specialism and generalism across an entire subphylum"},

  doi          = {10.5281/zenodo.10711059},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Feb 25 23:00:00 EST 2024},

  month        = {Sun Feb 25 23:00:00 EST 2024}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.5281/zenodo.10711059

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Y1000 gene family

Dataset Rokas, Antonis ; Hittinger, Chris Todd

All relative analysis data and codes used in this study are available on the Figshare data repository. You can also find the codes on the Github repository at https://github.com/vnuii/Y1000_gene_family.
DanaO523/Y1000Project: Y1000+ Analyses

Dataset Hittinger, Chris Todd ; Rokas, Antonis

R Scripts to analyze growth curves and perform KEGG enrichment analyses for the Y1000+ dataset.
PBL height from random forest, random forest classifier and lightGBM ML models.

Dataset Zhang, Damao ; Levin, Maxwell ; Goldberger, Lexie ; ...

PBL height from random forest, random forest classifier and lightGBM ML models.
Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data

Dataset Walker, Angela M ; Cliff, Ashley ; Romero, Jonathon ; ...

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3. We use both synthetic and empirical networks frommore »« less
An application of survival analysis to disruption prediction via Random Forests

Dataset Tinguely, R. A. ; Montes, K. J. ; Rea, C. ; ...

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trainedmore »« less

Similar Records