Kuncheng Song

Works (5)

Updated: September 9th, 2023 05:00

2023 journal article

Functional annotation of proteins for signaling network inference in non-model species

Nature Communications, 14(1).

By: L. Van den Broeck n , D. Bhosale n, K. Song n, C. Fonseca de Lima *, M. Ashley  n, T. Zhu *, S. Zhu *, B. Van De Cotte* ...

TL;DR: A multi-layer neural network that determines protein functionality directly from the protein sequence is developed that shows generalization and scalability and extends to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, ORCID, Crossref
Added: September 5, 2023

2023 journal article

Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations

BIOENGINEERING-BASEL, 10(2).

By: K. Song n & Y. Zhou  n

author keywords: feature selection; machine learning; microbiome; random forest; support vector machine; logistic regression
TL;DR: This study developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn’s disease, and immunotherapy response) using nine independent microbiome datasets using random forest as the top model. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 10, 2023

2022 journal article

C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data

BMC BIOINFORMATICS, 23(1).

By: K. Song n & Y. Zhou  n

author keywords: Co-occurrence network analysis; Microbiome; R package; Consensus clustering; Module preservation analysis
MeSH headings : Phylogeny; Consensus
TL;DR: C3NA is presented, a user-friendly R package for investigating compositional microbial sequencing data to identify and compare co-occurrence patterns across different taxonomic levels and discovered clusters of study and disease-dependent taxa that overlap with known functional taxa studied by other discovery studies and differential abundance analyses. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: November 21, 2022

2021 personal communication

The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning

Marvel, S. W., House, J. S., Wheeler, M., Song, K., Zhou, Y.-H., Wright, F. A., … Reif, D. M. (2021, January).

By: S. Marvel n, J. House* , M. Wheeler *, K. Song n, Y. Zhou  n, F. Wright n, W. Chiu *, I. Rusyn *, A. Motsinger-Reif*, D. Reif n 

Contributors: S. Marvel n, J. House* , M. Wheeler *, K. Song n, Y. Zhou  n, F. Wright n, W. Chiu *, I. Rusyn *, A. Motsinger-Reif*, D. Reif n 

MeSH headings : COVID-19 / epidemiology; Data Visualization; Health Status Indicators; Humans; Machine Learning; Models, Statistical; Vulnerable Populations
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Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 22, 2021

2020 journal article

Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction

FRONTIERS IN MOLECULAR BIOSCIENCES, 7.

By: K. Song n, F. Wright n & Y. Zhou  n

author keywords: phenotype prediction; machine learning method; k-mers; operational taxonomic unit (OTU); amplicon sequence variant (ASV); phylogenetic analysis
TL;DR: The use of short k-mers, which have computational advantages and conceptual simplicity, is shown to be effective as a source for microbiome-based prediction, among machine-learning approaches, and tree-based methods show consistent, though modest, advantages in prediction accuracy. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: January 19, 2021

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