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Aaron Chuah

Publications and source records attributed to Aaron Chuah.

2 recordsLinked to original sources

EDAmame: interactive exploratory data analyses with explainable models.

SUMMARY: Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements. AVAILABILITY AND IMPLEMENTATION: A limited online version can be accessed at https://edamame.org.au/ or can be downloaded from https://doi.org/10.5281/zenodo.15356492. The app is developed in R Shiny and implements tidyverse and tidymodels packages.

Machine Learning↗

A format for databasing and comparison of AFLP fingerprint profiles.

BACKGROUND: Amplified fragment length polymorphism (AFLP) is a PCR-based technique that involves restriction of genomic DNA followed by ligation of adaptors to the fragments generated and selective PCR amplification of a subset of these fragments. The amplified fragments are separated on a sequencing gel and visualized by autoradiography or fluorescent sequencing equipment. AFLP allows high-resolution genotyping but the lack of a format for databasing and comparison of AFLP fingerprint profiles limits its wider applications in profiling large numbers of biological samples. RESULTS: A scheme is described to represent a DNA fingerprint profile with a nucleotide sequence-like format in which the information line contains the minimal necessary details to interpret an AFLP DNA fingerprint profile. They include technique used, information on restriction enzymes, primer combination, biological source for DNA materials, fragment sizing and annotation. The bodylines contain information on size and relative intensity of DNA fragments by a string of defined alphabets or symbols. Algorithms for normalizing raw data, binning of fragments and comparing AFLP DNA fingerprint profiles are described. Firstly, the peak heights are normalized against their average and then represented by five symbols according to their relative intensities. Secondly, a binning algorithm based loosely on common springs and rubber bands is applied, which positions sequence fragments into their best possible integer approximations. A BLAST-like reward-penalty concept is used to compare AFLP fingerprint profiles by matching peaks using two metrics: score and percentage of similarity. A software package was developed based on our scheme and proposed algorithms. Example of use this software is given in evaluating novelty of a new tropical orchid cultivar by comparing its AFLP fingerprint profile against those of related commercial cultivars in a database. CONCLUSIONS: AFLP DNA fingerprint profiles can be databased and compared effectively with software developed based on our scheme and algorithms. It will facilitate wider use of this DNA fingerprinting technique in areas such as forensic study, intellectual property protection for biological materials and biodiversity management. Moreover, the same concepts can be applied to databasing and comparing DNA fingerprint profiles obtained with other DNA fingerprint techniques.

Algorithms↗