Hi! I'm Colby...

About Me

I'm Colby T. Ford, Ph.D.

I'm a Computational Biomathematician and Data Scientist. I have a passion for Mathematics and Genomics research. So, I use Machine Learning and Visual Analytics to help along the way.

Originally from Lenoir, North Carolina, I studied Applied Mathematics, Data Science, and Computational Biology at the University of North Carolina at Charlotte.
I currently work as a researcher in the Department of Bioinformatics and Genomics at UNCC and as a Data Scientist/Artificial Intelligence Solution Architect for BlueGranite, a Microsoft partner consulting firm focused on delivering cloud-based Data and AI solutions. I am currently located in Charlotte, North Carolina.

  • Connect:
  • Mathematics

    Statistical Modeling
    Bayesian Nets
    Operations Research
    Numerical Logic

  • Machine Learning

    Algorithm Design
    Natural Language Processing
    Deep Learning
    Artificial Intelligence

  • Computational Biology

    Human Genomics
    Molecular Sequence Analysis
    Infectious Diseases

  • Distributed Computing

    Apache Spark (Databricks)
    Cluster Computing (MPI/SNOW)
    GPU-Based Processing (CUDA)
    High Performance Computing

  • Programming

    Visual Basic

  • Web and Design

    API Development

  • Machine Learning, Distributed Computing, Data Engineering


    A collection of "cookbook-style" scripts for simplifying data engineering and machine learning in Apache Spark.

    Apache Spark is a highly-scalable, massively-parallel computing platform perfect for machine learning and data engineering tasks. Using distributed processing with the Spark API, users can perform various tasks on huge amounts of data using their their preferred language (Python, R, Scala, SQL, etc.), but often there is a bit of a learning curve to using the Spark functionality (PySpark or SparkR) even if the user is a pro at the base language. Sparkitecture is a ebook collection of various script to help make this process a little easier.

  • Genomics, Machine Learning, Infectious Diseases

    Machine Learning in Malaria

    Machine Learning Modeling in the Prediction of Artemisinin Resistance and Diagnostic Test Sensitivity in Malaria.

    Parallel Processing and Ensemble Machine Learning Modeling for the Prediction of Artemisinin Resistance in Malaria (Malaria DREAM Challenge 2019 Submission).

    The Malaria DREAM Challenge is open to anyone interested in contributing to the development of computational models that address important problems in advancing the fight against malaria. The overall goal of the first Malaria DREAM Challenge is to predict Artemisinin (Art) drug resistance level of a test set of malaria parasites using their in vitro transcription data and a training set consisting of published in vivo and unpublished in vitro transcriptomes. The in vivo dataset consists of ~1000 transcription samples from various geographic locations covering a wide range of life cycles and resistance levels, with other accompanying data such as patient age, geographic location, Art combination therapy used, etc. [Mok et al., (2015) Science]. The in vitro transcription dataset consists of 55 isolates, with transcription collected at two timepoints (6 and 24 hours post-invasion), in the absence or presence of an Art perturbation, for two biological replicates using a custom microarray at the Ferdig lab. Using these transcription datasets, participants will be asked to predict three different resistance states of a subset of the 55 in vitro isolate samples.

    Modeling Plasmodium falciparum Diagnostic Test Sensitivity using Machine Learning with Histidine-Rich Protein 2 Variants.

    Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most durable health threats in the world. Previously, simplistic regression-based models have been created to characterize malaria infections, though these models often only include a couple genetic factors. Specifically, the Baker et al., 2005 model uses two types of particular repeats in histidine-rich protein 2 (PfHRP2) to assert P. falciparum infection, though the efficacy of this model has waned over recent years due to genetic mutations in the parasite. In this work, we use a dataset of 406 P. falciparum PfHRP2 genetic sequences collected in Ethiopia and derived a larger set of motif repeat matches for use in generating a series of diagnostic machine learning models. Here we show that the usage of additional and different motif repeats proves effective in predicting infection. Furthermore, we use machine learning model explanability methods to highlight which of the repeat types are most important, thereby suggesting potential targets for future versions of rapid diagnostic tests.

  • Genomics, Phylogenetics, Infectious Diseases


    Shiny web application for visualizing disease transmittion networks from phylogenetic trees.

    Strainhub is designed as a web-based software to generate disease transmission networks and associated metrics from a combination of a phylogenetic tree and a metadata associated file. The software maps the metadata onto the tree and performs a parsimony ancestry reconstruction step to create links between the associated metadata and enable the construction of the network.

  • Genomics, Infectious Diseases

    Genetic Capitalism in E. coli

    Persistence of Genes that Confer Antimicrobial Resistance in the History of Escherichia coli Genomes

    Antimicrobial resistance (AMR) in pathogenic strains of bacteria, such as Escherichia coli (E. coli), adversely impact personal and public health. In this study, we examine competing hypotheses for the evolution of AMR including: 1) "genetic capitalism" in which genotypes that confer antibiotic resistance are gained and not often lost in lineages, and 2) "stabilizing selection" in which genotypes that confer antibiotic resistance are gained and lost often. To test these hypotheses, we assembled a dataset that includes annotations for 409 AMR genotypes and a phylogenetic tree based on genome-wide single nucleotide polymorphisms from 29,255 isolates of E. coli. We used phylogenetic methods to count the times each AMR genotype was gained and lost across the tree and used model-based clustering of the genotypes with respect to their gain and loss rates. We demonstrate that many genotypes cluster to support the hypothesis for genetic capitalism while a few cluster to support the hypothesis for stabilizing selection. Comparing the sets of genotypes that fall under each of the hypotheses, we found a statistically significant difference in the breakdown of resistance mechanisms through which the AMR genotypes function. The result that many AMR genotypes cluster under genetic capitalism reflects that strong positive selective forces, primarily induced by human industrialization of antibiotics, outweigh the potential fitness costs to the bacterial lineages for carrying the AMR genotypes. We expect genetic capitalism to further drive bacterial lineages to resist antibiotics. We find that antibiotics that function via replacement and efflux tend to behave under stabilizing selection and thus may be valuable in an antibiotic cycling strategy.

  • R Package


    Easily output package citations and tidy session information for reproducibility in research publications.

  • R Package, Genomics


    R functions for interfacing with the Microsoft Genomics service in Azure.

  • R Package


    A patchwork of efficient and tidy multidimensional data operations.

  • Human Genomics, Phylogenetics

    An Integrated Phylogeographic Analysis of the Bantu Migration

    Using phylogenetic analysis, dimensionality reduction, and machine learning, a combined migratory model of the Bantu migration was formed with heterogeneous data.

  • R Package, Genomics


    Parallel Implementations of the Empirical Bayesian Elastic Net Cross-Validation in R.

  • Human Genomics, Phylogenetics, Linguistics

    Visualizing Linguistic Disparity of Uto-Aztecan Languages and Bantu Languages

    Using unsupervised learning for dimensionality reduction and visualization.

  • Phylogenetics, Epidemiology, Infectious Diseases

    Spread of Middle East Respiratory Coronavirus: Genetic versus Epidemiological Data

    Analysis and visualzation of the WHO reports of MERS infections versus genomic data.

  • Human Genomics, Machine Learning

    MPS-IIIB/NAGLU Prediction

    PolyPhen2 + Machine Learning prediction of the effects of genetic mutations on mucopolysaccharidosis IIIB.


Let's Connect!

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