Research

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Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data.

#Machine Learning, Ribozyme, Function

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RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple sel-cleaving ribozymes.

#Ribozyme, Structure

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Primers to highly conserved elements optimized for qPCR-based telomere length measurement in vertebrates.

#Primers, Telomeres

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Fragmentation of Apolipoprotein E4 is Required for Differential Expression of Inflammation and Activation Related Genes in Microglia Cells.

#APOE4, Differential Expression

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A draft genome provides hypotheses on drought tolerance in a keystone plant species in Western North America threatened by climate change.

#Sagebrush Genome

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Code

Ribozyme Structure
Data: PRJEB52899 Data: PRJEB51631
Repository: Mut_12
Machine Learning
Data: PRJEB51631
Repository: ML-ribo-predict

About

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James D. Beck

Computational Scientist in Biology

I'm seeking to join a team of molecular biology researchers in September 2022. I'm most interested in those teams that are considering problems involving the diagnosis and treatment of disease. My ideal employment would involve a wide variety of research questions including the structural and functional predictions of biopolymers, computational learning of diagnostic patterns, genomic and metagenomic analysis, and tinyML solutions to personal health monitoring systems.

I love collaboration. The development of new science and computational tools to process and understand scientific data with a diverse team of professionals motivates me. I have developed customized scripts and pipelines for assessing a wide array of biological analyses in metagenomics, amplicon sequence error correction, differential protein expression, dimensionality reduction, and RNA characterizations of structure and function. I've implemented this code on research clusters using containerized solutions to assure reproducibility. The code is available on Gitlab by request.

I am experienced in a wide array of coding languages. My experience includes substantial implementations of Julia, Python, R, and C/C++ code and a growing familiarity with Rust and Lua. Additionally, I am a proficient user of Git version control, Docker and Singularity containerization tools, and high-performance computing tools such as SLURM and CUDA.

I am trained in numerical methods and statistical analysis. I have performed a wide array of analyses that include clustering, correlation testing, dimensionality reduction, k-fold validation, and regression analysis. I have also implemented parallelized code for the multi-dimensional analysis of wave patterns using numerical techniques.

I am a competent user of PyTorch and Scikit-Learn's Machine Learning tools I have implemented custom data ingestion tools. I have also implemented a wide array of models including the prediction of secondary structure using support vector regression and the prediction of functional activity using LSTM, Auto-encoders, Random-Forest, and Linear Models.
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