Anabel Yong

Current Highlights

Here’s a selection of projects that I’ve worked on. For each project, I’ve included a brief description, some of the technologies used, and links to view more details or the source code.

Gaussian Processes and Multi-Objective Bayesian Optimization

Project Image 3 Description: For my masters thesis, we present GP-MOBO, a novel multi-objective Bayesian Optimization algorithm that advances the state-of-the-art in molecular optimization. Our approach integrates a fast minimal package for Exact Gaussian Processes (GPs) capable of efficiently handling the full dimensionality of sparse molecular fingerprints without the need for extensive computational resources.

Probabilistic and Unsupervised Learning

Project Image 3 Description: Arguably the most interesting and difficult machine learning I have done! This spans from probabilistic modelling with multivariate Gaussians, model selection, expectation maximisation algorithm with binary data, linear gaussian state space models (LGSSMs), Monte Carlo Markov Chain methods (MCMC) for message decryption, Gibbs sampling with latent distributed allocation, and optimization problems. A course requirement for Gatsby Computational Neuroscience Unit PHD module

Investigation into Autism Spectrum Disorder with ClinVar, NCBI, BioBank data repositories

Project Image 3 Description: Using a bioinformatics and genetic approach to overcome and understand the complexity of this disease, the workflow includes literature mining search to research on gene-ASD associations through Simons Foundation Autism Research Initiative (SFARI) database[6], autism gene analysis by utilizing gene ontology (GO) for attempting to functionally characterize these genes and autism network analysis to decipher which genes interact with each other in certain pathways in autism. Development in this area of research could potentially uncover more accurate molecular mechanisms behind why these mutations occur and how this leads to ASD.

Evaluation of False Discovery Rate approaches in large-scale proteomics data

Project Image 3 Description: Here, we mathematically investigated different false discovery rate approaches for the identification of proteins and microproteins in ProteomeHD2, which is curated by SILAC quantitation from mass spectrometry. This was processed through bio-softwares MaxQuant, Percolator and Fragpipe to process mass spectrometry fragments from wet-lab experiments (SILAC-based MS). The pgFDR tool was optimized here to maximise the detection of true proteins. Performance of pgFDR tool increased to 58% in ProteomeHD2, created by Georg KustatscherNature link.

My Past Projects

Bayesian MCMC for ABO Blood Frequency Modelling

Bayesian MCMC for ABO Blood Fequency Modelling Description: Estimation of allele frequencies in the ABO blood group, The ABO blood type is determined by the presence or absence of the A and B antigens on erythrocytes. It is controlled by a single gene (the ABO gene) with three alleles: I^A, I^B, and i. Here I stands for isoagglutinogen or antigen, while i means absence of either antigen. For convenience we write the three alleles as A,B, and O. As both A and B alleles are dominant over O, genotypes AA or AO both have the same phenotype (type A), and individuals with BB or BO have type B. At Hardy-Weinberg equilibrium, the genotype and phenotype frequencies are given as functions of the frequencies of the three alleles, p, q, and r = 1− p−q. The data, X = (nA,nB,nAB,nO), are counts of the four blood types.

Using Structural Bioinformatics Approach for GULO functionality

Project Image 2 Description: Vitamin C is an important cofactor in many important physiological processes. Vitamin C deficiency usually leads to diseases such as scurvy.The GULO gene encodes an enzyme which converts L-gulono-1,4-lactone to L-ascorbate (vitamin C). GULO enzyme (L-gulonolactone oxidase) is required in the terminal step of catalytic reaction. Members of this enzyme fam- ily contain two important domains: FAD-binding domain and ALO domain. Here demonstrates a structural bioinformatics and functional genomics approach to determine the functionality of GULO gene in certain species.

Random Forest with Bayesian Optimisation for Heart Failure Prediction

Project Image 3 Description: One tool that has been used to address this is machine learning (ML). ML has attempted to detect heart disease in previous cases drawing significant attention in medical diagnostics. This is because ML can recognize patterns that are not immediate to human practitioners, by handling large volumes of data, and navigating the complex interactions of the numerous factors associated with heart failure. One such ML model implemented, is the Random Forest (RF) model. Within this study, we explore the reliability of RF for predicting heart failure considering data quality, feature engineering, model performance and hyperparameter tuning with Bayesian Optimisation.

Biochemistry-focused Projects

Investigating molecular mechanism behind DNMT1 methylation of CpG islands

Project Image 4 Description: I investigated the molecular mechanism of mouse DNMT1-DNA structures with available PDB structures DNMT1 bound to hemi-methylated DNA (4DA4) and unmethylated DNA (3PT6) found on UniProt and ProteinDataBank.

Investigation of molecular mechanism of Minichromosome Maintenance Protein (MCM) protein-DNA interactions

Project Image 4 Description: Part of my biochemistry module, we utilized both wet lab and bioinformatics approaches to investigate the molecular mechanism of MCM protein.

For inquiries or further information about my work, feel free to email me or check out my GitHub profile.