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Pablo Acera

@pabloacera

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Computational biologist with expertise in deep learning and drug discovery.

Australia

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What I'm looking for

I seek a role that fosters innovation in drug discovery and allows collaboration across multidisciplinary teams.

I am a computational biologist with over 8 years of experience in applying deep learning, probability, and statistical models to RNA biology, genomics, and drug discovery. My journey spans both academic and industry settings, where I have developed bioinformatics infrastructure, led machine learning projects, and collaborated across multidisciplinary teams. I am passionate about algorithm development and therapeutic innovations, aiming to bridge computational methods with biological insights to drive breakthroughs in patient care.

Currently, I serve as a postdoctoral researcher at the Children's Cancer Institute, where I develop advanced statistical and machine learning methods to enhance the drug discovery process. My work focuses on creating new small molecule compounds targeting mRNAs and predicting genes for targeted intervention with RNA splicing modulators. I have also built bioinformatics infrastructure for a drug discovery company, showcasing my ability to integrate complex data analysis with practical applications in the field.

Experience

Work history, roles, and key accomplishments

Current

Postdoctoral Researcher

Current

Children's Cancer Institute

Jan 2023 - Present (2 years 5 months)

As a Postdoctoral Researcher, I develop advanced statistical and machine learning methods to enhance drug discovery processes, focusing on small molecule compounds targeting mRNAs. I also built the bioinformatics infrastructure for Minimum Bio, processing Nanopore HTS data and performing statistical analyses.

Statistical Analysis Machine Learning Bioinformatics Python Deep Learning Drug Discovery HPC Docker

PhD Candidate

Australian National University

Sep 2019 - Jan 2022 (2 years 4 months)

During my PhD, I developed a deep learning model for predicting RNA modifications using Nanopore sequencing, benchmarking against other algorithms and applying it to various datasets, resulting in a first author publication in Nature Communications.

Deep Learning Bioinformatics Statistical Analysis Python Publications Machine Learning

Software Developer

European Bioinformatics Institute

Apr 2018 - Mar 2019 (11 months)

At EMBL-EBI, I developed and implemented algorithms for analyzing capture Hi-C datasets as part of the Multi-Scale Complex Genomics project, integrating tools into the Barcelona Supercomputer Center HPC platform.

Algorithm Development Capture Hi C Bioinformatics HPC Python Data Integration Statistical Analysis Genomics Software Development

Education

Degrees, certifications, and relevant coursework

Australian National University

PhD, Bioinformatics

2019 - 2022

During my PhD, I developed a deep learning model that predicts RNA modifications at single-nucleotide and molecule resolution using RNA direct Nanopore sequencing. We also thoroughly benchmarked our tool against other algorithms and applied it to several in-vitro, cell lines and mouse tissue sequencing datasets. My PhD work led to a first author publication in Nature Communications.

Lund University and Utrecht University

Master's Degree, Bioinformatics

2016 - 2018

Activities and societies: Erasmus + scholarship

In this project, we investigated the effects of single nucleotide polymorphisms (SNPs) epistasis and SNPs cumulative effects in the 3D genome on complex traits.