Sergio Peignier

Sergio Peignier

Bridging AI, computational science, and real-world biological systems

Associate Professor and researcher in machine learning and bioinformatics, working at the intersection of data science, systems biology, and explainable AI.

I collaborate with research institutions, biotech organizations, and data-driven teams to design and apply machine learning models for complex scientific and biological systems. My work focuses on transforming data into interpretable models and actionable insights.

Award-winning researcher with distinctions from INSA Lyon and Best Paper awards at leading international conferences (GECCO, SAC).

INSA Lyon · Machine Learning · Bioinformatics · Explainable AI · Complex Systems

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Sergio Peignier
INSA Lyon Associate Professor
30+ Publications Machine Learning & Biology
International Awards GECCO · SAC Best Papers

Areas of Expertise

Bioinformatics & Systems Biology

Modeling complex biological systems and gene regulatory networks.

Machine Learning

Applied machine learning and explainable AI for scientific data analysis.

Data Science

Data-driven approaches for extracting insights from high-dimensional datasets.

Full academic profile available (Download CV)

Academic & Applied Courses

Structured learning materials in mathematics, computer science, and data-driven modeling.

Statistical Learning & Signal Analysis

Foundations of statistical learning, signal analysis, and optimization methods for modeling and extracting information from complex datasets.

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Linear Algebra

Vectors, matrices, eigenvalues, and applications in data science and AI.

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Bioinformatics

Computational methods for analyzing biological data, including genomes and gene networks.

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Databases

Relational models, SQL, and data management systems.

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Software Development & Deployment

Software engineering principles, version control, and deployment pipelines.

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Graph Theory

Networks, graphs, algorithms, and applications in complex systems.

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Stochastic Processes

Poisson process, Moran process, Branching, Markov Chains.

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Computational Methods in Python

Applied Python for data analysis, machine learning, and computational modeling in complex systems and biological networks.

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Research & Academic Skills

Essential skills for scientific research, including critical thinking, academic writing, and effective communication of technical and scientific work.

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Webinars

Upcoming events and access to recorded sessions covering key topics and applied insights.

Looking for structured training or customized courses?

I offer academic and industry-oriented training programs in AI, data science, and computational biology, tailored to students, research teams, and organizations.

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Selected Work & Research Highlights

My research focuses on the intersection of machine learning, explainable AI, and computational biology, with applications ranging from gene regulatory network inference to complex biological system modeling.

I develop data-driven and interpretable machine learning methods to extract meaningful structure from high-dimensional biological and ecological systems, combining statistical learning, network science, and evolutionary computation.

More recently, my work has expanded into explainable AI for complex models and cross-domain applications of machine learning in biological and environmental systems.

30+ peer-reviewed publications in machine learning, bioinformatics, and computational biology · 350+ citations · International collaborations · AI applied to biological systems

Bacterial tubular networks channel carbohydrates in insect endosymbiosis

Cell · 2025

High-impact study on metabolic exchange mechanisms in insect–bacteria symbiosis.

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Explaining Random Forest and XGBoost with Shallow Decision Trees via Feature Co-Clustering

Machine Learning · 2025

Explainable AI methods for interpreting ensemble models using structured feature importance analysis.

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Bioactivity analysis of essential oils against agricultural pests

Industrial Crops and Products · 2023

Data-driven analysis of biological activity in complex natural systems for identifying effective plant-based pest control agents.

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GReNaDIne: Data-driven Python library for gene regulatory network inference

Genes · 2023

Computational framework for inferring gene regulatory networks using data-driven machine learning

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Gene regulatory network inference using ensemble learning

International Journal on Artificial Intelligence Tools · 2023

Application of ensemble machine learning techniques to reconstruct gene regulatory networks from high-dimensional biological data

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Spatial–spectral analysis of hyperspectral images for early plant disease detection

International Journal of Molecular Sciences · 2022

SVM approach to enable early detection of plant diseases from hyperspectral imagery.

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Gene regulatory network inference using classification algorithms

International Journal on Artificial Intelligence Tools · 2021

Machine learning-based framework using classification models to infer gene interactions in complex biological systems.

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Evolutionary novelty in apoptotic pathways of aphids

PNAS · 2020

Evolutionary analysis of apoptotic gene families revealing diversification patterns and functional innovation in insect genomes.

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Subspace clustering using evolvable genome structure

Genetic and Evolutionary Computation Conference · 2015

Evolutionary computation approach for subspace clustering based on genome-inspired representations of solution spaces.

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Computational Tools & Open Research Software

A collection of research software and computational tools for machine learning, bioinformatics, and complex systems modeling. These projects bridge theoretical developments with practical implementations used in scientific and applied contexts.

Chameleoclust (EvoWave Package)

Bio-inspired clustering algorithm based on evolvable genome structures, featuring variable-length representations, functional and non-functional elements, and mutation operators including chromosomal rearrangements.

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SubCMedians

Subspace clustering algorithm extending the K-medians paradigm, based on stochastic local exploration and efficient optimization in high-dimensional data spaces.

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GXN (Gene Self-Expressive Networks)

Framework for learning generalizable gene regulatory representations using self-expressive network models for biological data analysis.

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GReNaDIne

Data-driven framework for gene regulatory network inference from gene expression data, integrating machine learning and statistical modeling approaches.

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GSHAPA

Gene set analysis tool for single-cell RNA-seq data using Random Forest models and SHAP-based explainability techniques.

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Alzheimer's Disease GRN Analysis

Computational framework for studying gene regulatory programs associated with Alzheimer's disease using data-driven and explainable AI approaches.

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Books

Practical books on data science, machine learning, and computational methods designed for students, researchers, and professionals.

The Practitioner's Guide to Hyperspectral AI in Agriculture

A practical and applied guide to machine learning and hyperspectral imaging, focused on real-world agricultural systems, from data acquisition to predictive modeling.

Book in preparation (forthcoming on Amazon)

Coming soon (Pre-release access available upon request)

Collaborations & Consulting

I collaborate with research groups, companies, and institutions to apply machine learning and computational modeling to complex biological and data-driven problems.

AI for Biological Systems

Design and implementation of interpretable models for biological data.

Data Analysis & Modeling

Advanced analysis of complex datasets and system modeling.

Training & Workshops

Custom sessions for teams and organizations.

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