IEEE Congress on Evolutionary Computation (CEC)2026Conference Papers
EvoGrad is a unified differentiable framework that turns evolutionary and swarm operators into differentiable operators, integrating Evolutionary Computation and Swarm Intelligence with gradient-based optimization via backpropagation.
Citterio B.F.R., Papetti D.M., Dimitri G.M., Tangherloni A. (2026). EvoGrad: Accelerated Metaheuristics in a Differentiable Wonderland. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
IEEE/CVF International Conference on Computer Vision (ICCV)2025Conference Papers
To enable privacy-preserving video analysis, we propose AnonNET, a diffusion-based system that de-identifies faces yet maintains age, gender, pose, expression, and realistic temporal dynamics.
Egin A., Tangherloni A., Dantcheva A. (2025). Now You See Me, Now You Don’t: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos. In IEEE/CVF International Conference on Computer Vision (ICCV), IEEE.
Fabbricatore A., Buffa F.M., Tangherloni A. (2025). Knowledge-Enriched Cell-Type Annotation in Single-Cell Transcriptomics via LLM Embeddings. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2025Conference Papers
We introduce GENESIS, a generative modelling framework that bridges the gap between Multiome GEX and scRNA-Seq data by producing enhanced whole-cell–like expression profiles.
Riva S.G., Myers B., Buffa F.M., Tangherloni A. (2025). GENESIS: Generating scRNA-Seq data from Multiome Gene Expression. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
International Conference on the Applications of Evolutionary Computation (Part of EvoStar)2025Conference Papers
To prevent FST-PSO from getting trapped in local optima, we propose FTT-PSO, a time-travel strategy that rewinds the swarm and reinitialises the global best when progress stalls, yielding superior results on major benchmark suites.
Papetti D.M., Tangherloni A., Coelho V., Besozzi D., Cazzaniga P., Nobile M.S. (2025). We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), Springer.
A step-by-step guide to the analysis and interpretation of biomedical data, covering the computational and statistical methods used in modern biomedical research.
Citation
Cascino D.L., Gatti G., Unwith S., Matarazzo L.S., Riva S.G., Damiani G., Tangherloni A. (2025). Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation. River Publishers.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2024Conference Papers
We introduce FP, a novel forest-structured evolutionary algorithm that models complex gene regulatory interactions and reconstructs Boolean GRNs with efficient convergence and strong precision.
Stranieri N., Buffa F.M.,Tangherloni A. (2024). Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2024Conference Papers
We propose a novel and efficient feature-selection strategy for Fuzzy Inference Systems that uses Random Forest–based variable ranking to identify the smallest set of informative features.
Tangherloni A., Cazzaniga P., Stranieri N., Buffa F.M., Nobile M.S. (2024). A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Congress on Evolutionary Computation (CEC)2024Conference Papers
We introduce iEACOP, an enhanced evolutionary algorithm with minimal hyper-parameter tuning requirements, which outperforms the original EACOP on most CEC 2017 benchmark functions and achieves performance comparable to top competition algorithms.
Tangherloni A., Coelho V., Buffa F.M., Cazzaniga P. (2024). A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
Journal of Biomedical Informatics2023Journal Articles
MAGNETO is a fully-automatic framework that builds compact, optimal marker panels from single-cell gene expression data by solving a tailored bi-objective optimization problem.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2023Conference Papers
We introduce a pseudo-voting consensus approach within scALPO that combines multiple clustering outputs to achieve more robust and accurate cell-type annotation.
Riva S.G., Myers B., Cazzaniga P., Buffa F.M., Tangherloni A. (2023). Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Congress on Evolutionary Computation (CEC)2023Conference Papers
By manipulating the search space via LBDFs, our method reliably discovers new non-dominated solutions and improves Pareto front quality across standard multi-objective benchmarks.
Coelho V., Papetti D.M., Tangherloni A., Cazzaniga P., Besozzi D., Nobile M.S. (2023). The Domination Game: Dilating Bubbles to Fill Up Pareto Fronts. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
We propose GP4DFs, a Genetic Programming method that automatically evolves effective Dilation Functions to manipulate the fitness landscape and improve the optimization process.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2022Conference Papers
To address the complexity of selecting minimal yet discriminative marker panels, we model the task as a bi-objective optimization problem and evaluate multi-objective algorithms that outperform genetic algorithms in both quality and consistency.
Tangherloni A., Riva S.G., Myers B., Cazzaniga P. (2022). Multi-objective Optimization for Marker Panel Identification in Single-cell Data. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2022Conference Papers
scALPO is a novel automated pipeline that employs an LSTM neural network to assign cell types based solely on marker genes, surpassing current state-of-the-art tools in annotation accuracy.
Riva S.G., Myers B., Cazzaniga P., Tangherloni A.(2022).A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
SMGen automatically generates synthetic yet realistic models of biochemical reaction networks to benchmark simulation and analysis tools in computational systems biology.
Expert Systems with Applications2022Journal Articles
A critical review of the Salp Swarm Optimization algorithm that exposes its mathematical flaws, proposes a corrected variant (ASSO), and questions its advantages over classic metaheuristics.
Castelli M., Manzoni L., Mariot L., Nobile M.S., Tangherloni A. (2022). Salp Swarm Optimization: a Critical Review. Expert Systems with Applications, 189: 116029.
Riva S.G., Cazzaniga P., Tangherloni A. (2021). Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space. In IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2155-2162, IEEE.
FiCoS is a GPU-powered deterministic simulator combining fine- and coarse-grained parallelization to simulate large-scale biochemical models with dramatic speed-ups.
IEEE Congress on Evolutionary Computation (CEC)2021Conference Papers
To address the NP-hard challenge of marker panel identification, we introduce and compare three GA representations, demonstrating that more flexible encodings yield the most effective panels, especially in 0-knowledge scenarios.
Tangherloni A., Riva S.G., Spolaor S., Besozzi D., Nobile M.S., Cazzaniga P. (2021). The impact of representation on the optimization of marker panels for single-cell RNA data. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
Tangherloni A., Ricciuti F., Besozzi D., Liò P., Cvejic A. (2021). Analysis of single-cell RNA sequencing data based on autoencoders. BMC Bioinformatics, 22(1): 309.
An integrative single-cell RNA-Seq and ATAC-Seq analysis of human developmental hematopoiesis that reveals epigenetic priming of stem cells prior to lineage commitment.
Ranzoni A.M., Tangherloni A., Berest I., Riva S.G., Myers B., Strzelecka P.M., Xu J., Panada E., Mohorianu I., Zaugg J.B., Cvejic A. (2021). Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis. Cell Stem Cell, 28(3): 472-487.
Rundo L., Tangherloni A., Cazzaniga P., Mistri M., Galimberti S., Woitek R., Sala E., Mauri G., Nobile M.S. (2021). A CUDA-powered method for the feature extraction and unsupervised analysis of medical images. The Journal of Supercomputing, 77(8): 8514-8531.
ACDC is an automated method for detecting and counting fluorescently labeled cell nuclei in time-lapse microscopy, without relying on large annotated datasets.
Neural Approaches to Dynamics of Signal Exchanges (Springer)2020Books
A study of the generalization ability of CNNs (SegNet, U-Net, pix2pix) for prostate central-gland and peripheral-zone segmentation across two multi-centric MRI datasets.
Rundo L., Han C., Zhang J., Hataya R., Nagano Y., Militello C., Ferretti C., Nobile M.S., Tangherloni A., Gilardi M.C., Vitabile S., Nakayama H., Mauri G. (2020). CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study. In Neural Approaches to Dynamics of Signal Exchanges, 151: 269-280, Springer.
A survey showing how Computational Intelligence methods can solve complex optimization problems in life sciences, from protein folding to parameter estimation.
Totis N., Tangherloni A., Beccuti M., Cazzaniga P., Nobile M.S., Besozzi D., Pennisi M., Pappalardo F. (2020). Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization. In Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), Lecture Notes in Computer Science, 11925: 187-202, Springer.
International Conference on Parallel Computing Technologies (PaCT)2019Conference Papers
HaraliCU is a GPU-powered method for Haralick feature extraction on medical images that exploits the full dynamics of gray-scale levels without requantization.
Rundo L., Tangherloni A., Galimberti S., Cazzaniga P., Woitek R., Sala E., Nobile M.S., Mauri G. (2019). HaraliCU: GPU-powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels. In International Conference on Parallel Computing Technologies (PaCT), Lecture Notes in Computer Science, 11657: 304-318, Springer.
We show that benchmark functions do not fully capture real-world optimization difficulty, using biochemical parameter estimation as a case study and highlighting the impact of solution representation.
Tangherloni A., Spolaor S., Cazzaniga P., Besozzi D., Rundo L., Mauri G., Nobile M.S. (2019). Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design. Applied Soft Computing, 81: 105494.
Rundo L., Tangherloni A., Cazzaniga P., Nobile M.S., Russo G., Gilardi M.C., Vitabile S., Mauri G., Besozzi D., Militello C. (2019). A novel framework for MR image segmentation and quantification by using MedGA. Computer Methods and Programs in Biomedicine, 176: 159-172.
Expert Systems with Applications2019Journal Articles
MedGA is a Genetic Algorithm-based image enhancement method for images with a bimodal gray-level histogram, applied to contrast-enhanced MR image analysis.
Rundo L., Tangherloni A., Nobile M.S., Militello C., Besozzi D., Mauri G., Cazzaniga P. (2019). MedGA: A Novel Evolutionary Method for Medical Image Enhancement in Medical Imaging Systems. Expert Systems with Applications, 119: 387-399.
GenHap is a Genetic Algorithm-based method for haplotype assembly that yields accurate solutions and is substantially faster than state-of-the-art phasing tools.
Tangherloni A., Spolaor S., Rundo L., Nobile M.S., Cazzaniga P., Mauri G., Liò P., Merelli I., Besozzi D. (2019). GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly. BMC Bioinformatics, 20(Suppl 4): 172.
Beccuti M., Cazzaniga P., Pennisi M., Besozzi D., Nobile M.S., Pernice S., Russo G., Tangherloni A., Pappalardo F. (2019). GPU accelerated analysis of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis. In Euro-Par 2018: Parallel Processing Workshops, Lecture Notes in Computer Science, 11339: 626-637, Springer.
Tangherloni A., Rundo L., Spolaor S., Nobile M.S., Merelli I., Besozzi D., Mauri G., Cazzaniga P., Liò P. (2019). High Performance Computing for Haplotyping: Models and Platforms. In Euro-Par 2018: Parallel Processing Workshops, Lecture Notes in Computer Science, 11339: 650-661, Springer.
Spolaor S., Tangherloni A., Rundo L., Cazzaniga P., Nobile M.S. (2019). Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO’s Performance. In Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), Lecture Notes in Computer Science, 10834: 92-102, Springer.
IEEE Congress on Evolutionary Computation (CEC)2018Conference Papers
To address the challenge of estimating kinetic parameters in biochemical models, we benchmark seven state-of-the-art optimization techniques and find that a settings-free FST-PSO variant yields the most robust and accurate performance across diverse systems.
Nobile M.S., Tangherloni A., Rundo L., Spolaor S., Besozzi D., Mauri G., Cazzaniga P. (2018). Computational Intelligence for Parameter Estimation of Biochemical Systems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
Euromicro International Conference on Parallel, Distributed, and Network-based Processing (PDP)2018Conference Papers
MS2PSO is a parallel and distributed multi-swarm PSO, powered by GPU simulation, for estimating reaction constants in biological models across multiple experimental conditions.
Tangherloni A., Rundo L., Spolaor S., Cazzaniga P., Nobile M.S. (2018). GPU-Powered Multi-Swarm Parameter Estimation of Biological Systems: A Master-Slave Approach. In IEEE 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 698-705, IEEE.
Rundo L., Militello C., Tangherloni A., Russo G., Vitabile S., Gilardi M.C., Mauri G. (2018). NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique. International Journal of Imaging Systems and Technology, 28(1): 21-37.
Quantifying and Processing Biomedical and Behavioral Signals (Springer)2018Books
Computer-assisted approaches for uterine fibroid segmentation in MRgFUS treatments, with a quantitative evaluation of the methods and a clinical feasibility analysis.
Rundo L., Militello C., Tangherloni A., Russo G., Lagalla R., Mauri G., Gilardi M.C., Vitabile S. (2018). Computer-assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis. In Quantifying and Processing Biomedical and Behavioral Signals, Smart Innovation, Systems and Technologies, 103: 229-241, Springer.
Quantitative Biology: Theory, Computational Methods, and Models (MIT Press)2018Books
A chapter on accelerating stochastic simulations of mechanistic biological models on GPUs, discussing the advantages and the practical issues of parallelization.
Citation
Cazzaniga P., Nobile M.S., Tangherloni A., Besozzi D. (2018). Accelerating stochastic simulations of mechanistic models of biological systems: Advantages and issues in the parallelization on Graphics Processing Units. In Quantitative Biology: Theory, Computational Methods, and Models, 423-440, MIT Press.
Nobile M.S., Cazzaniga P., Tangherloni A., Besozzi D. (2017). Graphics Processing Units in Bioinformatics, Computational Biology and Systems Biology. Briefings in Bioinformatics, 18(5): 870-885.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)2017Conference Papers
Three reboot strategies for PSO that reinitialize particle positions to avoid local optima in the parameter estimation of biochemical systems, accelerated with GPU simulation.
Spolaor S., Tangherloni A., Rundo L., Nobile M.S., Cazzaniga P. (2017). Reboot Strategies in Particle Swarm Optimization and their Impact on Parameter Estimation of Biochemical Systems. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Congress on Evolutionary Computation (CEC)2017Conference Papers
To address PSO’s sensitivity to parameter settings, we propose PPSO, a fuzzy logic–driven algorithm that proactively adapts parameters for each particle and improves convergence and performance across high-dimensional benchmarks.
Tangherloni A., Rundo L., Nobile M.S. (2017). Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
LASSIE is a black-box GPU-accelerated deterministic simulator designed for large-scale biochemical models, achieving up to 92× speed-up over CPU integration.
Tangherloni A., Nobile M.S., Besozzi D., Mauri G., Cazzaniga P. (2017). LASSIE: simulating large-scale models of biochemical systems on GPUs. BMC Bioinformatics, 18(1): 246.
A parallel implementation of Gillespie’s Stochastic Simulation Algorithm on the Intel Many Integrated Core (Xeon Phi) coprocessor to accelerate stochastic simulations of biochemical networks.
Tangherloni A., Nobile M.S., Cazzaniga P., Besozzi D., Mauri G. (2017). Gillespie’s Stochastic Simulation Algorithm on MIC Coprocessor. The Journal of Supercomputing, 73(2): 676-686.
Rundo L., Tangherloni A., Militello C., Gilardi M.C., Mauri G. (2016). Multimodal medical image registration using Particle Swarm Optimization: A review. In IEEE Symposium Series on Computational Intelligence (SSCI), IEEE.
Tangherloni A., Nobile M.S., Cazzaniga P. (2016). GPU-powered bat algorithm for the parameter estimation of biochemical kinetic values. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
IEEE Congress on Evolutionary Computation (CEC)2016Conference Papers
TTo address the computational demands of biochemical parameter estimation, we combine PPSO with GPU-accelerated ODE simulation and demonstrate that PPSO outperforms standard PSO in runtime while achieving similar parameter-fitness quality.
Nobile M.S., Tangherloni A. Besozzi D., Cazzaniga P. (2016). GPU-powered and settings-free parameter estimation of biochemical systems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
