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Published: August 14, 2015

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Published: August 14, 2014

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Published: August 14, 2013

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Published: August 14, 2012

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publications

GPU-powered and settings-free parameter estimation of biochemical systems

Published in IEEE Congress on Evolutionary Computation (CEC), 2016

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.

Recommended citation: 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.
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GPU-powered bat algorithm for the parameter estimation of biochemical kinetic values

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016

A Lévy-flight Bat Algorithm combined with GPU simulation outperforms PSO in accuracy and speed for biochemical parameter estimation.

Recommended citation: 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.
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Multimodal medical image registration using Particle Swarm Optimization: A review

Published in IEEE Symposium Series on Computational Intelligence (SSCI), 2016

A critical review of biomedical image registration using Particle Swarm Optimization and its hybridizations with Evolutionary Strategies.

Recommended citation: 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.
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Gillespie’s Stochastic Simulation Algorithm on MIC Coprocessor

Published in The Journal of Supercomputing, 2017

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.

Recommended citation: 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.
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LASSIE: simulating large-scale models of biochemical systems on GPUs

Published in BMC Bioinformatics, 2017

LASSIE is a black-box GPU-accelerated deterministic simulator designed for large-scale biochemical models, achieving up to 92× speed-up over CPU integration.

Recommended citation: 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.
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Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems

Published in IEEE Congress on Evolutionary Computation (CEC), 2017

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.

Recommended citation: 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.
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Reboot Strategies in Particle Swarm Optimization and their Impact on Parameter Estimation of Biochemical Systems

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2017

Three reboot strategies for PSO that reinitialize particle positions to avoid local optima in the parameter estimation of biochemical systems, accelerated with GPU simulation.

Recommended citation: 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.
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Graphics Processing Units in Bioinformatics, Computational Biology and Systems Biology

Published in Briefings in Bioinformatics, 2017

A review of GPU-powered tools for Bioinformatics, Computational Biology, and Systems Biology, discussing their advantages and drawbacks.

Recommended citation: 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.
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Accelerating stochastic simulations of mechanistic models of biological systems: Advantages and issues in the parallelization on Graphics Processing Units

Published in Quantitative Biology: Theory, Computational Methods, and Models (MIT Press), 2018

A chapter on accelerating stochastic simulations of mechanistic biological models on GPUs, discussing the advantages and the practical issues of parallelization.

Recommended 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.

Computer-assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis

Published in Quantifying and Processing Biomedical and Behavioral Signals (Springer), 2018

Computer-assisted approaches for uterine fibroid segmentation in MRgFUS treatments, with a quantitative evaluation of the methods and a clinical feasibility analysis.

Recommended citation: 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.
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NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Published in International Journal of Imaging Systems and Technology, 2018

NeXt is a fully automatic method based on Fuzzy C-Means for extracting necrotic regions in brain tumor MRI to support neuro-radiosurgery.

Recommended citation: 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.
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GPU-Powered Multi-Swarm Parameter Estimation of Biological Systems: A Master-Slave Approach

Published in Euromicro International Conference on Parallel, Distributed, and Network-based Processing (PDP), 2018

MS2PSO is a parallel and distributed multi-swarm PSO, powered by GPU simulation, for estimating reaction constants in biological models across multiple experimental conditions.

Recommended citation: 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.
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Computational Intelligence for Parameter Estimation of Biochemical Systems

Published in IEEE Congress on Evolutionary Computation (CEC), 2018

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.

Recommended citation: 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.
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Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO’s Performance

Published in International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), 2019

Reboot strategies for Particle Swarm Optimization that help avoid local optima when estimating kinetic reaction constants of biochemical models.

Recommended citation: 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.
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High Performance Computing for Haplotyping: Models and Platforms

Published in Euro-Par 2018: Parallel Processing Workshops (Springer), 2019

A review of models and high-performance computing platforms for the haplotype assembly problem.

Recommended citation: 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.
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GPU accelerated analysis of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis

Published in Euro-Par 2018: Parallel Processing Workshops (Springer), 2019

GPU-accelerated analysis of the cross regulation between regulatory and effector T cells in relapsing-remitting multiple sclerosis.

Recommended citation: 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.
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GenHap: A Novel Computational Method Based on Genetic Algorithms for Haplotype Assembly

Published in BMC Bioinformatics, 2019

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.

Recommended citation: 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.
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MedGA: A Novel Evolutionary Method for Medical Image Enhancement in Medical Imaging Systems

Published in Expert Systems with Applications, 2019

MedGA is a Genetic Algorithm-based image enhancement method for images with a bimodal gray-level histogram, applied to contrast-enhanced MR image analysis.

Recommended citation: 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.
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A novel framework for MR image segmentation and quantification by using MedGA

Published in Computer Methods and Programs in Biomedicine, 2019

A novel evolutionary framework that uses MedGA as a pre-processing stage to improve the enhancement and segmentation of bimodal MR images.

Recommended citation: 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.
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Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

Published in Applied Soft Computing, 2019

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.

Recommended citation: 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.
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HaraliCU: GPU-powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels

Published in International Conference on Parallel Computing Technologies (PaCT), 2019

HaraliCU is a GPU-powered method for Haralick feature extraction on medical images that exploits the full dynamics of gray-scale levels without requantization.

Recommended citation: 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.
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USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Published in Neurocomputing, 2019

USE-Net incorporates Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation, achieving strong cross-dataset generalization on multi-institutional MRI.

Recommended citation: Rundo L., Han C., Nagano Y., Zhang J., Hataya R., Militello C., Tangherloni A., Nobile M.S., Ferretti C., Besozzi D., Gilardi M.C., Vitabile S., Mauri G., Nakayama H., Cazzaniga P. (2019). USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing, 365: 31-43.
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Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization

Published in International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), 2020

An efficient, settings-free strategy for calibrating detailed kinetic metabolic models, including the characterization of enzyme isoforms.

Recommended citation: 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.
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Computational Intelligence for Life Sciences

Published in Fundamenta Informaticae, 2020

A survey showing how Computational Intelligence methods can solve complex optimization problems in life sciences, from protein folding to parameter estimation.

Recommended citation: Besozzi D., Manzoni L., Nobile M.S., Spolaor S., Castelli M., Vanneschi L., Cazzaniga P., Ruberto S., Rundo L., Tangherloni A. (2020). Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4): 57-80.
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CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

Published in Neural Approaches to Dynamics of Signal Exchanges (Springer), 2020

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.

Recommended citation: 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.
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ACDC: Automated cell detection and counting for time-lapse fluorescence microscopy

Published in Applied Sciences, 2020

ACDC is an automated method for detecting and counting fluorescently labeled cell nuclei in time-lapse microscopy, without relying on large annotated datasets.

Recommended citation: Rundo L., Tangherloni A., Tyson D.R., Betta R., Militello C., Spolaor S., Nobile M.S., Besozzi D., Lubbock A.L.R., Quaranta V., Mauri G., Lopez C.F., Cazzaniga P. (2020). ACDC: Automated cell detection and counting for time-lapse fluorescence microscopy. Applied Sciences, 10(18): 6187.
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A CUDA-powered method for the feature extraction and unsupervised analysis of medical images

Published in The Journal of Supercomputing, 2021

CHASM is a GPU-accelerated method for Haralick feature extraction and self-organizing-map analysis of medical images.

Recommended citation: 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.
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Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis

Published in Cell Stem Cell, 2021

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.

Recommended citation: 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.
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Analysis of single-cell RNA sequencing data based on autoencoders

Published in BMC Bioinformatics, 2021

scAEspy is a unifying autoencoder-based tool for the low-dimensional representation and integration of single-cell RNA-Seq datasets.

Recommended citation: 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.
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The impact of representation on the optimization of marker panels for single-cell RNA data

Published in IEEE Congress on Evolutionary Computation (CEC), 2021

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.

Recommended citation: 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.
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FiCoS: a fine- and coarse-grained GPU-powered deterministic simulator for biochemical networks

Published in PLoS Computational Biology, 2021

FiCoS is a GPU-powered deterministic simulator combining fine- and coarse-grained parallelization to simulate large-scale biochemical models with dramatic speed-ups.

Recommended citation: Tangherloni A., Nobile M.S., Cazzaniga P., Capitoli G., Spolaor S., Rundo L., Mauri G., Besozzi D. (2021). FiCoS: a fine- and coarse-grained GPU-powered deterministic simulator for biochemical networks. PLoS Computational Biology, 17(9): e1009410.
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Integration of Multiple scRNA-Seq Datasets on the Autoencoder Latent Space

Published in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021

A novel Autoencoder-based strategy for integrating multiple scRNA-Seq datasets that outperforms Scanorama, Ingest, and Seurat in most cases.

Recommended citation: 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.
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Salp Swarm Optimization: a Critical Review

Published in Expert Systems with Applications, 2022

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.

Recommended citation: Castelli M., Manzoni L., Mariot L., Nobile M.S., Tangherloni A. (2022). Salp Swarm Optimization: a Critical Review. Expert Systems with Applications, 189: 116029.
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SMGen: A generator of synthetic models of biochemical reaction networks

Published in Symmetry, 2022

SMGen automatically generates synthetic yet realistic models of biochemical reaction networks to benchmark simulation and analysis tools in computational systems biology.

Recommended citation: Riva S.G., Cazzaniga P., Nobile M.S., Spolaor S., Rundo L., Besozzi D., Tangherloni A. (2022). SMGen: A generator of synthetic models of biochemical reaction networks. Symmetry, 14(1): 119.
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A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2022

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.

Recommended citation: 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.
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Multi-objective Optimization for Marker Panel Identification in Single-cell Data

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2022

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.

Recommended citation: 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.
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Simplifying Fitness Landscapes using Dilation Functions evolved with Genetic Programming

Published in IEEE Computational Intelligence Magazine, 2023

We propose GP4DFs, a Genetic Programming method that automatically evolves effective Dilation Functions to manipulate the fitness landscape and improve the optimization process.

Recommended citation: Papetti D.M., Tangherloni A., Farinati D., Cazzaniga P., Vanneschi L. (2023). Simplifying Fitness Landscapes using Dilation Functions evolved with Genetic Programming. IEEE Computational Intelligence Magazine, 18(1): 22-31.
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The Domination Game: Dilating Bubbles to Fill Up Pareto Fronts

Published in IEEE Congress on Evolutionary Computation (CEC), 2023

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.

Recommended citation: 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.
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Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2023

We introduce a pseudo-voting consensus approach within scALPO that combines multiple clustering outputs to achieve more robust and accurate cell-type annotation.

Recommended citation: 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.
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MAGNETO: Marker pAnels GeNEraTor with multi-Objective optimization

Published in Journal of Biomedical Informatics, 2023

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.

Recommended citation: Tangherloni A., Riva S.G., Myers B., Buffa F.M., Cazzaniga P. (2023). MAGNETO: Marker pAnels GeNEraTor with multi-Objective optimization. Journal of Biomedical Informatics, 147: 104510.
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A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems

Published in IEEE Congress on Evolutionary Computation (CEC), 2024

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.

Recommended citation: 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.
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A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2024

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.

Recommended citation: 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.
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Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2024

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.

Recommended citation: 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.
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Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation

Published in River Publishers, 2025

A step-by-step guide to the analysis and interpretation of biomedical data, covering the computational and statistical methods used in modern biomedical research.

Recommended 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.

We Are Sending You Back… to the Optimum! Fuzzy Time Travel Particle Swarm Optimization

Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025

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.

Recommended citation: 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.
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GENESIS: Generating scRNA-Seq data from Multiome Gene Expression

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2025

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.

Recommended citation: 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.
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Knowledge-Enriched Cell-Type Annotation in Single-Cell Transcriptomics via LLM Embeddings

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2025

We show that embedding biological knowledge through Modern-BERT gene vectors strengthens supervised models for scRNA-seq cell-type classification.

Recommended citation: 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.
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Now You See Me, Now You Don’t: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos

Published in IEEE/CVF International Conference on Computer Vision (ICCV), 2025

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.

Recommended citation: 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.
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EvoGrad: Accelerated Metaheuristics in a Differentiable Wonderland

Published in IEEE Congress on Evolutionary Computation (CEC), 2026

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.

Recommended citation: 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.
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