You can also find my articles on my Google Scholar profile.
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.
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.
We propose GP4DFs, a Genetic Programming method that automatically evolves effective Dilation Functions to manipulate the fitness landscape and improve the optimization process.
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.
SMGen automatically generates synthetic yet realistic models of biochemical reaction networks to benchmark simulation and analysis tools in computational systems biology.
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.
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.
FiCoS is a GPU-powered deterministic simulator combining fine- and coarse-grained parallelization to simulate large-scale biochemical models with dramatic speed-ups.
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.
scAEspy is a unifying autoencoder-based tool for the low-dimensional representation and integration of single-cell RNA-Seq datasets.
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.
CHASM is a GPU-accelerated method for Haralick feature extraction and self-organizing-map analysis of medical images.
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.
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.
A survey showing how Computational Intelligence methods can solve complex optimization problems in life sciences, from protein folding to parameter estimation.
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.
USE-Net incorporates Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation, achieving strong cross-dataset generalization on multi-institutional MRI.
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.
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.
A novel evolutionary framework that uses MedGA as a pre-processing stage to improve the enhancement and segmentation of bimodal MR images.
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.
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.
NeXt is a fully automatic method based on Fuzzy C-Means for extracting necrotic regions in brain tumor MRI to support neuro-radiosurgery.
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.
A review of GPU-powered tools for Bioinformatics, Computational Biology, and Systems Biology, discussing their advantages and drawbacks.
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.
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.
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