Multi-task deep latent spaces for cancer survival and drug sensitivity prediction

• Journal: Bioinformatics
• Date:
• Category: publication

CIBB 2024 was great!

The 19th conference on Computational Intelligence methods for Bioinformatics and Biostatistics has just finished and was a great success! Find more info (and pictures) at http://cibb2024.unisannio.it!

• Date:
• Category: news

Altered centriolar cohesion by CEP250 and appendages impact outcome of patients with pancreatic cancer

• Journal: Pancreatology
• Date:
• Category: publication

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data

• Journal: Briefings in Bioinformatics
• Date:
• Category: publication

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions expression data

• Journal: Journal of Translational Medicine
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• Category: publication

We are hosting CIBB 2024!

We are organizing the 19th conference on Computational Intelligence methods for Bioinformatics and Biostatistics. Find more info at http://cibb2024.unisannio.it!

• Date:
• Category: news

Luigi Cerulo

• Position: Lab Director

I started doing research as a job around the beginning of the new millennium, focusing on Software Engineering, particularly in software maintenance and evolution. I approached research and teaching at RCOST, a highly intellectually stimulating Research Centre on Software Technology at University of Sannio under the supervision of Gerardo Canfora. From 2003 to 2009, during my PhD and postdoc, I worked mainly on Mining Software Repositories, which was an emerging and very exciting topic in empirical software engineering. Large data repositories on software changes and bug fixes allowed quantitative studies and let researchers to induce new hypothesis and theories or to support/refute existing hypothesis from data. As a main contribution I proposed approaches, still cited in the literature, able to suggest which part of a software system needs to be fixed and who is the best candidate developer to achieve such a task. In 2017, a study on clone evolution, conducted ten years before, received the most influential award.

Around 2008, when the human genome had just been almost completely sequenced and made available, I joined Michele Ceccarelli which was approaching bioinformatics and was determined to introduce quantitative methods in a traditional department of biology. We created the conditions to let grow a research group in bioinformatics at University of Sannio. Since than I focused mainly on the problem of reconstructing gene regulatory networks from transcriptional data applied to molecular cancer research. Many of our students got relevant positions in both accademia and industry.

Currently I co-lead the computational biology research group of University of Sannio and manage research fundings of over 1M Euro. I’m interested to unveil the potential of recent machine learning algorithms, especially deep learning architectures, to address relevant biological problems and propose novel biological research hypotheses in cancer bioinformatics and computational genomics. At University of Sannio I teach courses on computational biology and genomics, computer programming, and machine learning in biology.

I authored over 80 papers appeared in referred international journals, conferences and workshops, I’m member of the Editorial boards of BMC Bioinformatics and Journal of Translational Medicine, and recently I was the co-general-chair of the 19th conference on
Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2024).

Francesco Napolitano

• Position: Lab Director
• GitHub: https://github.com/franapoli

F.N. is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno) and Ph.D. (University of Salerno and University of California, Irvine) on supervised and unsupervised data analysis, particularly for complex data clustering. Early experience with biological applications came with bioinformatic analysis of cancer transcriptomic and genomic data (University of Chieti-Pescara). Related interest in pharmacological therapy induced F.N. to explore the area of Chemoinformatics. In this regard, he studied the use of automatic classification for drug repositioning based on integrated transcriptomic and bio-chemical data (University of Helsinki, Finland). Subsequently, at the Telethon Institute of Genetics and Medicine (TIGEM), he focused on the development of computational methods for the identification of small molecules inducing cell-type specific transcriptional programs, with applications to generative medicine and differentiation therapy in cancer. He then joined as a Research Scientist the King Abdullah University of Science and Technology (KAUST) where he applied Deep Learning models to integrated omics data in the context of Drug Discovery and Metabolic Disease Modeling. He is now an Assistant Professor at the University of Sannio, Benevento, Italy, where he teaches Principles of Computer Science and Bioinformatics at Bachelor and Master courses of Biotechnology and Biological Sciences. F.N. developed and published software tools in the areas of Data Analysis and Reproducible Research.

Marco Nigro

• Position: Postdoctoral Fellow
• GitHub: https://github.com/MarcoN16

Marco was born in Brescia, Italy, and moved to Milan to pursue a bachelor’s degree in Pharmaceutical Biotechnology. During this time, he became fascinated by the field of neuroscience, which led him to pursue a master’s degree in Neurobiology (University of Pavia) and a Ph.D. in Neuroscience and Neurotechnologies (Italian Institute of Technology). Throughout these periods, Marco participated in various research projects, starting with the study of energy metabolism proteins and cellular plasticity in learned helpless rats. He then transitioned to analyzing different isoforms of receptors following transient ischemic attacks and conducting extracellular recordings in freely moving animals during social behavior paradigms in mouse models for psychiatric disorders. Later, he briefly worked as a postdoc researcher in a drug development lab focused on Fragile X syndrome. Afterward, he joined the Yang Lab at the University of California, Riverside, to study the roles of the prefrontal cortex and norepinephrine in cognitive flexibility. Recently, he joined Napolitano’s lab at the University of Sannio to study drug repositioning for Retinitis Pigmentosa using artificial intelligence applied to single-cell transcriptomics.

Antonio Ammendola

• Position: PhD student
• GitHub: https://github.com/napolitanodst/Antonio

Antonio Ammendola graduated from the University of Naples Federico II with a Master Degree in Molecular Biology with a thesis titled “Integrated Omics Strategies in a Case of Kabuki Syndrome”. The thesis focused on resolving a case of an individual with a rare genetic disease and, more generally, on the application of omics strategies for the diagnosis of such diseases. His PhD project at University of Sannio concerns the use of Artificial Intelligence in the study of epigenetic diseases.

Luca Faretra

• Position: PhD Student

Master Thesis abstract: Alternative Splicing (AS) is an important step during maturation of primary transcripts as it allows to have different mRNA molecules from the same gene. These molecules code for different proteins that can have similar functions, but can also assume completely different functions. Based on this, to make a more accurate cell expression analysis, alternative splicing must be considered. Currently, there is a huge amount of tools that can analyze alternative splicing starting from RNA-Seq data and new tools are also constantly developed, but at the same time there is a lack of studies in which these tools are evaluated together, making it very difficult to choose the appropriate tool. In literature, AS tools are generally analyzed only with one RNA-Seq example, without considering that results may change depending on differing sequencing parameters, such as sequencing errors, read length, library size, etc. This is a limitation because various conditions may affect tool performance. In this thesis we developed a pipeline that permits to have near real RNA-Seq data with controlled parameters and we used it to test some event-based AS tools in order to observe how these affect performances. Essentially, our purpose is to contribute to enhancing the understanding and selection of AS tools for future RNA-Seq analyses. The findings highlight that various parameters may impact on the performance of event-based AS tools. The impact of these parameters varies depending on both the type of tool employed and the specific type of splicing event under analysis. Notably is the effect of fragment length on SUPPA’s and rMATS’s performance. While SUPPA outperforms rMATS for each type of event with short fragments, it exhibits a noticeable performance decline with longer fragments, rendering it inferior to rMATS in such instances. This decrease, however, is noticeable only in specific splicing events, particularly in ES, where SUPPA goes from an F-Score 17.20\% higher than that of rMATS (0.69 ± 0.007 vs. 0.59 ± 0.01) to a value 8.96\% lower (0.55 ± 0.01 vs. 0.61 ± 0.006). This emphasizes the criticality of selecting the appropriate tool based on the specific characteristics of starting data and the objectives of AS analysis.

Diana Parente

• Position: Intern
• GitHub: https://github.com/napolitanodst/diana

Intern project: In collaboration with the National Cancer Institute “Fondazione Pascale” - Cancer Research Center, identification of Cdk9 inhibitory drugs through machine learning-based computational screening.

Felicita Masone

• Position: Bachelor student
• GitHub: https://github.com/orgs/napolitanodst/people/Felicita-Mas

Bachelor Thesis: This thesis focuses on developing a method, called RGDIST, to identify genes that are responsive to drug treatments. RGDIST is based on the assumption that these genes should show the effects of treatment more prominently in the cells closest to the site of drug injection. The study was applied to a spatial RNAseq dataset obtained after injections of heme in the mouse brain, with the aim of simulating intracerebral hemorrhage and evaluating the transcriptional effects of free heme in brain tissue. RGDIST was used to identify genes that are responsive to treatment and proved to be effective in detecting these genes more sensitively than other methods. The significant genes extracted with RGDIST are biologically relevant and consistent with the expected effects of the presence of free hemoglobin and heme.

Donatella Pierri

• Position: Bachelor student

Bachelor Thesis title: Studio del centrosoma attraverso dati disponibili di trascrittomica

Bachelor Thesis abstract: Questa tesi si propone di approfondire la comprensione dei meccanismi legati al centrosoma attraverso l’impiego della bioinformatica, concentrandosi in particolare sui programmi trascrizionali coinvolti. Al fine di raggiungere questo obiettivo, verranno identificati data set, di cui saranno fatte analisi preliminari, per individuare correlazioni significative e tendenze nei dati. Tale approccio consentirà di ottenere una visione più completa e dettagliata dei processi molecolari che regolano il centrosoma e la sua interazione con i programmi trascrizionali. Attraverso l’analisi bioinformatica dei dati di sequenziamento dell’mRNA, si potrebbero identificare varianti di splicing associate al centrosoma e studiarne l’impatto sulla biologia cellulare.

Francesco Saccomando Ciaramella

• Position: Intern, Bachelor student
• GitHub: https://github.com/orgs/napolitanodst/people/BioFrsk

Bachelor thesis abstract: The thesis focuses on revDECCODE, a computational method for identifying small molecules through differential profiles, for integration into standard therapies for glioblastoma (GBM) treatment. Glioblastoma represents one of the most aggressive forms of brain cancer, with limited therapeutic options and a poor prognosis. The first chapter introduces the biological context of glioblastoma, highlighting its molecular complexity. The second chapter explores the application of transcriptomics in the context of drug discovery against cancer, specifically targeting GBM. In the third chapter, the revDECCODE method is presented as an approach for identifying small molecules with therapeutic potential in glioblastoma. Through the analysis of differential profiles, revDECCODE enables the extraction of molecules that may act synergistically with standard therapies, improving treatment efficacy and reducing pharmacological resistance. The integration of methods like revDECCODE into therapeutic strategies for glioblastoma offers new perspectives for addressing this devastating disease. The thesis concludes by emphasizing the importance of omics research and analyzing potential future scenarios.

Internship project: Development of a geological dataset for the training of machine learning models in Google TensorFlow.