Training and Research

PhD Programme Courses/classes - 2023/2024

Clinical Biochemistry and Clinical Molecular Biology

Credits: 5

Language: Italiano

Teacher:  Giuseppe Lippi

Virus oncogeni

Credits: 2

Language: Italiano

Teacher:  Davide Gibellini

Oral Pathology

Credits: 2

Language: Italiano/English

Teacher:  Dario Bertossi

Experimental approaches to study FGFR3-related diseases

Credits: 2

Language: English

Teacher:  Patricia Lievens

Meccanismi molecolari nei processi di differenziamento staminale

Credits: 2,5

Language: Italiano

Teacher:  Maria Teresa Valenti

Emerging viruses and zoonosis

Credits: 3

Language: Italiano

Teacher:  Donato Zipeto

Corso di approfondimento sul virus dell'Immunodeficienza umana HIV

Credits: 2,5

Language: English

Teacher:  Alessandra Ruggiero

Corso di OMICA applicato alla medicina

Credits: 5

Language: English

Teacher:  Alessandra Ruggiero

Bioinformatics applied to genomics

Credits: 2

Language: Italiana e Inglese

Teacher:  Maria Romanelli

Epidemiologic Methodology

Credits: 3

Language: Italiano/English

Teacher:  Maria Elisabetta Zanolin

Il disegno degli studi osservazionali

Credits: 3

Language: Italiano/English

Teacher:  Maria Elisabetta Zanolin

Nuove strategie per combattere la resistenza agli antibiotici

Credits: 2

Language: Italiano

Teacher:  Annarita Mazzariol

Translational research in long COVID

Credits: 1,5

Language: Italiano / Inglese

Teacher:  Evelina Tacconelli

R avanzanto per gli studi del Genoma umano

Credits: 3

Language: English or Italian

Teacher:  Giovanni Malerba

Global Health: Determinanti di salute, fattori di rischio, stili di vita e disuguaglianze in sanità

Credits: 2

Language: Italiano

Teacher:  Stefano Tardivo

Developmental encephalopathies from diagnosis to precision medicine

Credits: 1

Language: Italiano

Teacher:  Francesca Darra

Citometria a flusso: principi ed applicazioni

Credits: 2

Language: Italiano/English

Teacher:  Maria Scupoli

Genetics of complex traits

Credits: 2

Language: English

Teacher:  Giovanni Malerba

central auditory deseses

Credits: 2,5

Language: Italiano

Teacher:  Luca Sacchetto

Bench to bedside infectious diseases - the immunocompromised patient

Credits: 1

Language: Italiano/English

Teacher:  Elda Righi

Oral microbiota and cancer: from clinic to research

Credits: 2,5

Language: Italiano

Teacher:  Nicoletta Zerman

PhD school courses/classes - 2023/2024

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.

Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, no confirmation e-mail will be sent after signing up.

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

Credits

2

Language

English

Class attendance

Free Choice

Location

VERONA

Learning objectives

An introduction to the fundamentals of generalized regression models will be given in this course, with a focus on models for count, binary, and categorical data. These types of response variables are widely used in industrial applications as well as observational and experimental research.
Upon successful completion of the course, students will be able to:
• Describe the general structure of a GLM and similarities and differences with linear models
• Estimate and interpret a logistic regression model
• Estimate and interpret a Poisson regression model
• Know of issues and some strategies for dealing with overdispersion in some generalised linear models (GLMs)

Prerequisites and basic notions

This course assumes a good understanding of probability and mid-level knowledge of linear regression theory.

Program

The course covers methods for regression analysis of responses that do not follow the normal distribution, especially of discrete responses. We will learn to understand some of the common statistical methods for fitting regression models to such data. In particular, we will consider logistic regression, Poisson regression and log-linear models. The lecture focuses on the development, theoretical justification, and interpretation of these methods.

When and where

Teaching forms mainly consist of lectures (8h) and exercises proposed by the teacher. The teaching material (slides of the theoretical lessons) is made available to the students on the e-learning web page of the course (Moodle platform). Lessons will be delivered via Zoom. Full attendance is required.

Learning assessment procedures

There is no exam

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

There is no exam, hence there is also no definition of the evaluation criteria.

Criteria for the composition of the final grade

There is no grade because there is no exam.

Scheduled Lessons

When Classroom Teacher topics
Tuesday 19 March 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to generalised linear models. Review of the general linear model: assumptions of the linear model (independence of observations, homoskedasticity of errors, linearity of coeffiicents) , least squares estimation and maximum likelihood estimation. Main features of the genralised linear model: i) the probability distribution function of the random component of the response variable belongs to the exponential family, ii) a differentiable and monotonic link function relates the mean of the response variable to the linear predictor, a linear combination of coefficients and explanatory variables.
Tuesday 26 March 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to the theoretical basis of logistic regression model, commonly used for binary (proportion/percentage) data, as a generalised linear model. Binomial distribution for the outcome binary variable. The link between probability and logodds. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance.
Wednesday 03 April 2024
14:30 - 16:30
Duration: 2:00 AM
To be defined Lucia Cazzoletti Introduction to the theoretical basis of Poisson regression model, commonly used for count data, as a generalised linear model. Poisson distribution for the outcome variable. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance. Use of the offset to take into account the different exposure of subjects. Extensions of the Poisson Regression Model: Negative binomial regression model (NBRM), Zero-inflated poisson (ZIP) model, Zero-truncated count data model.
Wednesday 24 April 2024
14:30 - 16:30
Duration: 2:00 AM
Aula virtuale - Lezione online Lucia Cazzoletti Using Deviances to Compare Models for Logistic and for Poisson Regression Models. Use of the Likelihood Ratio Test to assess the presence of overdispersion. Some hints about the log-linear model in the presence of contingency tables

Faculty

A B C D G L M R S T V Z

Accordini Simone

symbol email simone.accordini@univr.it symbol phone-number +39 045 8027657

Bertossi Dario

symbol email dario.bertossi@univr.it symbol phone-number +39 045 812 4096

Bombieri Cristina

symbol email cristina.bombieri@univr.it symbol phone-number 045-8027284

Cantalupo Gaetano

symbol email gaetano.cantalupo@univr.it symbol phone-number +39 045 812 7123

Cazzoletti Lucia

symbol email lucia.cazzoletti@univr.it symbol phone-number 045 8027656

Danese Elisa

symbol email elisa.danese@univr.it symbol phone-number +39 045 812 6698

Darra Francesca

symbol email francesca.darra@univr.it symbol phone-number +39 045 812 7869

Gibellini Davide

symbol email davide.gibellini@univr.it symbol phone-number +390458027559

Lievens Patricia

symbol email patricia.lievens@univr.it symbol phone-number 0458027218

Lippi Giuseppe

symbol email giuseppe.lippi@univr.it symbol phone-number +39 045 812 4308

Malerba Giovanni

symbol email giovanni.malerba@univr.it symbol phone-number 045/8027685

Marcon Alessandro

symbol email alessandro.marcon@univr.it symbol phone-number +39 045 802 7668

Mazzariol Annarita

symbol email annarita.mazzariol@univr.it symbol phone-number 045 8027690

Montagnana Martina

symbol email martina.montagnana@univr.it symbol phone-number +39 045 812 6698

Righi Elda

symbol email elda.righi@univr.it

Romanelli Maria

symbol email mariagrazia.romanelli@univr.it symbol phone-number +39 045 802 7182

Ruggiero Alessandra

symbol email alessandra.ruggiero@univr.it symbol phone-number 045 8027208

Sacchetto Luca

symbol email luca.sacchetto@univr.it symbol phone-number +39 045 812 7538

Salvagno Gian Luca

symbol email gianluca.salvagno@univr.it symbol phone-number 045 8124308-0456449264

Savoia Anna

symbol email anna.savoia@univr.it symbol phone-number 045 802 7295

Scupoli Maria

symbol email mariateresa.scupoli@univr.it symbol phone-number 045-8027405 045-8128425

Signoretto Caterina

symbol email caterina.signoretto@univr.it symbol phone-number 045 802 7195

Tacconelli Evelina

symbol email evelina.tacconelli@univr.it symbol phone-number 0458128243 (Segreteria)

Tardivo Stefano

symbol email stefano.tardivo@univr.it symbol phone-number +39 045 802 7660

Trabetti Elisabetta

symbol email elisabetta.trabetti@univr.it symbol phone-number 045/8027209

Valenti Maria Teresa

symbol email mariateresa.valenti@univr.it symbol phone-number +39 045 812 8450

Verlato Giuseppe

symbol email giuseppe.verlato@univr.it symbol phone-number 045 8027628

Zanolin Maria Elisabetta

symbol email elisabetta.zanolin@univr.it symbol phone-number +39 045 802 7654

Zerman Nicoletta

symbol email nicoletta.zerman@univr.it symbol phone-number + 39 045 812 4251 - 4857

Zipeto Donato

symbol email donato.zipeto@univr.it symbol phone-number +39 045 802 7204

PhD students

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Course lessons
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Guidelines for PhD students

Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.