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Oggetto:
Oggetto:

Introduction to data analysis for natural and social sciences

Oggetto:

Introduction to data analysis for natural and social sciences

Oggetto:

Anno accademico 2022/2023

Codice attività didattica
FIS0087
Docente
Prof. Michele Caselle
Corso di studio
Laurea Magistrale Interateneo in Fisica dei sistemi complessi
Anno
1° anno, 2° anno
Periodo
Secondo Semestre
Tipologia
D=A scelta dello studente
Crediti/Valenza
6
SSD attività didattica
FIS/02 - fisica teorica, modelli e metodi matematici
Erogazione
Tradizionale
Lingua
Inglese
Frequenza
Facoltativa
Tipologia esame
Scritto ed orale
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Sommario insegnamento

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Obiettivi formativi

Obiettivo del corso è fornire una panoramica dei più avanzati strumenti di analisi dati. In particolare:

- Discutere alcune applicazioni esemplari

- Discutere i progressi più recenti nel campo dell'inferenza statistica.

 

Il corso sarà tenuto da tre visiting professors invitati appositamente per questo corso.

Loredana Martignetti,  Laurance Calzone e Andrea Mazzolini

Provide an overview of the most recent tools in the context of Data Analysis
- Discuss a few topical applications both in the context of Human and of Natural Sciences
- Introduce a few of the most recent computational tools for data analysis and inference

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Risultati dell'apprendimento attesi

Alla fine del corso lo studente avrà una conoscenza approfondita dei più moderni risultati nel campo della Analisi Dati e dell'inferenza statistica.

At the end of course the student will reach a good knowledge of the most advanced results in Data Analysis and Statistical Inference

 

Oggetto:

Programma

Il Corso è diviso in tre parti. La prima, tenuta da Loredana Martignetti la seconda da Laurance Calzone e la terza da Andrea Mazzolini.

1st module (L.Martignetti) :

* Introduction to the manipulation of molecular biology data(sequencing data, single cell data, spatial transcriptomics and multi-omics)
* Bioinformatics methods and tools for high-throughput data analysis and integrative bioinformatics
* Network-based approaches for the analysis of complex biological systems


2nd module (L.Calzone):

* Analysis of network representation of biological knowledge (applications to cancer, cystic fibrosis, rheumatoid arthritis)
* Dynamic modelling of signalling pathways deregulated in diseases
* Data integration in mathematical models to suggest personalised treatments to clinicians
* Prediction of single and drug combinations for cancer patients using stochastic Boolean approaches

 

3rd module (A. Mazzolini):

The last set of lectures aims to show recent successes of statistical physics methods to the study of the immune system.
After a brief biological introduction to this extremely complex and surprisingly powerful biological system, the course will be split in two parts.
The first part is dedicated to "data-driven" results. We start from the inference of the probability that a given antibody can be generated.
We will then use this to infer which antibodies in a given patient are responding to an undergoing infection.
The second part will be more theoretical, using simplified mathematical models to make sense of non-trivial features of the immune system.

 

   


The course consists of three modules of 16 hours each of theoretical and practical lessons, including a tutored project.

Here is the program of the three mdules:

1st module (L.Martignetti) :

* Introduction to the manipulation of molecular biology data(sequencing data, single cell data, spatial transcriptomics and multi-omics)
* Bioinformatics methods and tools for high-throughput data analysis and integrative bioinformatics
* Network-based approaches for the analysis of complex biological systems


2nd module (L.Calzone):

* Analysis of network representation of biological knowledge (applications to cancer, cystic fibrosis, rheumatoid arthritis)
* Dynamic modelling of signalling pathways deregulated in diseases
* Data integration in mathematical models to suggest personalised treatments to clinicians
* Prediction of single and drug combinations for cancer patients using stochastic Boolean approaches

 

3rd module (A. Mazzolini):

The last set of lectures aims to show recent successes of statistical physics methods to the study of the immune system.
After a brief biological introduction to this extremely complex and surprisingly powerful biological system, the course will be split in two parts.
The first part is dedicated to "data-driven" results. We start from the inference of the probability that a given antibody can be generated.
We will then use this to infer which antibodies in a given patient are responding to an undergoing infection.
The second part will be more theoretical, using simplified mathematical models to make sense of non-trivial features of the immune system.

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Modalità di insegnamento

Lezioni interattive con analisi hands-on in classe

Interactive lessons with hands-on in-class analysis of data sets

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Modalità di verifica dell'apprendimento

La valutazione del corso si baserà su un progetto finale e su  una prova orale, nella quale viene chiesto di affrontare ab initio due o tre argomenti svolti a lezione. In caso di non superamento dell'esame la ripetizione dello stesso deve avvenire almeno due settimane dopo la prima prova.

 The course evaluation will be based on a final  project  and an oral examination, during which the student has to develop ab initio two or three  of the topics explained during the lectures. In case the exam fails, it cannot be repeated earlier than two weeks after the first attempt.

 

Testi consigliati e bibliografia

Oggetto:

Dispense e riferimenti forniti durante il corso.

 

 

Lecture notes and references provided therein.



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