On this page you find information about lectures we
are offering. In addition, we provide information about the preparation of MSc/BSc thesis and PhD thesis.

The lecture *Computational Diagnostics of Data* is a 5 cr course offered at Tampere University (MSc level).

After
completing the course, the student gained a basic understanding of the
definition and the meaning of computational diagnostics and its utility
for biomedical research. Case studies will be discussed illustrating the
interplay between computational and statistical methods that are
applied to large-scale and high-dimensional data sets from various experiments. Moreover, the student will learn how to practically
approach such problems by using the statistical programming language R.
In general, the course teaches statistical thinking in the context of
biomedical problems, i.e., the adaptation of machine learning methods in
a problem specific manner.

The name of the course emphasizes that adopting a mechanistic or
cookbook-type approach to data science is destined to be unsuccessful,
underscoring the necessity for active involvement and critical thinking
from the analyst. The overall goal of the course is to learn statistical thinking.

The textbook of the course is: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

The lecture * Introduction to Probability and Statistical Inference* is a 5 cr course offered at Tampere University (BSc level, English implementation).

After
completing the course, the student gained a basic understanding of the
definition and the meaning of computational diagnostics and its utility
for biomedical research. Case studies will be discussed illustrating the
interplay between computational and statistical methods that are
applied to large-scale and high-dimensional data sets from various experiments. Moreover, the student will learn how to practically
approach such problems by using the statistical programming language R.
In general, the course teaches statistical thinking in the context of
biomedical problems, i.e., the adaptation of machine learning methods in
a problem specific manner.

Textbook of the course: In preparation.

We provide interesting projects for students to perpare a
Master (MSc) thesis (or BSc thesis). Below we list some publications that resulted from MSc projects.

- Smolander, J., Dehmer, M., &
Emmert-Streib, F. (2019). Comparing deep belief networks with support
vector machines for classifying gene expression data from complex
disorders.
*FEBS Open Bio*,*9*(7), 1232-1248. - Moore, D., de Matos Simoes, R., Dehmer, M., & Emmert-Streib, F. (2019). Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data. Current genomics, 20(1), 38-48.
- Smolander, J., Stupnikov, A., Glazko, G., Dehmer, M., & Emmert-Streib, F. (2019). Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients. BMC cancer, 19(1), 1176.
- Yang, Z., Dehmer, M., Yli-Harja, O.,
& Emmert-Streib, F. (2020). Combining deep learning with token
selection for patient phenotyping from electronic health records.
*Scientific reports*,*10*(1), 1432.

Context us when you are interested in such a project.

We are also providing interesting projects for gifted students to prepare a PhD thesis. If interested please send the following information:

- CV (including information about GPA)
- Brief research statement (1 page)
- What fellowship will you apply

Context us when you are interested in such a project.

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