[BBC] BioSB course: Machine Learning for bioinformatics and systems biology - 5-9 October 2020

[Femke Francissen]

*Machine Learning for bioinformatics and systems biologyDate: 5-9 October
2020, online course*
Modern biology is a data-rich science, driven by our ability to measure the
detailed molecular characteristics of cells, organs, and individuals at
many different levels. Interpretation of these large-scale biological data
requires the detection of statistical dependencies and patterns in order to
establish useful models of complex biological systems. Techniques from
machine learning are key in this endeavour. Typical examples are the
visualization of single-cell RNA-seq data using dimensionality reduction
methods, base calling for nanopore sequencing data using hidden Markov
models and (recurrent) neural networks, and classification of
high-throughput microscopy image data using convolutional neural networks.
In this one-week course, the foundations of machine learning will be laid
out and commonly used methods for unsupervised (clustering, dimensionality
reduction, visualization) and supervised (mainly classification) learning
will be explained in detail. Methods will be illustrated using recent
examples from the fields of systems biology and bioinformatics. Methods
discussed in the morning lectures will be put into practice during the
afternoon computer lab sessions.

Topics include:

   - Density estimation, including histograms, nearest neighbour, Parzen
   - Evaluation, including ROC, cross-validation
   - Parametric and non-parametric classifiers, including linear
   discriminant analysis, k-nearest neighbours, logistic regression, decision
   trees and random forests
   - Feature selection, including search algorithms (forward, backward,
   branch & bound) and sparse classifiers (ridge, lasso, elastic net)
   - Dimensionality reduction, including principal component analysis,
   multidimensional scaling, t-SNE.
   - Clustering, including hierarchical clustering, k-means, Gaussian
   mixture models
   - Hidden Markov models
   - (Deep) neural networks
   - Kernel-based methods, including support vector machines

After having followed this course, the student has a good understanding of
a wide range of machine learning techniques and is able to recognize what
method is most applicable to data analysis problems (s)he encounters in
bioinformatics and systems biology applications.

*Target audience*
This course is aimed at PhD students with a background in bioinformatics,
systems biology, computer science or a related field, and life sciences.
Participants from the private sector are also welcome. A working knowledge
of basic statistics and linear algebra is assumed. Preparation material on
statistics and linear algebra will be distributed before the course, to be
studied by students missing the required background.

*More information and registration* via:
https://www.biosb.nl/archive-courses/machine-learning-for-bioinformatics-and-systems-biology-2020/

*About BioSB*
The Netherlands Bioinformatics and Systems Biology research school (BioSB)
is the national platform in which the Dutch community of researchers in
bioinformatics and systems biology is organized. BioSB also organizes
fundamental and advanced post-graduate courses, the yearly BioSB conference
and hot topics meetings. The aim of the BioSB research school is to offer
PhD students, postdocs and senior researchers a vibrant environment for
their scientific development as well as education in bioinformatics and
systems biology.


BioSB research school
E-mail: education at biosb.nl
Website: w <http://goog_1921660760>ww.biosb.nl

[image: BioSB_logo_full_250x226.png]
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