Seminar Algorithms in Bioinformatics

Lecturer:

• Van Dinh Tran
• Alireza Khanteymoori
• Alex Mitrofanov
• Rolf Backofen

Dates:

• Initial compulsory meeting 23. October 2019, 14:00, room 02 006, building 106
• Sildes

Prelimenary Topics:

• DeepCRISPR: optimized CRISPR guide RNA design by deep learning
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• Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks
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• Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs
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• Off-target predictions in CRISPR-Cas9 gene editing using deep learning
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• Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)
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• Clustering single-cell RNA-seq data with a model-based deep learning approach
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• Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes
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• Triplexator: Detecting nucleic acid triple helices in genomic and transcriptomic data
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• DeepSol: a deep learning framework for sequence-based protein solubility prediction
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• Single-cell RNA-seq denoising using a deep count autoencoder
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• DeepHistone: a deep learning approach to predicting histone modifications
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• An integration of deep learning with feature embedding for protein–protein interaction prediction
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• DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity
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• Deep learning architectures for prediction of nucleosome positioning from sequences data
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• Prediction of Long Non-Coding RNAs Based on Deep Learning
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Organisation

• The seminar will be held in English.
• Hand in presentation a week before seminar begins.
• Active participation for all talks.
• Give 25 min presentation. (+ ~5-10 min discussion after)
• One page summary (no copy paste).
• Order of talks is based on order of submission.

Prerequisites

• Previous participation in at least one bioinformatics lecture
• Basic knowledge of algorithms, molecular biology and interest in biologically relevant problems