Intro
Preface
Acknowledgements
Organization
Contents
TooT-BERT-T: A BERT Approach on Discriminating Transport Proteins from Non-transport Proteins
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Dataset
3.2 Protein Sequence Representation
3.3 Fine-Tuning a BERT Model
3.4 Logistic Regression
3.5 Evaluation
3.6 Evaluation Metrics
4 Results and Discussion
4.1 Fine-Tuning ProtBERT-BFD and MembraneBERT
4.2 Logistic Regression with Fine-Tuned ProtBERT-BFD
4.3 Comparison of TooT-BERT-T with State-of-the-Art Models
5 Conclusion

Towards a Flexible and Portable Workflow for Analyzing miRNA-Seq Neuropsychiatric Data: An Initial Replicability Assessment
1 Introduction
2 Materials and Methods
2.1 Data Acquisition
2.2 myBrain-Seq Implementation
2.3 Experimental Setup
3 Results and Discussion
3.1 Quality Control
3.2 Application of the Same Statistical Thresholds as the Original Articles
3.3 Comparison Between the Original Results, MiARma-Seq Results and MyBrain-Seq Results
4 Conclusion
References
The NAD Interactome, Identification of Putative New NAD-Binding Proteins
1 Introduction

2 Methods
2.1 Data Collection and Definition of Protein Datasets
2.2 Datasets Analysis
2.3 Identification of Putative New NAD-Binding Proteins
3 Results
3.1 NAD-Binding Proteins (NADBPs) Dataset
3.2 NAD-Protein-Protein Interactions (NAD-PPIs) Dataset
3.3 Pathway Enrichment Analysis
3.4 Characterization of Protein Domains of the NADBPs Dataset
3.5 Identification of Putative NAD-Binding Proteins
4 Discussion
5 Conclusion
References
Multiple Instance Learning Based on Mol2vec Molecular Substructure Embeddings for Discovery of NDM-1 Inhibitors
1 Introduction

2 Materials and Methods
2.1 Dataset Collection
2.2 Labeling the Database
2.3 Calculating Mol2vec Embeddings
2.4 Classification and Ranking Frame Work
3 Results and Discussion
3.1 Results
4 Conclusion
References
Towards Improving Bio-Image Segmentation Quality Through Ensemble Post-processing of Deep Learning and Classical 3D Segmentation Pipelines
1 Introduction
2 Previous Research on Ensemble Methods for Segmentation
3 Methodology
3.1 3D Segmentation Pipelines
3.2 Dataset
3.3 Fusion Algorithm 1: Label Set Intersection