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In this thesis, different laser based methods to produce high density peptide arrays were developed. They use laser radiation to structure special micro particles with amino acids. The particles are heated and fused to a substrate (Combinatorial Laser Fusing) or they are transferred trough a shock wave to another substrate (Combinatorial Laser Transfer). This way, microarrays with up to 1 million spots per cm² are produced whereas the number of chemical coupling cycles is minimized.

Microarray, Peptid, Laser, Partikel, Aminosäuremicroarray, peptide, laser, particle, amino acid

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Phages have shown a high biotechnological potential with numerous applications. The advent of high-resolution microscopy techniques aligned with omic and molecular tools have revealed innovative phage features and enabled new processes that can be further exploited for biotechnological applications in a wide variety of fields. The high-quality original articles and reviews presented in this Special Issue demonstrate the incredible potential of phages and their derived proteins in a wide range of biotechnological applications for human benefit. Considering the emergence of amazing new available bioengineering tools and the high abundance of phages and the multitude of phage proteins yet to be discovered and studied, we believe that the upcoming years will present us with many more fascinating and new previously unimagined phage-based biotechnological applications.

gene expression regulation --- molecular probe --- macromolecular interactions --- phage-host interaction --- bacteriophage --- endolysin --- Clostridium perfringens --- alpha-sheet --- cancerous tumors --- capsid dynamics --- drug delivery vehicles --- native gel electrophoresis --- neurodegenerative disease --- pathogenic viruses --- phage display --- landscape phage --- major coat protein --- nanomedicine --- diagnostics --- biosensors --- M13 bacteriophage --- biofilm --- porous structure --- filters --- self-assembly --- T7phage library --- sarcoidosis --- tuberculosis --- microarray --- immunoscreening --- R-type pyocin --- bacteriocin --- contractile injection systems --- Pseudomonas aeruginosa --- X-ray crystallography --- receptor-binding protein --- Shigella flexneri --- bacteriophage --- tailspike proteins --- O-antigen --- serotyping --- microtiter plate assay --- fluorescence sensor --- bacteriophages --- encapsulation --- niosomes --- transfersomes --- liposomes --- Staphylococcus aureus --- phage --- Enterococcus faecalis --- Streptococcus agalactiae --- culture enrichment --- bacteriophage --- diagnostics --- Listeria monocytogenes --- endolysin --- magnetic separation --- reporter phage --- endolysin --- Pal --- Cpl-1 --- safety --- toxicity --- immune response --- Streptococcus pneumoniae --- self-assembly --- nanotubular structures --- tail sheath protein --- bacteriophage vB_EcoM_FV3 --- Appelmans --- bacteriophage evolution --- bacteriophage recombination --- phage therapy --- Pseudomonas aeruginosa --- antibiotic resistance --- bacteriophages --- Myoviridae --- bacteriophage-derived lytic enzyme --- enzybiotics --- endolysin --- in vitro activity --- ESKAPE --- n/a

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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

comorbidity score --- mortality --- locoregionally advanced --- HNSCC --- curative surgery --- traditional Chinese medicine --- health strengthening herb --- cancer treatment --- network pharmacology --- network target --- high-throughput analysis --- brain metastases --- colorectal cancer --- KRAS mutation --- PD-L1 --- tumor infiltrating lymphocytes --- drug resistance --- gefitinib --- erlotinib --- biostatistics --- bioinformatics --- Bufadienolide-like chemicals --- molecular mechanism --- anti-cancer --- bioinformatics --- cancer --- brain --- pathophysiology --- imaging --- machine learning --- extreme learning --- deep learning --- neurological disorders --- pancreatic cancer --- TCGA --- curation --- DNA --- RNA --- protein --- single-biomarkers --- multiple-biomarkers --- cancer-related pathways --- colorectal cancer --- DNA sequence profile --- Monte Carlo --- mixture of normal distributions --- somatic mutation --- tumor --- mutable motif --- activation induced deaminase --- AID/APOBEC --- transcriptional signatures --- copy number variation --- copy number aberration --- TCGA mining --- cancer CRISPR --- firehose --- gene signature extraction --- gene loss biomarkers --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- microarray --- ovarian cancer --- predictive model --- machine learning --- overall survival --- observed survival interval --- skin cutaneous melanoma --- The Cancer Genome Atlas --- omics --- breast cancer prognosis --- artificial intelligence --- machine learning --- decision support systems --- cancer prognosis --- independent prognostic power --- omics profiles --- histopathological imaging features --- cancer --- intratumor heterogeneity --- genomic instability --- epigenetics --- mitochondrial metabolism --- miRNAs --- cancer biomarkers --- breast cancer detection --- machine learning --- feature selection --- classification --- denoising autoencoders --- breast cancer --- feature extraction and interpretation --- concatenated deep feature --- cancer modeling --- interaction --- histopathological imaging --- clinical/environmental factors --- oral cancer --- miRNA --- bioinformatics --- datasets --- biomarkers --- TCGA --- GEO DataSets --- hormone sensitive cancers --- breast cancer --- StAR --- estrogen --- steroidogenic enzymes --- hTERT --- telomerase --- telomeres --- alternative splicing --- network analysis --- hierarchical clustering analysis --- differential gene expression analysis --- cancer biomarker --- diseases genes --- variable selection --- false discovery rate --- knockoffs --- bioinformatics --- copy number variation --- cell-free DNA --- methylation --- mutation --- next generation sequencing --- self-organizing map --- head and neck cancer --- treatment de-escalation --- HP --- molecular subtypes --- tumor microenvironment --- Bioinformatics tool --- R package --- machine learning --- meta-analysis --- biomarker signature --- gene expression analysis --- survival analysis --- functional analysis --- bioinformatics --- machine learning --- artificial intelligence --- Network Analysis --- single-cell sequencing --- circulating tumor DNA (ctDNA) --- Neoantigen Prediction --- precision medicine --- Computational Immunology