Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics

Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.

Novel pattern recognition approaches for transcriptomics data analysis

We proposed a family of methods for transcriptomics and genomics data analysis based on multi-level thresholding approach, such as OMTG for sub-grid and spot detection in DNA microarrays, and OMT for detecting significant regions based on next generation sequencing data. Extensive experiments on real-life datasets and a comparison to other methods show that the proposed methods perform these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approaches can be used in various types of transcriptome analysis problems such as microarray image gridding with different resolutions and spot sizes as well as finding the interacting regions of DNA with a protein of interest using ChIP-Seq data without any need for parameter adjustment. We also developed constrained multi-level thresholding (CMT), an algorithm used to detect enriched regions on ChIP-Seq data with the ability of targeting regions within a specific range. We show that CMT has higher accuracy in detecting enriched regions (peaks) by objectively assessing its performance relative to other previously proposed peak finders. This is shown by testing three algorithms on the well-known FoxA1 Data set, four transcription factors (with a total of six antibodies) for Drosophila melanogaster and the H3K4ac antibody dataset. Finally, we propose a tree-based approach that conducts gene selection and builds a classifier simultaneously, in order to select the minimal number of genes that would reliably predict a given breast cancer subtype. Our results support that this modified approach to gene selection yields a small subset of genes that can predict subtypes with greater than 95%overall accuracy. In addition to providing a valuable list of targets for diagnostic purposes, the gene ontologies of the selected genes suggest that these methods have isolated a number of potential genes involved in breast cancer biology, etiology and potentially novel therapeutics

Approximate Matching in Genomic Sequence Data

Ph.DDOCTOR OF PHILOSOPH

Hidden Citations Obscure True Impact in Science

References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it. Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations. We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed is a discovery, the less visible it is to standard bibliometric analysis. Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus

New Advances in Melanoma

Melanoma is a very aggressive tumor which is derived from the transformation of pigment-producing cells termed the melanocytes. This cancer type accounts for most of the deaths associated with skin cancer as well as its incidence and is in constant evolution. Because of the rapid and very high metastatic potential of this tumor, melanoma prognosis has been quite poor for a long time. In the past decade, groundbreaking discoveries in the melanoma research field have led to the development of two main treatment strategies: combination therapies targeting specific kinases or combination therapies focused on immune checkpoint inhibitors (ICIs). These treatment approaches have become the standard of care in most cancer centers and significantly improved the prognosis and overall survival of advanced melanoma patients. Nevertheless, many patients do not benefit from or even respond to these treatments. It is therefore essential to better comprehend the phenomenon of drug resistance, immune escape mechanisms, as well as to search for alternative treatment strategies. In addition, strong predictive biomarkers are desperately needed to improve clinical efficacy. The aim of this Special Issue is to present recent advances in the field of melanoma research, in which the abovementioned areas represent the primary focus, and other relevant themes are also discussed

7th Annual Student Academic Conference: Conference Program & Abstracts Volume VII

Minnesota State University Moorhead Student Academic Conference abstract book.https://red.mnstate.edu/sac-book/1006/thumbnail.jp

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Going viral : an integrated view on virological data analysis from basic research to clinical applications

Viruses are of considerable interest for several fields of life science research. The genomic richness of these entities, their environmen- tal abundance, as well as their high adaptability and, potentially, pathogenicity make treatment of viral diseases challenging. This thesis proposes three novel contributions to antiviral research that each concern analysis procedures of high-throughput experimen- tal genomics data. First, a sensitive approach for detecting viral genomes and transcripts in sequencing data of human cancers is presented that improves upon prior approaches by allowing de- tection of viral nucleotide sequences that consist of human-viral homologs or are diverged from known reference sequences. Sec- ond, a computational method for inferring physical protein contacts from experimental protein complex purification assays is put for- ward that allows statistically meaningful integration of multiple data sets and is able to infer protein contacts of transiently binding protein classes such as kinases and molecular chaperones. Third, an investigation of minute changes in viral genomic populations upon treatment of patients with the mutagen ribavirin is presented that first characterizes the mutagenic effect of this drug on the hepatitis C virus based on deep sequencing data.Viren sind von beträchtlichem Interesse für die biowissenschaftliche Forschung. Der genetische Reichtum, die hohe Vielfalt, wie auch die Anpassungsfähigkeit und mögliche Pathogenität dieser Organismen erschwert die Behandlung von viralen Erkrankungen. Diese Promotionsschrift enthält drei neuartige Beiträge zur antiviralen Forschung welche die Analyse von experimentellen Hochdurchsatzdaten der Genomik betreffen: erstens, ein sensitiver Ansatz zur Entdeckung viraler Genome und Transkripte in Sequenzdaten humaner Karzinome, der die Identifikation von viralen Nukleotidsequenzen ermöglicht, die von Referenzgenomen ab- weichen oder homolog zu humanen Faktoren sind. Zweitens, eine computergestützte Methode um physische Proteinkontakte von experimentellen Proteinkomplex-Purifikationsdaten abzuleiten welche die statistische Integration von mehreren Datensätzen erlaubt um insbesondere Proteinkontakte von flüchtig interagierenden Proteinklassen wie etwa Kinasen und Chaperonen aus den Daten ableiten zu können. Drittens, eine Untersuchung von kleinsten Änderungen viraler Genompopulationen während der Behandlung von Patienten mit dem Mutagen ribavirin die zum ersten Mal die mutagene Wirkung dieses Medikaments auf das Hepatitis C Virus mittels Tiefensequenzdaten nachweist