Unsupervised multiple kernel learning approaches for integrating molecular cancer patient data

Cancer is the second leading cause of death worldwide. A characteristic of this disease is its complexity leading to a wide variety of genetic and molecular aberrations in the tumors. This heterogeneity necessitates personalized therapies for the patients. However, currently defined cancer subtypes used in clinical practice for treatment decision-making are based on relatively few selected markers and thus provide only a coarse classifcation of tumors. The increased availability in multi-omics data measured for cancer patients now offers the possibility of defining more informed cancer subtypes. Such a more fine-grained characterization of cancer subtypes harbors the potential of substantially expanding treatment options in personalized cancer therapy. In this thesis, we identify comprehensive cancer subtypes using multidimensional data. For this purpose, we apply and extend unsupervised multiple kernel learning methods. Three challenges of unsupervised multiple kernel learning are addressed: robustness, applicability, and interpretability. First, we show that regularization of the multiple kernel graph embedding framework, which enables the implementation of dimensionality reduction techniques, can increase the stability of the resulting patient subgroups. This improvement is especially beneficial for data sets with a small number of samples. Second, we adapt the objective function of kernel principal component analysis to enable the application of multiple kernel learning in combination with this widely used dimensionality reduction technique. Third, we improve the interpretability of kernel learning procedures by performing feature clustering prior to integrating the data via multiple kernel learning. On the basis of these clusters, we derive a score indicating the impact of a feature cluster on a patient cluster, thereby facilitating further analysis of the cluster-specific biological properties. All three procedures are successfully tested on real-world cancer data. Comparing our newly derived methodologies to established methods provides evidence that our work offers novel and beneficial ways of identifying patient subgroups and gaining insights into medically relevant characteristics of cancer subtypes.Krebs ist eine der häufigsten Todesursachen weltweit. Krebs ist gekennzeichnet durch seine Komplexität, die zu vielen verschiedenen genetischen und molekularen Aberrationen im Tumor führt. Die Unterschiede zwischen Tumoren erfordern personalisierte Therapien für die einzelnen Patienten. Die Krebssubtypen, die derzeit zur Behandlungsplanung in der klinischen Praxis verwendet werden, basieren auf relativ wenigen, genetischen oder molekularen Markern und können daher nur eine grobe Unterteilung der Tumoren liefern. Die zunehmende Verfügbarkeit von Multi-Omics-Daten für Krebspatienten ermöglicht die Neudefinition von fundierteren Krebssubtypen, die wiederum zu spezifischeren Behandlungen für Krebspatienten führen könnten. In dieser Dissertation identifizieren wir neue, potentielle Krebssubtypen basierend auf Multi-Omics-Daten. Hierfür verwenden wir unüberwachtes Multiple Kernel Learning, welches in der Lage ist mehrere Datentypen miteinander zu kombinieren. Drei Herausforderungen des unüberwachten Multiple Kernel Learnings werden adressiert: Robustheit, Anwendbarkeit und Interpretierbarkeit. Zunächst zeigen wir, dass die zusätzliche Regularisierung des Multiple Kernel Learning Frameworks zur Implementierung verschiedener Dimensionsreduktionstechniken die Stabilität der identifizierten Patientengruppen erhöht. Diese Robustheit ist besonders vorteilhaft für Datensätze mit einer geringen Anzahl von Proben. Zweitens passen wir die Zielfunktion der kernbasierten Hauptkomponentenanalyse an, um eine integrative Version dieser weit verbreiteten Dimensionsreduktionstechnik zu ermöglichen. Drittens verbessern wir die Interpretierbarkeit von kernbasierten Lernprozeduren, indem wir verwendete Merkmale in homogene Gruppen unterteilen bevor wir die Daten integrieren. Mit Hilfe dieser Gruppen definieren wir eine Bewertungsfunktion, die die weitere Auswertung der biologischen Eigenschaften von Patientengruppen erleichtert. Alle drei Verfahren werden an realen Krebsdaten getestet. Den Vergleich unserer Methodik mit etablierten Methoden weist nach, dass unsere Arbeit neue und nützliche Möglichkeiten bietet, um integrative Patientengruppen zu identifizieren und Einblicke in medizinisch relevante Eigenschaften von Krebssubtypen zu erhalten

Flamingo Vol. IV N 9

Anonymous. Cover. Picture. 0. Anonymous. Untitled. Picture. 5. Anonymous. Untitled. Picture. 6. C.K. Flamingo. Picture. 7. E.T. Late Spring. Prose. 7. Anonymous. Untitled. Picture. 8. Aussi. TO Q. S. Poem. 8. Kibby. HORACE, BOOK I, ODE NINE. Poem. 9. X. EXCLUSION. Poem .9. I.K. DISAPPOINTMENT. Poem. 9. G.W. March. Poem. 9. V. TO A PICTURE. Poem. 9. I.K. I KNOW A POEM. Poem. 9. Z.X. Lovesick. Poem. 9. Cal. Our Serenade. Poem. 10. G.W. Irish Lullaby. Poem. 10. Anonymous. Untitled. Poem. 10. Anonymous. Untitled. Prose. 10. C.E.D. A Lass! Poem. 10. Anonymous. Untitled. Prose. 10. CeMorin. Walnuts. Picture. 11. Anonymous. Do you think women are fair to men? Picture. 12. Anonymous. ANOTHER SCOOP. Prose. 12. Anonymous. Untitled. Prose. 12. V.F. Curl Stuff. Poem. 12. Anonymous. F.F.V. Prose. 12. Anonymous. Call a Reporter. Prose. 12. Anonymous. Untitled. Prose. 12. E.T. You seem to like Jack\u27s attentions. Why don\u27t you marry him? Picture. 12. Ye Editor. PROBLEMS IN ETIQUETTE. Prose. 12. Anonymous. Untitled. Prose. 12. V.F. Ternpus Fudgets. Poem. 13. Anonymous. AT PATSY\u27S. Prose. 13. Anonymous. Untitled. Prose. 13. Q.E.D. Same Old Story. Poem. 13. Anonymous. Untitled. Prose. 13. Anonymous. ANATOMICAL ACCIDENTS. Prose. 13. Anonymous. Untitled. Prose. 13. W. Speaker— I wish now to tax your memory—. Picture. 13. Kal. Me an\u27 Annie. Poem. 13. Anonymous. Untitled. Prose. 13. B.K.R. LAMENTATION. Poem. 13. Anonymous. THE HOODOO CHAIR. Prose. 14. Anonymous. Untitled. Picture. 14. Anonymous. Untitled. Picture. 15. Anonymous. SPRING TURNS NORTH. Prose. 15. Anonymous. SEE YOU LATER! Prose. 15. W.G. Untitled. Prose. 15. Bridge. Denison Comics. Picture. 16. A. It— Did your mother say anything about my staying so late? Picture. 18. N.H.G. Mark XVII. Prose. 18. Anonymous. Untitled. Prose. 18. Anonymous. Untitled. Poem. 18. A. But darling, don\u27t you want to marry a man who is economical? Picture. 18. Anonymous. Untitled. Poem. 18. Anonymous. Nobody can fool my man Bill! Picture. 19. B. Contempt of Court. Picture. 19. Anonymous. Don\u27t you think her color is pretty? Picture. 19. Anonymous. Untitled. Prose. 20. W.G. and G.W. The End of a Perfect Daze. Picture. 20. C.K. Boston Bags. Picture. 21. Anonymous. Were you cool when the burglar entered the room? Picture. 22. Johncy. HELYES. Poem. 22. Anonymous. Untitled. Prose. 22. Kitty. THE VILLAGE LOOTSMITH. Poem. 22. J.E. TO A BOOKWORM. Poem. 22. Anonymous. Untitled. Prose. 22. Anonymous. She- You said you wished to see my father alone? Picture. 22. Speicher, J. She— I think handsome men are always dumb. Picture. 25. H. Untitled. Picture. 26. Merror. Untitled. Prose. 26. Jack-o\u27-Lantern. Untitled. Poem. 26. Siren. Untitled. Prose. 26. Puppet. Untitled. Prose. 28. Record. Untitled. Prose. 28. Octopus. Untitled. Prose. 31. Royal Gaboon. Untitled. Prose. 31. Siren. Untitled. Prose. 31

The Free Quon Gas Suffers Gibbs' Paradox

We consider the Statistical Mechanics of systems of particles satisfying the $q$-commutation relations recently proposed by Greenberg and others. We show that although the commutation relations approach Bose (resp.\ Fermi) relations for $q\to1$ (resp.\ $q\to-1$), the partition functions of free gases are independent of $q$ in the range $-1<q<1$. The partition functions exhibit Gibbs' Paradox in the same way as a classical gas without a correction factor $1/N!$ for the statistical weight of the $N$-particle phase space, i.e.\ the Statistical Mechanics does not describe a material for which entropy, free energy, and particle number are extensive thermodynamical quantities.Comment: number-of-pages, LaTeX with REVTE

Superheavy nuclei in relativistic effective Lagrangian model

Isotopic and isotonic chains of superheavy nuclei are analyzed to search for spherical double shell closures beyond Z=82 and N=126 within the new effective field theory model of Furnstahl, Serot, and Tang for the relativistic nuclear many-body problem. We take into account several indicators to identify the occurrence of possible shell closures, such as two-nucleon separation energies, two-nucleon shell gaps, average pairing gaps, and the shell correction energy. The effective Lagrangian model predicts N=172 and Z=120 and N=258 and Z=120 as spherical doubly magic superheavy nuclei, whereas N=184 and Z=114 show some magic character depending on the parameter set. The magicity of a particular neutron (proton) number in the analyzed mass region is found to depend on the number of protons (neutrons) present in the nucleus.Comment: 26 pages, REVTeX, 10 ps figures; changed conten

Superheavy nuclei in relativistic effective Lagrangian model

Isotopic and isotonic chains of superheavy nuclei are analyzed to search for spherical double shell closures beyond Z=82 and N=126 within the new effective field theory model of Furnstahl, Serot, and Tang for the relativistic nuclear many-body problem. We take into account several indicators to identify the occurrence of possible shell closures, such as two-nucleon separation energies, two-nucleon shell gaps, average pairing gaps, and the shell correction energy. The effective Lagrangian model predicts N=172 and Z=120 and N=258 and Z=120 as spherical doubly magic superheavy nuclei, whereas N=184 and Z=114 show some magic character depending on the parameter set. The magicity of a particular neutron (proton) number in the analyzed mass region is found to depend on the number of protons (neutrons) present in the nucleus.Comment: 26 pages, REVTeX, 10 ps figures; changed conten

Effective remodelling of human osteoarthritic cartilage by sox9 gene transfer and overexpression upon delivery of rAAV vectors in polymeric micelles

[Abstract] Recombinant adeno-associated virus (rAAV) vectors are well suited carriers to provide durable treatments for human osteoarthritis (OA). Controlled release of rAAV from polymeric micelles was already shown to increase both the stability and bioactivity of the vectors while overcoming barriers, precluding effective gene transfer. In the present study, we examined the convenience of delivering rAAV vectors via poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO) polymeric (PEO–PPO–PEO) micelles to transfer and overexpress the transcription factor SOX9 in monolayers of human OA chondrocytes and in experimentally created human osteochondral defects. Human osteoarthritic (OA) chondrocytes and human osteochondral defect models were produced using human OA cartilage obtained from patients subjected to total knee arthroplasty. Samples were genetically modified by adding a rAAV-FLAG-hsox9 vector in its free form or via polymeric micelles for 10 days relative to control conditions (unmodified cells). The effects of sox9 overexpression in human OA cartilage samples were monitored by biochemical, histological, and immunohistochemical analyses. Delivery of rAAV-FLAG-hsox9 via polymeric micelles enhanced the levels of sox9 expression compared with free vector administration, resulting in increased proteoglycan deposition and in a stimulated cell proliferation index in OA chondrocytes. Moreover, higher production of type II collagen and decreased hypertrophic events were noted in osteochondral defect cultures when compared with control conditions. Controlled therapeutic rAAV sox9 gene delivery using PEO–PPO–PEO micelles is a promising, efficient tool to promote the remodelling of human OA cartilage