CancerNet: a unified deep learning network for pan‑cancer diagnostics

Article states that despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. The author's proposed framework for cancer diagnostics detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas. Their model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, holding a great promise in early cancer detection

The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases

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

Dimensionality reduction methods for microarray cancer data using prior knowledge

Microarray studies are currently a very popular source of biological information. They allow the simultaneous measurement of hundreds of thousands of genes, drastically increasing the amount of data that can be gathered in a small amount of time and also decreasing the cost of producing such results. Large numbers of high dimensional data sets are currently being generated and there is an ongoing need to find ways to analyse them to obtain meaningful interpretations. Many microarray experiments are concerned with answering specific biological or medical questions regarding diseases and treatments. Cancer is one of the most popular research areas and there is a plethora of data available requiring in depth analysis. Although the analysis of microarray data has been thoroughly researched over the past ten years, new approaches still appear regularly, and may lead to a better understanding of the available information. The size of the modern data sets presents considerable difficulties to traditional methodologies based on hypothesis testing, and there is a new move towards the use of machine learning in microarray data analysis. Two new methods of using prior genetic knowledge in machine learning algorithms have been developed and their results are compared with existing methods. The prior knowledge consists of biological pathway data that can be found in on-line databases, and gene ontology terms. The first method, called ``a priori manifold learning'' uses the prior knowledge when constructing a manifold for non-linear feature extraction. It was found to perform better than both linear principal components analysis (PCA) and the non-linear Isomap algorithm (without prior knowledge) in both classification accuracy and quality of the clusters. Both pathway and GO terms were used as prior knowledge, and results showed that using GO terms can make the models over-fit the data. In the cases where the use of GO terms does not over-fit, the results are better than PCA, Isomap and a priori manifold learning using pathways. The second method, called ``the feature selection over pathway segmentation algorithm'', uses the pathway information to split a big dataset into smaller ones. Then, using AdaBoost, decision trees are constructed for each of the smaller sets and the sets that achieve higher classification accuracy are identified. The individual genes in these subsets are assessed to determine their role in the classification process. Using data sets concerning chronic myeloid leukaemia (CML) two subsets based on pathways were found to be strongly associated with the response to treatment. Using a different data set from measurements on lower grade glioma (LGG) tumours, four informative gene sets were discovered. Further analysis based on the Gini importance measure identified a set of genes for each cancer type (CML, LGG) that could predict the response to treatment very accurately (> 90%). Moreover a single gene that can predict the response to CML treatment accurately was identified.Open Acces