13 research outputs found
Gene Expression Analysis Methods on Microarray Data a A Review
In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.ope
Developing RNA diagnostics for studying healthy human ageing
Developing strategies to cope with increase in the ageing population and age-related chronic diseases is one of the societies biggest challenges. The characteristics of the ageing process shows significant inter-individual variation. Building genomic signatures that could account for variation
in health outcomes with age may facilitate early prognosis of individual age-correlated diseases (e.g. cancer, coronary artery diseases and dementia) and help in developing better targeted
treatments provided years in advance of acquiring disabling symptoms for these diseases. The aim of this thesis was to explore methods for diagnosing molecular features of human ageing. In
particular, we utilise multi-platform transcriptomics, independent clinical data and classification
methods to evaluate which human tissues demonstrate a reproducible molecular signature for age
and which clinical phenotypes correlated with these new RNA biomarkers. [Continues.
Statistical Methods to Enhance Clinical Prediction with High-Dimensional Data and Ordinal Response
Der technologische Fortschritt ermöglicht es heute, die moleculare
Konfiguration einzelner Zellen oder ganzer Gewebeproben zu
untersuchen. Solche in groĂen Mengen produzierten
hochdimensionalen Omics-Daten aus der Molekularbiologie lassen sich
zu immer niedrigeren Kosten erzeugen und werden so immer
hÀufiger auch in klinischen Fragestellungen eingesetzt.
Personalisierte Diagnose oder auch die Vorhersage eines
Behandlungserfolges auf der Basis solcher Hochdurchsatzdaten stellen
eine moderne Anwendung von Techniken aus dem maschinellen Lernen dar.
In der Praxis werden klinische Parameter, wie etwa der
Gesundheitszustand oder die Nebenwirkungen einer Therapie, hÀufig auf
einer ordinalen Skala erhoben (beispielsweise gut, normal,
schlecht).
Es ist verbreitet, Klassifikationsproblme mit ordinal skaliertem
Endpunkt wie generelle Mehrklassenproblme zu behandeln und somit die
Information, die in der Ordnung zwischen den Klassen enthalten ist, zu
ignorieren. Allerdings kann das VernachlÀssigen dieser Information zu
einer verminderten KlassifikationsgĂŒte fĂŒhren oder sogar eine
ungĂŒnstige ungeordnete Klassifikation erzeugen.
Klassische AnsÀtze, einen ordinal skalierten Endpunkt direkt zu
modellieren, wie beispielsweise mit einem kumulativen Linkmodell,
lassen sich typischerweise nicht auf hochdimensionale Daten anwenden.
Wir prÀsentieren in dieser Arbeit hierarchical twoing (hi2) als
einen Algorithmus fĂŒr die Klassifikation hochdimensionler Daten in
ordinal Skalierte Kategorien. hi2 nutzt die MĂ€chtigkeit der
sehr gut verstandenen binÀren Klassifikation, um auch in ordinale
Kategorien zu klassifizieren. Eine Opensource-Implementierung von
hi2 ist online verfĂŒgbar.
In einer Vergleichsstudie zur Klassifikation von echten wie von
simulierten Daten mit ordinalem Endpunkt produzieren etablierte
Methoden, die speziell fĂŒr geordnete Kategorien entworfen wurden,
nicht generell bessere Ergebnisse als state-of-the-art
nicht-ordinale Klassifikatoren. Die FĂ€higkeit eines Algorithmus, mit
hochdimensionalen Daten umzugehen, dominiert die
Klassifikationsleisting. Wir zeigen, dass unser Algorithmus hi2
konsistent gute Ergebnisse erzielt und in vielen FĂ€llen besser
abschneidet als die anderen Methoden
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