An incremental approach to automated protein localisation

Tscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. Results: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. Conclusion: We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Abstract—We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers. Index Terms—Fluorescence in situ hybridization (FISH), fuzzy ARTMAP neural network (NN), genetic abnormality diagnosis, image classification, off- and online learning. I

Machine learning based classification system for overlapping data and irregular repetititve signals

Master'sMASTER OF SCIENC

COMPREHENSIVE PERFORMANCE EVALUATION AND OPTIMIZATION OF HIGH THROUGHPUT SCANNING MICROSCOPY FOR METAPHASE CHROMOSOME IMAGING

Specimen scanning is a critically important tool for diagnosing the genetic diseases in today’s hospital. In order to reduce the clinician’s work load, many investigations have been conducted on developing automatic sample screening techniques in the last twenty years. However, the currently commercialized scanners can only accomplish the low magnification sample screening (i.e. under 10× objective lens), and still require clinicians’ manual operation for the high magnification image acquisition and confirmation (i.e. under 100× objective lens). Therefore, a new high throughput scanning method is recently proposed to continuously scan the specimen and select the clinically analyzable cells. In the medical imaging lab, University of Oklahoma, a prototype of high throughput scanning microscopy is built based on the time delay integration (TDI) line scanning detector. This new scanning method, however, raises several technical challenges for evaluating and optimizing the performance. First, we need to use the clinical samples to compare this new prototype with the conventional two-step scanners. Second, the system DOF should be investigated to assess the impact on clinically analyzable metaphase chromosomes. Further, in order to achieve the optimal results, we should carefully assess and select the auto-focusing methods for the high throughput scanning system. Third, we need to optimize the scanning scheme by finding the optimal trade-off between the image quality and efficiency. Finally, analyzing the performance of the various image features is meaningful for improving the performance of the computer aided detection (CAD) scheme under the high throughput scanning condition. The purpose of this dissertation is to comprehensively evaluate the performance of the high throughput scanning prototype. The first technical challenge was solved by the first investigation, which utilized a number of 9 slides from five patients to compare the detecting performance of the high throughput scanning prototype. The second and third studies were performed for the second technical challenge. In the second study, we first theoretically computed the DOF of our prototype and then experimentally measured the system DOF. After that, the DOF impact was analyzed using cytogenetic images from different pathological specimens, under the condition of two objective lenses of 60× (dry, N.A. = 0.95) and 100× (oil, N.A. = 1.25). In the third study, five auto-focusing functions were investigated using metaphase chromosome images. The performance of these different functions was compared using four widely accepted criteria. The fourth and fifth investigations were designed for the third technical challenge. The fourth study objectively assessed chromosome band sharpness by a gradient sharpness function. The sharpness of the images captured from standard resolution target and several pathological chromosomes was objectively evaluated by the gradient sharpness function. The fifth study presented a new slide scanning scheme, which only applies the auto-focusing operations on limited locations. The focusing position was adjusted very quickly by linear interpolation for the other locations. The sixth study was aimed for the fourth technical challenge. The study investigated 9 different feature extraction methods for the CAD modules applied on our high throughput scanning prototype. A certain amount of images were first acquired from 200 bone marrow cells. Then the tested features were performed on these images and the images containing clinically meaningful chromosomes were selected using each feature individually. The identifying accuracy of each feature was evaluated using the receiver operating characteristic (ROC) method. In this dissertation, we have the following results. First, in most cases, we demonstrated that the high throughput scanning can select more diagnostic images depicting clinically analyzable metaphase chromosomes. These selected images were acquired with adequate spatial resolution for the following clinical interpretation. Second, our results showed that, for the commonly used pathological specimens, the metaphase chromosome band patterns are clinically recognizable when these chromosomes were obtained within 1.5 or 1.0 μm away from the focal plane, under the condition of applying the two 60× or 100× objective lenses, respectively. In addition, when scanning bone marrow and blood samples, the Brenner gradient and threshold pixel counting methods can achieve the optimal performance, respectively. Third, we illustrated that the optimal scanning speed of clinical samples is 0.8 mm/s, for which the captured image sharpness is optimized. When scanning the blood sample slide with an auto-focusing distance of 6.9 mm, the prototype obtained an adequate number of analyzable metaphase cells. More useful cells can be captured by increasing the auto-focusing operations, which may be needed for the high accuracy diagnosis. Finally, we found that the optimal feature for the online CAD scheme is the number of the labeled regions. When applying the offline CAD scheme, the satisfactory results can be achieved by combining four different features including the number of the labeled regions, average region area, average region pixel value, and the standard deviation of the either region circularity or distance. Although these investigations are encouraging, there exist several limitations. First, the number of the specimens is limited in most of the assessments. Second, some important impacts, such as the DOF of human eye and the sample thickness, are not considered. Third, more recently proposed algorithms and image features are not used for the evaluation. Therefore, several further studies are planned, which may provide more meaningful information for improving the scanning efficiency and image quality. In summary, we believe that the high throughput scanning may be extensively applied for diagnosing genetic diseases in the future

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv