Mapping high-dimensional data onto a relative distance plane--an exact method for visualizing and characterizing high-dimensional patterns.

We introduce a distance (similarity)-based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances between points with respect to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). As only a single calculation of a distance matrix is required, this method is computationally efficient, an essential requirement for any exploratory data analysis. The data visualization afforded by this representation permits a rapid assessment of class pattern distributions. In particular, we can determine with a simple statistical test whether both training and validation sets of a 2-class, high-dimensional dataset derive from the same class distributions. We can explore any dataset in detail by identifying the subset of reference pairs whose members belong to different classes, cycling through this subset, and for each pair, mapping the remaining patterns. These multiple viewpoints facilitate the identification and confirmation of outliers. We demonstrate the effectiveness of this method on several complex biomedical datasets. Because of its efficiency, effectiveness, and versatility, one may use the RDP representation as an initial, data mining exploration that precedes classification by some classifier. Once final enhancements to the RDP mapping software are completed, we plan to make it freely available to researchers.

Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy.

BACKGROUND: The aim was to develop robust classifiers to analyse magnetic resonance spectroscopy (MRS) data of fine-needle aspirates taken from breast tumours. The resulting data could provide computerized, classification-based diagnosis and prognostic indicators. METHODS: Fine-needle aspirate biopsies obtained at the time of surgery for both benign and malignant breast diseases were analysed by one-dimensional proton MRS at 8.5 Tesla. Diagnostic correlation was performed between the spectra and standard pathology reports, including the presence of vascular invasion by the primary cancer and involvement of the excised axillary lymph nodes. RESULTS: Malignant tissue was distinguished from benign lesions with an overall accuracy of 93 per cent. From the same spectra, lymph node involvement was predicted with an overall accuracy of 95 per cent, and tumour vascular invasion with an overall accuracy of 94 per cent. CONCLUSION: The pathology, nodal involvement and tumour vascular invasion were predicted by computerized statistical classification of the proton MRS spectrum from a fine-needle aspirate biopsy taken from the primary breast lesion.

Disease pattern recognition testing for rheumatoid arthritis using infrared spectra of human serum.

BACKGROUND: In view of the importance of the diagnosis of rheumatoid arthritis, a novel diagnostic method based on spectroscopic pattern recognition in combination with laboratory parameters such as the rheumatoid factor is described in the paper. Results of a diagnostic study of rheumatoid arthritis employing this method are presented. METHOD: The method uses classification of infrared (IR) spectra of serum samples by means of discriminant analysis. The spectroscopic pattern yielding the highest discriminatory power is found through a complex optimization procedure. In the study, IR spectra of 384 serum samples have been analyzed in this fashion with the objective of differentiating between rheumatoid arthritis and healthy subjects. In addition, the method integrates results from the classification with levels of the rheumatoid factor in the sample by optimized classifier weighting, in order to enhance classification accuracy, i.e. sensitivity and specificity. RESULTS: In independent validation, sensitivity and specificity of 84% and 88%, respectively, have been obtained purely on the basis of spectra classification employing a classifier designed specifically to provide robustness. Sensitivity and specificity are improved by 1% and 6%, respectively, upon inclusion of rheumatoid factor levels. Results for less robust methods are also presented and compared to the above numbers. CONCLUSION: The discrimination between RA and healthy by means of the pattern recognition approach presented here is feasible for IR spectra of serum samples. The method is sufficiently robust to be used in a clinical setting. A particular advantage of the method is its potential use in RA diagnosis at early stages of the disease.

The use of 1H magnetic resonance spectroscopy in inflammatory bowel diseases: distinguishing ulcerative colitis from Crohn's disease.

OBJECTIVES: The distinction between the two major forms of inflammatory bowel diseases (IBD), i.e., ulcerative colitis (UC) and Crohn's disease is sometimes difficult and may lead to a diagnosis of indeterminate colitis. We have used 1H magnetic resonance spectroscopy (MRS) combined with multivariate methods of spectral data analysis to differentiate UC from Crohn's disease and to evaluate normal-appearing mucosa in IBD. METHODS: Colon mucosal biopsies (45 UC and 31 Crohn's disease) were submitted to 1H MRS, and multivariate analysis was applied to distinguish the two diseases. A second study was performed to test endoscopically and histologically normal biopsies from IBD patients. A classifier was developed by training on 101 spectra (76 inflamed IBD tissues and 25 normal control tissues). The spectra of 38 biopsies obtained from endoscopically and histologically normal areas of the colons of patients with IBD were put into the validation test set. RESULTS: The classification accuracy between UC and Crohn's disease was 98.6%, with only one case of Crohn's disease and no cases of UC misclassified. The diagnostic spectral regions identified by our algorithm included those for taurine, lysine, and lipid. In the second study, the classification accuracy between normal controls and IBD was 97.9%. Only 47.4% of the endoscopically and histologically normal IBD tissue spectra were classified as true normals; 34.2% showed "abnormal" magnetic resonance spectral profiles, and the remaining 18.4% could not be classified unambiguously. CONCLUSIONS: There is a strong potential for MRS to be used in the accurate diagnosis of indeterminate colitis; it may also be sensitive in detecting preclinical inflammatory changes in the colon.

Disease pattern recognition in infrared spectra of human sera with diabetes mellitus as an example.

To benefit from the full information content of the mid-IR spectra of human sera, we directly related the overall shape of the spectra to the donors' disease states. For this approach of disease pattern recognition we applied cluster analysis and discriminant analysis to the example of the disease states diabetes type 1, diabetes type 2, and healthy. In a binary, supervised classification of any pair of these disease states we achieved specificities and sensitivities of approximately 80% within our data set.

Near-optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra.

We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is genetic algorithm-guided. It is compared with our enhanced version of the standard forward selection algorithm. Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. The power of the methods is demonstrated on two biomedical examples: the discrimination between meningioma and astrocytoma in brain tissue biopsies, and the classification of colorectal biopsies into normal and tumour classes. Both preprocessing methods lead to classification accuracies over 97% for the two examples.

Computerized consensus diagnosis: a classification strategy for the robust analysis of MR spectra. I. Application to 1H spectra of thyroid neoplasms.

We introduce and apply a new classification strategy we call computerized consensus diagnosis (CCD). Its purpose is to provide robust, reliable classification of biomedical data. The strategy involves the cross-validated training of several classifiers of diverse conceptual and methodological origin on the same data, and appropriately combining their outcomes. The strategy is tested on proton magnetic resonance spectra of human thyroid biopsies, which are successfully allocated to normal or carcinoma classes. We used Linear Discriminant Analysis, a Neural Net-based method, and Genetic Programming as independent classifiers on two spectral regions, and chose the median of the six classification outcomes as the consensus. This procedure yielded 100% specificity and 100% sensitivity on the training sets, and 100% specificity and 98% sensitivity on samples of known malignancy in the test sets. We discuss the necessary steps any classification approach must take to guarantee reliability, and stress the importance of fuzziness and undecidability in robust classification.

[Molecular-biological study of vaccinia virus genome. II. Localization and nucleotide sequence of vaccinia virus genes coding for proteins 36K and 12K]. / Molekuliarno-biologicheskoe izuchenie genoma virusa ospovaktsiny. II. Lokalizatsiia i opredelenie posledovatel'nosti nukleotidov genov virusa ospovaktsiny, kodiruiushchikh belki 36K i 12K.

Genes encoding virus-specific late proteins with molecular mass 36 kDa and 12 kDa were mapped in HindIII-P DNA fragment of vaccinia virus strain L-IVP by hybrid selection of RNA to cloned DNA fragments followed by in vitro translation. RNA origin site of the 36K protein was detected in HindIII-J fragment. Nucleotide sequences of these genes were determined. Amino acid sequences of the 36K and 12K polypeptides were compared with the protein bank PIR.

[Rapid automated synthesis on paper disks of oligodeoxyribonucleotides constituting the promoter fragment of the vaccinia virus genome]. / Bystryi avtomatizirovannyi sintez na bumazhnykh diskakh oligodeoksiribonukleotidov, sostavliaiushchikh promotornyi fragment genoma virusa ospovaktsiny.

For automation of segmental solid-phase synthesis a simple approach leading to the optimal scheme of synthesis of a large numbers of oligonucleotides in one reaction vessel has been proposed. An advantage of the scheme as compared with synthesis in four reaction vessels is a lower number of condensation steps and increased economy of the process. Sixteen oligodeoxyribonucleotides constituting promoter fragment of the viral genome have been synthesised by the modified segmental method on "Victoriya-2" synthesizer according to the optimal scheme.