Decreased functional connectivity within a language subnetwork in benign epilepsy with centrotemporal spikes.

Objective: Benign epilepsy with centrotemporal spikes (BECTS, also known as Rolandic epilepsy) is a common epilepsy syndrome that is associated with literacy and language impairments. The neural mechanisms of the syndrome are not known. The primary objective of this study was to test the hypothesis that functional connectivity within the language network is decreased in children with BECTS.

Autoantibodies to MUC1 glycopeptides cannot be used as a screening assay for early detection of breast, ovarian, lung or pancreatic cancer.

Background: Autoantibodies have been detected in sera before diagnosis of cancer leading to interest in their potential as screening/early detection biomarkers. As we have found autoantibodies to MUC1 glycopeptides to be elevated in early-stage breast cancer patients, in this study we analysed these autoantibodies in large population cohorts of sera taken before cancer diagnosis.

Methods: Serum samples from women who subsequently developed breast cancer, and aged-matched controls, were identified from UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) and Guernsey serum banks to formed discovery and validation sets.

Determining confidence of predicted interactions between HIV-1 and human proteins using conformal method.

Identifying protein-protein interactions (PPI's) is critical for understanding virtually all cellular molecular mechanisms. Previously, predicting PPI's was treated as a binary classification task and has commonly been solved in a supervised setting which requires a positive labeled set of known PPI's and a negative labeled set of non-interacting protein pairs. In those methods, the learner provides the likelihood of the predicted interaction, but without a confidence level associated with each prediction.

Early detection of ovarian cancer in samples pre-diagnosis using CA125 and MALDI-MS peaks.

Aim: A nested case-control discovery study was undertaken to test whether information within the serum peptidome can improve on the utility of CA125 for early ovarian cancer detection.

Materials And Methods: High-throughput matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS) was used to profile 295 serum samples from women pre-dating their ovarian cancer diagnosis and from 585 matched control samples. Classification rules incorporating CA125 and MS peak intensities were tested for discriminating ability.

Autoantibodies to aberrantly glycosylated MUC1 in early stage breast cancer are associated with a better prognosis.

Introduction: Detection of serum biomarkers for early diagnosis of breast cancer remains an important goal. Changes in the structure of O-linked glycans occur in all breast cancers resulting in the expression of glycoproteins that are antigenically distinct. Indeed, the serum assay widely used for monitoring disease progression in breast cancer (CA15.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling.

Objectives: Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance.

Materials And Methods: Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples.

Reliable confidence measures for medical diagnosis with evolutionary algorithms.

Conformal Predictors (CPs) are machine learning algorithms that can provide predictions complemented with valid confidence measures. In medical diagnosis, such measures are highly desirable, as medical experts can gain additional information for each machine diagnosis. A risk assessment in each prediction can play an important role for medical decision making, in which the outcome can be critical for the patients.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools.

Peptides generated ex vivo from serum proteins by tumor-specific exopeptidases are not useful biomarkers in ovarian cancer.

Background: The serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls.

Predicting clinical outcome in patients diagnosed with synchronous ovarian and endometrial cancer.

Purpose: Patients with synchronous ovarian and endometrial cancers may represent cases of a single primary tumor with metastasis (SPM) or dual primary tumors (DP). The diagnosis given will influence the patient's treatment and prognosis. Currently, a diagnosis of SPM or DP is made using histologic criteria, which are frequently unable to make a definitive diagnosis.

Cancer informatics by prototype networks in mass spectrometry.

Objective: Mass spectrometry has become a standard technique to analyze clinical samples in cancer research. The obtained spectrometric measurements reveal a lot of information of the clinical sample at the peptide and protein level. The spectra are high dimensional and, due to the small number of samples a sparse coverage of the population is very common.

Clinical mass spectrometry proteomic diagnosis by conformal predictors.

The paper describes an application of conformal predictors to diagnose breast cancer using proteomic mass spectrometry data provided by Leiden University Medical Center. Unlike many conventional classification systems, this approach allows us not just to classify samples, but add valid measures of confidence in our predictions for individual patients.

Preanalytic influence of sample handling on SELDI-TOF serum protein profiles.

Background: High-throughput proteomic methods for disease biomarker discovery in human serum are promising, but concerns exist regarding reproducibility of results and variability introduced by sample handling. This study investigated the influence of different preanalytic handling methods on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) protein profiles of prefractionated serum. We investigated whether older collections with longer sample transit times yield useful protein profiles, and sought to establish the most feasible collection methods for future clinical proteomic studies.

Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines.

We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively.

Prospective gene expression analysis accurately subtypes acute leukaemia in children and establishes a commonality between hyperdiploidy and t(12;21) in acute lymphoblastic leukaemia.

We have prospectively analysed and correlated the gene expression profiles of children presenting with acute leukaemia to the Royal London and Great Ormond Street Hospitals with morphological diagnosis, immunophenotype and karyotype. Total RNA extracted from freshly sorted blast cells was obtained from 84 lymphoblastic [acute lymphoblastic leukaemia (ALL)], 20 myeloid [acute myeloid leukaemia (AML)] and three unclassified acute leukaemias and hybridised to the high density Affymetrix U133A oligonucleotide array. Analysis of variance and significance analysis of microarrays was used to identify discriminatory genes.

Plant promoter prediction with confidence estimation.

Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSP-TCM for the prediction of plant promoters that also provides confidence of the prediction. The program was trained on 132 and 104 sequences and tested on 40 and 25 sequences (containing TATA and TATA-less promoters, respectively) with known transcription start sites (TSSs).

Sequence alignment kernel for recognition of promoter regions.

Unlabelled: In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition.

PlantProm: a database of plant promoter sequences.

PlantProm DB, a plant promoter database, is an annotated, non-redundant collection of proximal promoter sequences for RNA polymerase II with experimentally determined transcription start site(s), TSS, from various plant species. The first release (2002.01) of PlantProm DB contains 305 entries including 71, 220 and 14 promoters from monocot, dicot and other plants, respectively.

Statistical modelling in specific case analysis.

In spite of the problems associated with the development of a statistical approach in specific case analysis, the potential of two statistical methods, logistic regression and Bayesian belief networks, has been investigated and found encouraging in the context of a database relating to child murders with a sexual connotation. Continual collaboration between statisticians and detectives would be needed in the collection of the data, the choice and construction of the models, and the interpretation of the results.

Bayesian diagnostic probabilities without assuming independence of symptoms.

The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups.

A mathematical model of phytochrome - the receptor of photomorphogenetic processes in plants.

A mathematical model of the phytochrome pigment system is considered, the parameters of which are determined by solving the problem of identification on the basis of the dynamic characteristics of the photoreceptor obtained experimentally. The model is based on two photochemical and four dark reactions occurring in the phytochrome system. A study of the model permitted establishing the dynamics of processes in the photoreceptor and determining the relation between the behavior of the active form of phytochrome and the intensity and spectral distribution of light.