Classification of gene functions using support vector machine for time-course gene expression data

Dynamic microRNA activity identifies therapeutic targets in trastuzumab-resistant HER2+ breast cancer

MicroRNAs (miRNAs) are implicated in numerous physiologic and pathologic processes, such as the development of resistance to chemotherapy. Determining the role of miRNAs in these processes is often accomplished through measuring miRNA abundance by polymerase chain reaction, sequencing, or microarrays. We have developed a system for the large-scale monitoring of dynamic miRNA activity and have applied this system to identify the contribution miRNA activity to the development of trastuzumab resistance in a cell model of HER2⁺ breast cancer. MiRNA activity measurements identified significantly different activity levels between BT474 cells (HER2 ⁺ breast cancer) and BT474R cells (HER2 ⁺ breast cancer cells selected for resistance to trastuzumab). We created a library of 32 miRNA reporter constructs, which were delivered by lentiviral transduction into cells, and miRNA activity was quantified by bioluminescence imaging. Upon treatment with the bioimmune therapy, trastuzumab, the activity of 11 miRNAs were significantly altered in parental BT474 cells, and 20 miRNAs had significantly altered activity in the therapy-resistant BT474R cell line. A combination of statistical, network and classification analysis was applied to the dynamic data, which identified miR-21 as a controlling factor in trastuzumab response. Our data suggested downregulation of miR-21 activity was associated with resistance, which was confirmed in an additional HER2 ⁺ breast cancer cell line, SKBR3. Collectively, the dynamic miRNA activity measurements and analysis provided a system to identify new potential therapeutic targets in treatment-resistant cancers.

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients

Background

DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves.

Methods

Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status.

Results

Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates.

Conclusion

Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.

Applications of functional data analysis: A systematic review

BACKGROUND

Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods.

METHODS

A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995-2010. Papers reporting methodological considerations only were excluded, as were non-English articles.

RESULTS

In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data.

CONCLUSIONS

Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.

Non invasive clinical assessment of trunk deformities associated with scoliosis

Besides the spinal deformity, scoliosis modifies notably the general appearance of the trunk resulting in trunk rotation, imbalance and asymmetries which constitutes patients' major concern. Existing classifications of scoliosis, based on the type of spinal curve as depicted on radiographs, are currently used to guide treatment strategies. Unfortunately, even though a perfect correction of the spinal curve is achieved, some trunk deformities remain, making patients dissatisfied with the treatment received. The purpose of this study is to identify possible shape patterns of trunk surface deformity associated with scoliosis. First, trunk surface is represented by a multivariate functional trunk shape descriptor based on 3D clinical measurements computed on cross sections of the trunk. Then, the classical formulation of hierarchical clustering is adapted to the case of multivariate functional data and applied to a set of 236 trunk surface 3D reconstructions. The highest internal validity is obtained when considering 11 clusters that explain up to 65% of the variance in our dataset. Our clustering result shows a concordance with the radiographic classification of spinal curves in 68% of the cases. As opposed to radiographic evaluation, the trunk descriptor is three-dimensional and its functional nature offers a compact and elegant description of not only the type, but also the severity and extent of the trunk surface deformity along the trunk length. In future work, new management strategies based on the resulting trunk shape patterns could be thought of in order to improve the esthetic outcome after treatment, and thus patients satisfaction.

Multilevel analysis of trunk surface measurements for noninvasive assessment of scoliosis deformities

STUDY DESIGN

Reliability study.

OBJECTIVES

To assess between-acquisition reliability of new multilevel trunk cross sections measurements, in order to define what is a real change when comparing 2 trunk surface acquisitions of a same patient, before and after surgery or throughout the clinical monitoring.

SUMMARY OF BACKGROUND DATA

Several cross-sectional surface measurements have been proposed in the literature for noninvasive assessment of trunk deformity in patients with adolescent idiopathic scoliosis (AIS). However, only the maximum values along the trunk are evaluated and used for monitoring progression and assessing treatment outcome.

METHODS

Back surface rotation (BSR), trunk rotation (TR), and coronal and sagittal trunk deviation are computed on 300 cross sections of the trunk. Each set of 300 measures is represented as a single functional data, using a set of basis functions. To evaluate between-acquisition variability at all trunk levels, a test-retest reliability study is conducted on 35 patients with AIS. A functional correlation analysis is also carried out to evaluate any redundancy between the measurements.

RESULTS

Each set of 300 measures was successfully described using only 10 basis functions. The test-retest reliability of the functional measurements is good to very good all over the trunk, except above the shoulders level. The typical errors of measurement are between 1.20° and 2.2° for the rotational measures and between 2 and 6 mm for deviation measures. There is a very strong correlation between BSR and TR all over the trunk, a moderate correlation between coronal trunk deviation and both BSR and TR, and no correlation between sagittal trunk deviation and any other measurement.

CONCLUSION

This novel representation of trunk surface measurements allows for a global assessment of trunk surface deformity. Multilevel trunk measurements provide a broader perspective of the trunk deformity and allow a reliable multilevel monitoring during clinical follow-up of patients with AIS and a reliable assessment of the esthetic outcome after surgery.

HIV-1 CRF01_AE coreceptor usage prediction using kernel methods based logistic model trees

The determination of HIV-1 coreceptor usage plays a major role in HIV treatment. Since Maraviroc has been used in a treatment for patients those exclusively harbor R5-tropic strains, the efficient performance of classifying HIV-1 coreceptor usage can help choose the most advantaged HIV treatment. In general, HIV-1 variants are classified as R5-tropic and X4-tropic or dual/mixed tropic based on their coreceptor usages. The classification of the coreceptor usage has been developed by using the various computational methods or genotypic algorithms based on V3 amino acid sequences. Most genotypic tools have been designed based on a data set of the HIV-1 subtype B that seemed to be reliable only for this subtype. However, the performance of these tools decreases in non-B subtypes. In this study, the support vector machine (SVM) method has been used to classify the HIV-1 coreceptor. To develop an efficient SVM classifier, we present a feature selector using the logistic model tree (LMT) method to select the most relevant positions from the V3 amino acid sequences. Our approach achieves as high as 97.8% accuracy, 97.7% specificity, and 97.9% sensitivity measured by ten-fold cross-validation on 273 sequences.