A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

PurposeAdaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.Methods1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB^® homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.ResultsUsing retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.ConclusionsWarping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints.     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.     

Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine

Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen’s self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.     

基于小波特征分析的手指动作识别研究

目的:本文利用表面肌电(sEMG)信号来研究多种手指组合动作的识别问题。方法:在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机(SVM)分类算法来实现手指组合动作的识别。结果:动作识别率最高达到100%。结论:所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。     

Using string kernel to predict signal peptide cleavage site based on subsite coupling model

Summary. Owing to the importance of signal peptides for studying the molecular mechanisms of genetic diseases, reprogramming cells for gene therapy, and finding new drugs for healing a specific defect, it is in great demand to develop a fast and accurate method to identify the signal peptides. Introduction of the so-called {−3,−1, +1} coupling model (Chou, K. C.: Protein Engineering, 2001, 14–2, 75–79) has made it possible to take into account the coupling effect among some key subsites and hence can significantly enhance the prediction quality of peptide cleavage site. Based on the subsite coupling model, a kind of string kernels for protein sequence is introduced. Integrating the biologically relevant prior knowledge, the constructed string kernels can thus be used by any kernel-based method. A Support vector machines (SVM) is thus built to predict the cleavage site of signal peptides from the protein sequences. The current approach is compared with the classical weight matrix method. At small false positive ratios, our method outperforms the classical weight matrix method, indicating the current approach may at least serve as a powerful complemental tool to other existing methods for predicting the signal peptide cleavage site. The software that generated the results reported in this paper is available upon requirement, and will appear at http://www.pami.sjtu.edu.cn/wm. An erratum to this article is available at .     

Extraction of leukemia specific glycan motifs in humans by computational glycomics

There have been almost no standard methods for conducting computational analyses on glycan structures in comparison to DNA and proteins. In this paper, we present a novel method for extracting functional motifs from glycan structures using the KEGG/GLYCAN database. First, we developed a new similarity measure for comparing glycan structures taking into account the characteristic mechanisms of glycan biosynthesis, and we tested its ability to classify glycans of different blood components in the framework of support vector machines (SVMs). The results show that our method can successfully classify glycans from four types of human blood components: leukemic cells, erythrocyte, serum, and plasma. Next, we extracted characteristic functional motifs of glycans considered to be specific to each blood component. We predicted the substructure alpha-D-Neup5Ac-(2-->3)-beta-D-Galp-(1-->4)-D-GlcpNAc as a leukemia specific glycan motif. Based on the fact that the Agrocybe cylindracea galectin (ACG) specifically binds to the same substructure, we conducted an experiment using cell agglutination assay and confirmed that this fungal lectin specifically recognized human leukemic cells.     

Low-frequency Fourier spectrum for predicting membrane protein types

Cell membranes are vitally important to living cells. Although the infrastructure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Knowledge of membrane protein types often provides crucial hints toward determining the function of an uncharacterized membrane protein. With the avalanche of new protein sequences generated in the post-genomic era, it is highly demanded to develop a high throughput tool in identifying the type of newly found membrane proteins according to their primary sequences, so as to timely annotate them for reference usage in both basic research and drug discovery. To realize this, the key is to establish a powerful identifier that can catch their characteristic sequence patterns for different membrane protein types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo-amino acid composition [K.C. Chou, PROTEINS: Struct., Funct., Genet. 43 (2001) 246-255], the low-frequency Fourier spectrum analysis is introduced. The merits by doing so are that the sequence pattern information can be more effectively incorporated into a set of discrete components, and that all the existing prediction algorithms can be straightforwardly used on such a formulation for protein samples. High success rates were observed by the re-substitution test, jackknife test, and independent dataset test, indicating that the low-frequency Fourier spectrum approach may become a very useful tool for membrane protein type prediction. The novel approach also holds a high potential for predicting many other attributes of proteins.     

Proteome analysis by bio-active ceramic water in rat liver: contribution to antioxidant enzyme expression, SOD I

The purpose of this study was to investigate the protective effect of bio-active ceramic water on rat liver. Male Wistar rats were divided into 4 groups of 15 animals each. Groups 1 and 2 were fed bio-active ceramic water and tap water for 4 months, respectively. Groups 3 and 4 were treated with the same condition for 12 months. The changes of protein expression of these four groups were investigated using two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Eleven proteins were significantly up-regulated in bio-active ceramic water treated rat liver including aldehyde dehydrogenase I and II, albumin, fructose-1,6-bisphosphatase, and superoxide dismutase I (SOD I). The most highly expressed protein, SOD I with up-regulated enzyme activity, was confirmed by immunoblots as a major antioxidant capable of detoxifying normally generated reactive oxygen species. These data suggest that modified protein expression of the liver contributes to enhance liver function.     

Less harmful acidic degradation of poly(lacticco-glycolic acid) bone tissue engineering scaffolds through titania nanoparticle addition

In the last 10 years, biodegradable aliphatic polyesters, such as poly(lactic-co-glycolic acid) (PLGA), have attracted increasing attention for their use as scaffold materials in bone tissue engineering because their degradation products can be removed by natural metabolic pathways. However, one main concern with the use of these specific polymers is that their degradation products reduce local pH, which in turn induces an inflammatory reaction and damages bone cell health at the implant site. Thus, the objective of the present in vitro study was to investigate the degradation behavior of PLGA when added with dispersed titania nanoparticles. The results of this study provided the first evidence that the increased dispersion of nanophase titania in PLGA decreased the harmful change in pH normal for PLGA degradation. Moreover, previous studies have demonstrated that the increased dispersion of titania nanoparticles into PLGA significantly improved osteoblast (bone-forming cell) functions (such as adhesion, collagen synthesis, alkaline phosphatase activity, and calcium-containing minerals deposition). In this manner, nanophase titania-PLGA composites may be promising scaffold materials for more effective orthopedic tissue engineering applications.