Recognition of DNA Splice Junction via Machine Learning Approaches

Successful recognition of splice junction sites of human DNA sequences was achieved via three machine learning approaches. Both unsupervised (Kohonen's Self-Organizing Map, KSOM) and supervised (Back-propagation Neural Network, BNN; and Support Vector Machine, SVM) machine learning techniques were used for the classification of sequences from the testing set into one of three categories: transition from exon to intron, transition from intron to exon, and no transition. The dataset used in this study is comprised of 1,424 DNA sequences obtained from the National Center for Bioinformatics Information (NCBI). Performance of the machine learning approaches were assessed by the construction of learning models from 1,000 sequences of the training set and evaluated on the 424 sequences of the testing set that is unknown to the learning model. Each sequence is a window of 32 nucleotides long with regions comprising -15 to +15 nucleotides from the dinucleotide splice site. Since the nucleotides (A, C, G, and T) are represented by four digit binary code (e.g. 0001, 0010, 0100, and 1000) the number of descriptors increased from 32 to 128. The performance of machine learning techniques in order of increasing accuracy are as follows SVM > BNN > KSOM, suggesting that SVM is a robust method in the identification of unknown splice site. Although KSOM gave lower prediction accuracy than the two supervised methods, it is fascinating that it was able to make such prediction based only on knowledge of the input whereas the supervised method requires that the output be known during training. It is expected that the Support Vector Machine method can provide a powerful computational tool for predicting the splice junction sites of uncharacterized DNA

POPISK: T-cell reactivity prediction using support vector machines and string kernels

BACKGROUND: Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. RESULTS: This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. CONCLUSIONS: A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK

Higher-order epistasis and phenotypic prediction

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA [Formula: see text] splice sites, for which we also validate our model predictions via additional low-throughput experiments

Learning a peptide-protein binding affinity predictor with kernel ridge regression

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of accurately predicting the binding affinity of any peptide to any protein. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value < 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. The method should be of value to a large segment of the research community with the potential to accelerate peptide-based drug and vaccine development.Comment: 22 pages, 4 figures, 5 table

Efficient Training of Graph-Regularized Multitask SVMs

We present an optimization framework for graph-regularized multi-task SVMs based on the primal formulation of the problem. Previous approaches employ a so-called multi-task kernel (MTK) and thus are inapplicable when the numbers of training examples n is large (typically n < 20,000, even for just a few tasks). In this paper, we present a primal optimization criterion, allowing for general loss functions, and derive its dual representation. Building on the work of Hsieh et al. [1,2], we derive an algorithm for optimizing the large-margin objective and prove its convergence. Our computational experiments show a speedup of up to three orders of magnitude over LibSVM and SVMLight for several standard benchmarks as well as challenging data sets from the application domain of computational biology. Combining our optimization methodology with the COFFIN large-scale learning framework [3], we are able to train a multi-task SVM using over 1,000,000 training points stemming from 4 different tasks. An efficient C++ implementation of our algorithm is being made publicly available as a part of the SHOGUN machine learning toolbox [4]

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

Regularized and robust regression methods for high dimensional data

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Recently, variable selection in high-dimensional data has attracted much research interest. Classical stepwise subset selection methods are widely used in practice, but when the number of predictors is large these methods are difficult to implement. In these cases, modern regularization methods have become a popular choice as they perform variable selection and parameter estimation simultaneously. However, the estimation procedure becomes more difficult and challenging when the data suffer from outliers or when the assumption of normality is violated such as in the case of heavy-tailed errors. In these cases, quantile regression is the most appropriate method to use. In this thesis we combine these two classical approaches together to produce regularized quantile regression methods. Chapter 2 shows a comparative simulation study of regularized and robust regression methods when the response variable is continuous. In chapter 3, we develop a quantile regression model with a group lasso penalty for binary response data when the predictors have a grouped structure and when the data suffer from outliers. In chapter 4, we extend this method to the case of censored response variables. Numerical examples on simulated and real data are used to evaluate the performance of the proposed methods in comparisons with other existing methods

End-to-end learning framework for circular RNA classification from other long non-coding RNAs using multi-modal deep learning.

Over the past two decades, a circular form of RNA (circular RNA) produced from splicing mechanism has become the focus of scientific studies due to its major role as a microRNA (miR) ac tivity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is a vital operation for continued comprehension of their biogenesis and purpose. Prediction of circular RNA can be achieved by first distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs (lncRNAs), and finally pre dicting circular RNAs from other lncRNAs. However, available tools to distinguish circular RNAs from other lncRNAs have only reached 80% accuracy due to the difficulty of classifying circular RNAs from other lncRNAs. Therefore, the availability of a faster, more accurate machine learning method for the identification of circular RNAs, which will take into account the specific features of circular RNA, is essential in the development of systematic annotation. Here we present an End to-End multimodal deep learning framework, our tool, to classify circular RNA from other lncRNA. It fuses a RCM descriptor, an ACNN-BLSTM sequence descriptor, and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that our tool is not only faster compared to existing tools but also eclipses other tools by an over 12% increase in accuracy. Another interesting result found from analysis of a ACNN-BLSTM sequence descriptor is that circular RNA sequences share the characteristics of the coding sequence

Exploring the genetic contribution to idiopathic Parkinson disease

Background: Parkinson disease (PD) is a major cause of death and disability and has a devastating global socioeconomic impact. It affects 1-2% of the population above the age of 65 and its prevalence increases as the population ages. Several biological processes have been implicated in Parkinson disease, including mitochondrial dysfunction, aberrant protein clearance, and neuroinflammation. To which degree these processes are cause, effect or bystander to disease initiation and progression, remains however largely unknown. Having limited understanding of the mechanisms underlying the pathogenesis and pathophysiology of Parkinson disease, we are unable to develop disease-modifying therapies and patients face a future of progressive disability and premature death. There is a clear hereditary component to idiopathic PD, established through both twin studies and genome-wide association studies. However, only a minor fraction of the total estimated heritability can be explained by known associated genetic variability. It has been hypothesized that the cumulative effects of rare, low-impact mutations spread across genes and biological pathways could explain some of this “missing heritability”. Aims: The aim of this work was to explore the genetic contribution to idiopathic PD, focusing on the cumulative effects of rare mutations. Materials and methods: The main study population utilized in all four papers was the ParkWest cohort, a Norwegian population-based cohort of incident PD. In paper I, ParkWest provided both cases and controls, including clinical longitudinal data up to and including 7 years after baseline. All ParkWest cases were whole-exome sequenced and combined with previously sequenced control samples to form the genetic cohort utilized in papers II-IV. Additionally, a whole-exome sequencing cohort from the Parkinson Progression Markers Initiative was used in papers II-IV. Finally, a publicly available chip-genotyped dataset (NeuroX) from the International Parkinson’s Disease Genomics Consortium was used as a replication cohort in paper IV. In paper I, we characterized the familial aggregation of Parkinson disease in the ParkWest cohort and explored the effect of family history on disease progression. Subsequently, we used genetic data from multiple cohorts to assess the impact of rare, protein-altering mutations in mitochondrial biological pathways (paper III) and in genes previously linked to PD (paper II and IV). Results and conclusions: We show that, while familial aggregation is present in our Norwegian cohort, this has a slightly lower effect size compared to previous studies. Through regression analysis we also show that having a family history of PD among first degree relatives is associated with a slightly milder phenotype, which may be due to genetic variability. In paper II, we attempted to replicate the results of a recently published study reporting an association between genetic variation in the TRAP1 gene and Parkinson disease. Our analyses did not replicate this association in our Norwegian cohort. Moreover, using stricter quality control parameters abolished the association in the same dataset used in the original study. Our results do not support the proposed role of TRAP1 in idiopathic PD. In paper III, we sought to investigate the role of rare, amino acid changing variation in molecular pathways related to mitochondrial function. Using the sequence kernel association (SKAT) test, we detected a statistically significant enrichment in the pathway of mitochondrial DNA maintenance. Impaired mitochondrial DNA homeostasis has previously been shown to be present in PD neurons, and our results indicate that this dysfunction could be partly mediated by inherited genetic mutations. In paper IV, we performed a targeted single gene and gene-set association study on genes that had previously been implicated in PD through genome-wide association studies. We identified 303 genes of interest, but did not find statistically significant associations, either in the single gene or gene-set analyses. Our results do not therefore support a major role for rare variant enrichment in genes tagged by GWAS, but cannot rule out effects with small effect sizes.Doktorgradsavhandlin