Support Vector Machines and Kernels for Computational Biology

ISSN:1553-734XISSN:1553-735

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk.

Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na+, the acetylcholine-sensitive and ultra-rapid K+ channels, and the Na+/Ca2+ exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: •allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk;•is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca2+ sparks);•is computationally more efficient than one-at-a-time parameter sensitivity analysis

Classification DNA sequences using Gap–Weighted subsequences kernel

The aim of this paper is to show experimental results of classification DNA sequences using gap–weighted subsequences kernel including the assess the expected error rate of a classification algorithm. The process involve a type of kernel specific with a classification algorithm for learn to recognize sites that regulate transcription, sites that can be detected in the laboratory as DNaseI hypersensitive sites (HSs) on DNA sequences. The classification algorithm is support vector machine (SVM), which learns by example to discriminate between two given classes of data. The DNA sequences are converted using gap–weighted subsequences kernel in a matrix kernel, which is processed by the classification algorithm to produce a model with the which we can predict the classification of new examples. It is important to know that a high accuracy with computational methods for the identification of the DNaseI hypersensitive sites would to help to speed up the functional annotation of the human genomePresentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Spaced seeds improve k-mer-based metagenomic classification

Metagenomics is a powerful approach to study genetic content of environmental samples that has been strongly promoted by NGS technologies. To cope with massive data involved in modern metagenomic projects, recent tools [4, 39] rely on the analysis of k-mers shared between the read to be classified and sampled reference genomes. Within this general framework, we show in this work that spaced seeds provide a significant improvement of classification accuracy as opposed to traditional contiguous k-mers. We support this thesis through a series a different computational experiments, including simulations of large-scale metagenomic projects. Scripts and programs used in this study, as well as supplementary material, are available from http://github.com/gregorykucherov/spaced-seeds-for-metagenomics.Comment: 23 page

mGene.web: a web service for accurate computational gene finding

We describe mGene.web, a web service for the genome-wide prediction of protein coding genes from eukaryotic DNA sequences. It offers pre-trained models for the recognition of gene structures including untranslated regions in an increasing number of organisms. With mGene.web, users have the additional possibility to train the system with their own data for other organisms on the push of a button, a functionality that will greatly accelerate the annotation of newly sequenced genomes. The system is built in a highly modular way, such that individual components of the framework, like the promoter prediction tool or the splice site predictor, can be used autonomously. The underlying gene finding system mGene is based on discriminative machine learning techniques and its high accuracy has been demonstrated in an international competition on nematode genomes. mGene.web is available at http://www.mgene.org/web, it is free of charge and can be used for eukaryotic genomes of small to moderate size (several hundred Mbp)