In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

https://downloads.hindawi.com/journals/cmmm/2013/867924.pdf

Machine learning approaches: from theory to application in schizophrenia.

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Gene clustering based on amino acid sequence similarity has been one of the most important problems and always challenging in molecular biology. The most conventional methods are based on alignment-technique. These methods cannot identify and classify sequences, especially when the lengths of sequence are long and unequal. Therefore, in order to classify fungal hexosaminidase amino acid sequences and put them in the right taxonomical group we evaluate the feasibility of computational free alignment methods based on machine learning classifiers such as SVM, KNN, SOM and ensemble technique. The classifiers have appropriately categorized large Dikarya hexosaminidase amino acid sequences as data sets according to their taxonomical groups in two phyla named, the “Ascomycota” and the “Basidiomycota”. Two statistical methods including paired t test and PCA were used for the feature selection and reduce the dimensionality of the features, respectively. Seven classifier performance metrics, randomized complete block design, pairwise Tukey’s honestly significant difference tests and the technique for order preference by similarity to ideal solution with modified k-fold cross validation have been used as tools in order to evaluate and ranking of classifiers. In this study, the effect of training data size on the classifier performance was investigated. The results showed that the rank and the performance of classifiers were depended on the training data size. The highest obtained values for the average overall accuracy of the following training data sizes, 80, 60, 40 and 20% using KNN, KNN, ensemble and ensemble classifier were 96.96, 95.81, 94.47 and 92.47%, respectively.

Free alignment classification of dikarya fungi using some machine learning methods

Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.

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Early prediction of developing spontaneous activity in cultured neuronal networks.

Rule-based classifiers have one major advantage over other classes of supervised learning algorithms: interpretability. They provide a means to read into a model and find how the features co-act in order to come to a classification outcome. This in turn enables the researcher to visualize the feature interactions and evaluate the key features that discern between different decision classes. The rules generated from these algorithms, however, can be very many and their analysis is not trivial. This is where proper visualization techniques enable the researcher to filter out clutter and see only important relationships. In addition, the next natural step for genomic data is to find out relationships between the interacting genes and biological networks is always a good starting place. In this study, we introduce VisuNet, a highly interactive, web-based tool for visualization of feature interactions in rule-based classifiers as well as annotation of genomic data with information on biological networks involved. VisuNet can be used with any rule-based classifiers such as decision trees and Rough-Sets, or any model from which rules can be extracted. The tool is hosted online at http://bioinf.icm.uu.se/~visunet/.

VisuNet: Visualizing Networks of feature interactions in rule-based classifiers

Support Vector Machines (SVM) is a statistical machine learning algorithm that has been used extensively in the past 3 years in computational biology. SVM has been widely used to detect, classify, and predict complex biological patterns. SVMs have been widely applied to many areas of bioinformatics, including protein function prediction, functional site recognition, transcription initiation site prediction, and gene expression data classification. This chapter gives a brief overview of SVM algorithm along with its specific applications in fungal genome and proteome annotations.

Application of Support Vector Machines in Fungal Genome and Proteome Annotation

Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.

/pdf/application-of-support-vector-machines-in-viral-biology-1alx1qsri4.pdf

Application of support vector machines in viral biology

Present research efforts coupled with improved experimental techniques have provided voluminous genomic data. To convert this data into useful knowledge, novel tools for phenomenological and data driven modelling approaches are needed. This need has spurred initiation of a lot of rigorous efforts and has resulted in development of robust artificial intelligence (AI) and machine learning (ML) based models. While these paradigms individually and in synergistic combinations have been employed in various bioinformatics applications, the viral biology discipline has particularly benefitted most. These methodologies can efficiently handle single dimensional sequence to higher dimensional protein structures, microarray data, image and text data, experimental data emanating from spectroscopy, etc. Our analysis deals with ML tools like support vector machines (SVM), neural networks, deep neural networks, random forest, and decision tree. Analysis and interpretations are provided along with ample illustrations of their relevance to real-life applications. AI and evolutionary computing based tools like Genetic Algorithms, Ant Colony optimization, Particle swarm optimization and their applicability to viral biology problems are also discussed. Hybrid combination of these tools with ML techniques have resulted in simultaneous selection of informative attributes and high performance classification. This hybrid methodology has been discussed in detail.

Sonal Modak

Papers

Application of Support Vector Machines in Fungal Genome and Proteome Annotation

Application of support vector machines in viral biology

Applications of artificial intelligence and machine learning in viral biology

Recent Advances in Applications of Support Vector Machines in Fungal Biology