Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of “recent” paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. Contact: rf.irbal@rakras.     

Kernel energy method: application to DNA

The kernel energy method (KEM) has been used in three recent papers (1-3) to calculate the quantum mechanical ab inito molecular energy of peptides and the protein insulin. It was found to have good accuracy. The computational difficulty of representing a molecule increases only modestly with the number of atoms. The calculations are simplified by adopting the approximation that a full biological molecule can be represented by smaller "kernels" of atoms. In this paper, the accuracy of the KEM is tested in the application to DNA, whose basic kernels, chemical bonding, and overall molecular structure are quite different from peptides and proteins. The basic kernel in the case of peptides and proteins is an amino acid. The basic kernel in the case of DNA is a nucleotide consisting of a phosphate-sugar-base. The molecular energy is calculated for all three basic types of DNA, i.e., B, A, and Z configurations of DNA. The results give an accuracy that is comparable to that achieved with peptides and proteins. Thus, the KEM is found to be applicable to major types of biological molecules.     

Metabolic network prediction through pairwise rational kernels

Background

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.

Results

We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.

Conclusions

The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.     

The transducer and encoder of frog muscle spindles are essentially nonlinear. Physiological conclusions from a white-noise analysis

1. Nonlinear second order white-noise analysis has been applied to the isolated frog muscle spindle. Power (²) of the Gaussian white noise (GWN) and the average prestretch level L were varied and the response of both the isolated receptor potential (transducer) and the action potential (encoder) level were analysed. 2. The standard white-noise method is briefly presented. Particular emphasis, however, is put on the limitations in the range of validity of the method and, consequently, on the use and interpretation of the kernels as a Wiener model. Conclusions in the present paper are within this frame and are mainly of qualitative nature. 3. The analysis reveals that the nonlinear contributions of the model are essential for approximating physiological results, thus ruling out purely linear modelling for this receptor organ. 4. The dependence of the transducer kernels on are compatible with the behaviour of a rectifier. Rectification is represented by the lack of hyperpolarization within the isolated receptor potential and is enhanced by the substantial memory in the linear and nonlinear kernels as demonstrated by their extent in time. This is equivalent to low power in high frequencies of the response. Obviously, the hyperpolarizing potentials following each spike counteract the long transducer memory. 5. At the encoder level the memory of the system is strongly reduced. This is achieved by using predominantly high frequency components of the receptor potential for triggering the process of impulse generation, and by the precise coupling and high frequency content of the impulses. This coupling precision is possible because of the sensitivity of the spike-generating mechanism to steep rising transients of the receptor potential and also owing to the reduction in transducer memory by the hyperpolarizing afferpotentials. 6. The preference given to the high frequency components is also read from the structure of the second order transducer kernel and from both the linear and the second order encoder kernels, which allows the most effective input waveform for triggering action potentials to be determined. 6. When the operating point is changed to higher prestretch values, kernel heights increase strongly implying higher response strength of the muscle spindle. The kernel structure is changed as well in the direction of reducing the effective memory already at the level of the receptor potentials, probably a means to prevent too high depolarization values.     

L'amido E L'endosperma Delle Cariossidi Dure E Tenerizzate Di Triticum Durum Desf

Abstract

Starch and endosperm of vitreous and starchy (yellow-berries) kernels of Triticum durum Desf. — Starch and endosperm in kernels of T. durum Desf. have been examined in relation with the problem of the origin and nature of the vitreous or starchy texture which this tissue shows in the ripe fruits. This research has demonstrated that the starch of the vitreous differs from that of the starchy kernels in the shape, respectively irregular and regular; in the distribution of the frequencies y versus the length of the diameter x which is exponential and of the type log y = log a — x log b, in the vitreous kernels the value of log b is generally lower than in the starchy kernels; in the submicroscopical texture, because the irregular grains of the vitreous kernels do not show either birefringence or polarisation cross and are coloured with Congo red: starchy kernels behave quite differently. The starchy kernels are also more swollen and regular, their apparent specific weight and their nitrogen content being less than in the vitreous; the process of starchiness is always in close contact with fibro-vascular bundle of the row; in the starchy cells the cytoplasm shows some interruptions of continuity which leave partly uncovered the starch grains; the endosperm and the starch of the hard kernels wetted with water or placed in moist atmosphere become micro- and macroscopically similar to the starchy ones; in the starch the process of trasformation of the orderly into the amorphous texture is reversible, but for this to happen the only contact with water is insufficient; according to the treatments the vitreous kernels might show a decrease in their nitrogen content. Both starch types are connected with the presence orabsence of slightly bound water; the hypothesis which considers the nitrogen supply of the fruit as immediate cause of the endosperm texture is criticised and it is considered more plausible that such cause might be the different structure and texture of the cytoplasmatic gels during, in function of temperature and moisture.     

Ice nucleation in nature: supercooling point (SCP) measurements and the role of heterogeneous nucleation

In biological systems, nucleation of ice from a supercooled aqueous solution is a stochastic process and always heterogeneous. The average time any solution may remain supercooled is determined only by the degree of supercooling and heterogeneous nucleation sites it encounters. Here we summarize the many and varied definitions of the so-called "supercooling point," also called the "temperature of crystallization" and the "nucleation temperature," and exhibit the natural, inherent width associated with this quantity. We describe a new method for accurate determination of the supercooling point, which takes into account the inherent statistical fluctuations of the value. We show further that many measurements on a single unchanging sample are required to make a statistically valid measure of the supercooling point. This raises an interesting difference in circumstances where such repeat measurements are inconvenient, or impossible, for example for live organism experiments. We also discuss the effect of solutes on this temperature of nucleation. Existing data appear to show that various solute species decrease the nucleation temperature somewhat more than the equivalent melting point depression. For non-ionic solutes the species appears not to be a significant factor whereas for ions the species does affect the level of decrease of the nucleation temperature.     

Disease gene identification by using graph kernels and Markov random fields

Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods.     

Temperature factor and magnetic noise under conditions of stochastic resonance of magnetosomes

The influence of magnetic noise on the dynamics of magnetic nanoparticles under stochastic resonance conditions is considered. The effect of magnetic noise on the nanoparticles at a fixed actual ambient temperature is equivalent to an increase in the effective temperature of the thermostat. This observation may be used to test whether magnetic nanoparticles are involved in the biological effects of weak magnetic fields.     

A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs.

The prediction of translation initiation sites (TISs) in eukaryotic mRNAs has been a challenging problem in computational molecular biology. In this paper, we present a new algorithm to recognize TISs with a very high accuracy. Our algorithm includes two novel ideas. First, we introduce a class of new sequence-similarity kernels based on string editing, called edit kernels, for use with support vector machines (SVMs) in a discriminative approach to predict TISs. The edit kernels are simple and have significant biological and probabilistic interpretations. Although the edit kernels are not positive definite, it is easy to make the kernel matrix positive definite by adjusting the parameters. Second, we convert the region of an input mRNA sequence downstream to a putative TIS into an amino acid sequence before applying SVMs to avoid the high redundancy in the genetic code. The algorithm has been implemented and tested on previously published data. Our experimental results on real mRNA data show that both ideas improve the prediction accuracy greatly and that our method performs significantly better than those based on neural networks and SVMs with polynomial kernels or Salzberg kernels.     

Calculation of the Volterra kernels of non-linear dynamic systems using an artificial neural network

The Volterra series is a well-known method of describing non-linear dynamic systems. A major limitation of this technique is the difficulty involved in the calculation of the kernels. More recently, artificial neural networks have been used to produce black box models of non-linear dynamic systems. In this paper we show how a certain class of artificial neural networks are equivalent to Volterra series and give the equation for the nth order Volterra kernel in terms of the internal parameters of the network. The technique is then illustrated using a specific non-linear system. The kernels obtained by the method described in the paper are compared with those obtained by a Toeplitz matrix inversion technique. Received: 4 June 1993/Accepted in revised form: 2 March 1994     

The Wheat Spikelet--Growth Response to Light and Temperature--Experiment and Hypothesis

Effects of temperature and light treatment, applied to wholewheat plants, on the growth rates of individual kernels of centralspikelets of the intact ear are examined. The treatments appliedare the factorial combinations of 10, 15, 20 and 25 °C,and 100, 50 and 25 per cent light over a 2-week period beginningabout 2 weeks after anthesis. The growth-rate ratios, whichprovide information about possible mechanisms responsible forthe dry matter partitioning between kernels within a spikelet,are examined in conjunction with a model. An anatomically-based model of the spikelet is constructed andanalysed, and the predicted results are compared with the experimentaldata. It is suggested that the more distal kernels are increasinglyhandicapped by pathway resistances, but this effect is partlynullified by the fact that the proximal kernels, which are exposedto higher substrate levels, may be operating on the flatterpart of a Michaelis-Menten-like response. Further, it is foundnecessary to assume that the biochemical rate constants (notthe Michaelis-Menten constants) of the kernels fall off towardsthe more distal kernels. Triticum aestivum L., wheat, spikelet growth     

Protein homology detection using string alignment kernels

MOTIVATION: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences. RESULTS: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection. AVAILABILITY: Software and data available upon request.     

Preparing Serial Sections of Mature Corn and Wheat Kernels

Methods are described for preparing serial sections of paraffin-embedded mature corn and wheat kernels. Prior to embedding corn kernels are killed and fixed in formalin-aceto-alcohol (FAA), then steeped 5 days in 50% glycerol. After embedding by a special procedure, a thin slice is cut from one side of the kernel and the first few cell layers removed. The exposed surface is submerged in 20% glacial acetic acid in 60% ethanol for 2 or 8 days depending on the surface exposed, 2 days in air at 100% relative humidity at room temperature, and 2 days in air at 100% relative humidity at 8°C, successively. Wheat kernels, fixed in formalin-aceto-alcohol and embedded by the regular paraffin procedure, are similarly trimmed to expose a surface which is submerged in 20% glacial acetic acid in 60% ethanol for 2 days, 2 days in air at 100% relative humidity at room temperature and 2 days in air at 100% relative humidity at 8°C, successively. The corn and wheat kernels prepared by these methods give good serial sections when cut as thin as 14μ. The application of these methods to other seeds and caryopses is suggested.     

A method of nonlinear analysis in the frequency domain.

A method is developed for the analysis of nonlinear biological systems based on an input temporal signal that consists of a sum of a large number of sinusoids. Nonlinear properties of the system are manifest by responses at harmonics and intermodulation frequencies of the input frequencies. The frequency kernels derived from these nonlinear responses are similar to the Fourier transforms of the Wiener kernels. Guidelines for the choice of useful input frequency sets, and examples satisfying these guidelines, are given. A practical algorithm for varying the relative phases of the input sinusoids to separate high-order interactions is presented. The utility of this technique is demonstrated with data obtained from a cat retinal ganglion cell of the Y type. For a high spatial frequency grafting, the entire response is contained in the even-order nonlinear components. Even at low contrast, fourth-order components are detectable. This suggests the presence of an essential nonlinearity in the functional pathway of the Y cell, with its singularity at zero contrast.     

Evolutionary optimization of sequence kernels for detection of bacterial gene starts

Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernels for the detection of bacterial gene starts. The resulting kernels lead to higher classification rates, and the adapted parameters reveal the importance of particular triplets for classification, for example of those occurring in the Shine-Dalgarno Sequence.     

Active microwave tomographic imaging of isolated, perfused animal organs

The relative transparency of biological materials to high-frequency electromagnetic waves has encouraged the development of new systems for imaging. This report describes experiments of microwave tomography conducted on a prototype. The object to be analyzed is submerged in water and is illuminated by a plane wave. The total electric field is analyzed by a microwave camera. The recorded data are then processed numerically in order to reconstruct the image that corresponds to the distribution of equivalent currents in a defined plane of a section. Experiments have been conducted on isolated kidneys with and without perfusion. The influence of the perfusing solution temperature has also been studied. These experiments show the potential of this system, especially through the correlation between microwave images and the biological structures. They also confirm previous results concerning spatial resolution and depth of exploration. Finally, the results demonstrate the influence of temperature and support the applicability of this imaging system in non-invasive thermometry, especially for clinical hyperthermia.     

Differential effect of temperature on mitrochondrial activity in shoots from freshly harvested and moderately aged kernels of maize (Zea mays L.)

This paper reports on a study of mitochondrial activity in etiolated shoots of freshly harvested and moderately aged kernels of maize. Activity was investigated after incubation at a favourable temperature (25°C), sub-optimal temperature (13°C) and after a heat shock (46°C for 2h). Although impaired mitochondrial activity in shoots from moderately aged maize kernels was not detected at 25°C, deficiencies became evident under low temperature stress (13°C). State 3 oxygen uptake, cyanide-insensitive oxygen uptake and cytochrome oxidase activity were lower in mitochondria from these shoots at 13°C than in mitochondria from shoots of freshly harvested kernels at this temperature. After a heat shock, cyanide-insensitive oxygen uptake was higher, and cytochrome oxidase activity lower, in shoots of aged kernels than in shoots of fresh kernels. No significant differences in ADP: O ratio or succinate dehydrogenase activity occurred between mitochondria from shoots of the two seed lots in any of the temperature treatments.     

Marginalized kernels for biological sequences

MOTIVATION: Kernel methods such as support vector machines require a kernel function between objects to be defined a priori. Several works have been done to derive kernels from probability distributions, e.g., the Fisher kernel. However, a general methodology to design a kernel is not fully developed. RESULTS: We propose a reasonable way of designing a kernel when objects are generated from latent variable models (e.g., HMM). First of all, a joint kernel is designed for complete data which include both visible and hidden variables. Then a marginalized kernel for visible data is obtained by taking the expectation with respect to hidden variables. We will show that the Fisher kernel is a special case of marginalized kernels, which gives another viewpoint to the Fisher kernel theory. Although our approach can be applied to any object, we particularly derive several marginalized kernels useful for biological sequences (e.g., DNA and proteins). The effectiveness of marginalized kernels is illustrated in the task of classifying bacterial gyrase subunit B (gyrB) amino acid sequences.