Features-based deisotoping method for tandem mass spectra.

For high-resolution tandem mass spectra, the determination of monoisotopic masses of fragment ions plays a key role in the subsequent peptide and protein identification. In this paper, we present a new algorithm for deisotoping the bottom-up spectra. Isotopic-cluster graphs are constructed to describe the relationship between all possible isotopic clusters.

Estimating parameters in genetic regulatory networks with SUM logic.

Many methods for inferring genetic regulatory networks have been proposed. However inferred networks can hardly be used to analyze the dynamics of genetic regulatory networks. Recently nonlinear differential equations are proposed to model genetic regulatory networks.

Peptide charge state determination of tandem mass spectra from low-resolution collision induced dissociation.

Background: Charge states of tandem mass spectra from low-resolution collision induced dissociation can not be determined by mass spectrometry. As a result, such spectra with multiple charges are usually searched multiple times by assuming each possible charge state. Not only does this strategy increase the overall database search time, but also yields more false positives.

Applications of graph theory in protein structure identification.

There is a growing interest in the identification of proteins on the proteome wide scale. Among different kinds of protein structure identification methods, graph-theoretic methods are very sharp ones. Due to their lower costs, higher effectiveness and many other advantages, they have drawn more and more researchers' attention nowadays.

Stability and bifurcation analysis of models for zebrafish somitogenesis.

Notch-Delta signaling is indispensable for somitogenesis, which controls the vertebrate segmentation during embryonic development. Several theoretical models have been proposed to explain this interesting process. In zebrafish somitogenesis, genes her1, her7, delta, and their proteins plays the important roles.

Nonlinear model-based method for clustering periodically expressed genes.

Clustering periodically expressed genes from their time-course expression data could help understand the molecular mechanism of those biological processes. In this paper, we propose a nonlinear model-based clustering method for periodically expressed gene profiles. As periodically expressed genes are associated with periodic biological processes, the proposed method naturally assumes that a periodically expressed gene dataset is generated by a number of periodical processes.

Inference of biological S-system using the separable estimation method and the genetic algorithm.

Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity.

Delay-independent stability of genetic regulatory networks.

Genetic regulatory networks can be described by nonlinear differential equations with time delays. In this paper, we study both locally and globally delay-independent stability of genetic regulatory networks, taking messenger ribonucleic acid alternative splicing into consideration. Based on nonnegative matrix theory, we first develop necessary and sufficient conditions for locally delay-independent stability of genetic regulatory networks with multiple time delays.

Speed improvements of peptide-spectrum matching using single-instruction multiple-data instructions.

Peptide-spectrum matching is one of the most time-consuming portion of the database search method for assignment of tandem mass spectra to peptides. In this study, we develop a parallel algorithm for peptide-spectrum matching using Single-Instruction Multiple Data (SIMD) instructions. Unlike other parallel algorithms in peptide-spectrum matching, our algorithm parallelizes the computation of matches between a single spectrum and a given peptide sequence from the database.

An adaptive approach to denoising tandem mass spectra.

In our recent work on denoising, a linear combination of five features was used to adjust the peak intensities in tandem mass spectra. Although the method showed a promise, the coefficients (weights) of the linear combination were fixed and determined empirically. In this paper, we proposed an adaptive approach for estimating these weights.

Global and robust stability analysis of genetic regulatory networks with time-varying delays and parameter uncertainties.

The study of stability is essential for designing or controlling genetic regulatory networks. This paper addresses global and robust stability of genetic regulatory networks with time delays and parameter uncertainties. Most existing results on this issue are based on the linear matrix inequalities (LMIs) approach, which results in checking the existence of a feasible solution to high dimensional LMIs.

SVM-RFE based feature selection for tandem mass spectrum quality assessment.

In literature, hundreds of features have been proposed to assess the quality of tandem mass spectra. However, many of these features are irrelevant in describing the spectrum quality and they can degenerate the spectrum quality assessment performance. We propose a two-stage Recursive Feature Elimination based on Support Vector Machine (SVM-RFE) method to select the highly relevant features from those collected in literature.

Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem.

Parameter estimation method for improper fractional models and its application to molecular biological systems.

Derived from biochemical principles, molecular biological systems can be described by a group of differential equations. Generally these differential equations contain fractional functions plus polynomials (which we call improper fractional model) as reaction rates. As a result, molecular biological systems are nonlinear in both parameters and states.

Identification of gene regulatory networks from time course gene expression data.

Several methods have been proposed to infer gene regulatory networks from time course gene expression data. As the number of genes is much larger than the number of time points at which gene expression (mRNA concentration) is measured, most existing methods need some ad hoc assumptions to infer a unique gene regulatory network from time course gene expression data. It is well known that gene regulatory networks are sparse and stable.

Assigning probabilities to Mascot peptide identification using logistic regression.

We propose a method to assign probabilities to Mascot peptide identification by using logistic regression. Three key scores, Mascot ions score (MIS), identity threshold, and homology threshold, are integrated into the logistic regression model. Two features in the model are constructed as the differences between MIS and the two thresholds, respectively.

Charge state determination of peptide tandem mass spectra using support vector machine (SVM).

A single mass spectrometry experiment could produce hundreds of thousands of tandem mass spectra. Several search engines have been developed to interpret tandem mass spectra. All search engines need to determine the masses of peptide ions from their mass/charge ratios.

Parameter estimation in rational models of molecular biological systems.

Based on statistical thermodynamics or Michaelis -Menten kinetics, molecular biological systems can be modeled by a system of nonlinear differential equations. The nonlinearity in the model stems from rational reaction rates whose numerator and denominator are linear in parameters. It is a nonlinear problem to estimate the parameters in such rational models of molecular biological systems.

Model based clustering for tandem mass spectrum quality assessment.

Several computational methods have been proposed to assess the quality of tandem mass spectra. These methods range from supervised to unsupervised algorithms, discriminative to generative models. Unsupervised learning algorithms for tandem mass spectra are not probabilistic model based and they don't provide probabilities for spectra quality assessment.

Using a state-space model and location analysis to infer time-delayed regulatory networks.

Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model.

Dynamic-model-based method for selecting significantly expressed genes from time-course expression profiles.

This paper proposes a dynamic-model-based method for selecting significantly expressed (SE) genes from their time-course expression profiles. A gene is considered to be SE if its time-course expression profile is more likely time-dependent than random. The proposed method describes a time-dependent gene expression profile by a nonzero-order autoregressive (AR) model, and a time-independent gene expression profile by a zero-order AR model.

Sparse representation for classification of tumors using gene expression data.

Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level.

A novel approach to denoising ion trap tandem mass spectra.

Background: Mass spectrometers can produce a large number of tandem mass spectra. They are unfortunately noise-contaminated. Noises can affect the quality of tandem mass spectra and thus increase the false positives and false negatives in the peptide identification.