Modular composition predicts kinase/substrate interactions

<p>Abstract</p> <p>Background</p> <p>Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database.</p> <p>Results</p> <p>The signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy.</p> <p>Conclusions</p> <p>The method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases.</p

Functional signatures in protein-protein interactions and their impact on signaling pathways

Protein-protein interactions (PPIs) are the most fundamental biological processes at the molecular level. PPIs have been proved to be involved in pathologic mechanisms of many diseases. The experimental methods for testing the binding of PPIs are time-consuming and limited by analogs for many reactions. As a result, a computational model is necessary to predict PPIs and to explore the consequences of signal alterations in biological pathways.A score matrix selection model was built based on overrepresented signature combinations. The case study focused on phosphorylation, which is a well studied post-translational modification category. The signature pairs were extended to signature-string pairs because of the multiple binding sites of kinase/substring interactions. A hypergeometric test was applied to select the significant signals due to the multiple-multiple relationship between the proteins and the domains/motifs. The prediction result shows an extremely high specificity (~100% compared to random combinations in the human protein pool) and an acceptable sensitivity rate (>65%) according to 10-fold evaluations. The score matrix model has then been extended to the user-defined-input software, named ‘YiRen’. A group of PPIs related to transcription factors were evaluated in the test case.Since the signatures embedded in protein sequences effect signal strength and they could be applied as the predictors in PPIs, alterations of these signatures could lead to broken edges in biological networks. An SNP is a kind of sequence variation. It is the major cause of human genetic variations and plays a key role in personalized medicine. In the DA-SNP (Domain-altering SNP) model, the SNPs from a dbSNP database were filtered through the domain regions on human proteomes. The SNPs were selected if they altered the domain signal strength by more than 10%. Then the selected SNPs were checked through an OMIM database for SNP-disease mappings, while the SNP-corresponding proteins were checked through the protein-disease database in Human Protein Reference Database (HPRD). The altered domains then projected into significant signature vectors in PPI prediction and the broken edges in biological pathways. The model linked the phenotypes and the sequence variation together with functional units in order to provide potential explanations for the phenotypes.Ph.D., Bioinformatics -- Drexel University, 201

Speech Recognition via fNIRS Based Brain Signals

In this paper, we present the first evidence that perceived speech can be identified from the listeners' brain signals measured via functional-near infrared spectroscopy (fNIRS)—a non-invasive, portable, and wearable neuroimaging technique suitable for ecologically valid settings. In this study, participants listened audio clips containing English stories while prefrontal and parietal cortices were monitored with fNIRS. Machine learning was applied to train predictive models using fNIRS data from a subject pool to predict which part of a story was listened by a new subject not in the pool based on the brain's hemodynamic response as measured by fNIRS. fNIRS signals can vary considerably from subject to subject due to the different head size, head shape, and spatial locations of brain functional regions. To overcome this difficulty, a generalized canonical correlation analysis (GCCA) was adopted to extract latent variables that are shared among the listeners before applying principal component analysis (PCA) for dimension reduction and applying logistic regression for classification. A 74.7% average accuracy has been achieved for differentiating between two 50 s. long story segments and a 43.6% average accuracy has been achieved for differentiating four 25 s. long story segments. These results suggest the potential of an fNIRS based-approach for building a speech decoding brain-computer-interface for developing a new type of neural prosthetic system

Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark

AHP Aided Decision-Making in Virtual Machine Migration for Green Cloud

In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment

Identifying Potential Cropland Losses When Conserving 30% and 50% Earth with Different Approaches and Spatial Scales

Biodiversity conservation is the cornerstone for sustainable development. Bold conservation targets provide the last opportunities to halt the human-driven mass extinction. Recently, bold conservation targets have been proposed to protect 30% or 50% of Earth. However, little is known about its potential impacts on cropland. We identify potential cropland losses when 30% and 50% of global terrestrial area is given back to nature by 2030/2050, at three spatial scales (global, biome and country) and using two approaches (“nature-only landscapes” and “shared landscapes”). We find that different targets, applied scales and approaches will lead to different cropland losses: (1) At the global scale, it is possible to protect 50% of the Earth while having minimum cropland losses. (2) At biome scale, 0.64% and 8.54% cropland will be lost globally in 2030 and 2050 under the nature-only approach while by contrast, the shared approach substantially reduces the number of countries confronted by cropland losses, demanding only 0% and 2.59% of global cropland losses in 2030 and 2050. (3) At the national scale, the nature-only approach causes losses of 3.58% and 10.73% of global cropland in 2030 and 2050, while the shared approach requires 0.77% and 7.55% cropland in 2030 and 2050. Our results indicate that bold conservation targets could be considered, especially when adopting the shared approach, and we suggest adopting ambitious targets (protecting at least 30% by 2030) at the UN Biodiversity Conference (COP 15) to ensure a sustainable future for Earth