Scatter-search with support vector machine for prediction of relative solvent accessibility

Proteins have vital roles in the living cells. The protein function is almost completely dependent on protein structure. The prediction of relative solvent accessibility gives helpful information for the prediction of tertiary structure of a protein. In recent years several relative solvent accessibility (RSA) prediction methods including those that generate real values and those that predict discrete states have been developed. The proposed method consists of two main steps: the first one, provided subset selection of quantitative features based on selected qualitative features and the second, dedicated to train a model with selected quantitative features for RSA prediction. The results show that the proposed method has an improvement in average prediction accuracy and training time. The proposed method can dig out all the valuable knowledge about which physicochemical features of amino acids are deemed more important in prediction of RSA without human supervision, which is of great importance for biologists and their future researches

Reconstructing the cell-cell interaction network among mouse immune cells.

Intercellular interactions and cell-cell communication are critical to regulating cell functions, especially in normal immune cells and immunotherapies. Ligand-receptor pairs mediating these cell-cell interactions can be identified using diverse experimental and computational approaches. Here, we reconstructed the intercellular interaction network between Mus musculus immune cells using publicly available receptor-ligand interaction databases and gene expression data from the immunological genome project. This reconstructed network accounts for 50,317 unique interactions between 16 cell types between 731 receptor-ligand pairs. Analysis of this network shows that cells of hematopoietic lineages use fewer communication pathways for interacting with each other, while nonhematopoietic stromal cells use the most network communications. We further observe that the WNT, BMP, and LAMININ pathways are the most significant contributors to the overall number of cell-cell interactions among the various pathways in the reconstructed communication network. This resource will enable the systematic analysis of normal and pathologic immune cell interactions, along with the study of emerging immunotherapies

Unraveling the transcriptional complexity of compactness in sistan grape cluster

© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license:http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (Feb 2018) in accordance with the publisher’s archiving policyYaghooti grape of Sistan is the earliest ripening grape in Iran, harvested every May annually. It is adapted to dry conditions in Sistan region and its water requirement is less than the other grape cultivars. The transcriptional complexity of this grape was studied in three stages of cluster development. Totally, 24121 genes were expressed in different cluster development steps (step 1: cluster formation, step 2: berry formation, step 3: final size of cluster) of which 3040 genes in the first stage, 2381 genes in the second stage and 2400 genes in the third stage showed a significant increase in expression. GO analysis showed that when the clusters are ripening, the activity of the nucleus, cytoplasmic, cytosol, membrane and chloroplast genes in the cluster architecture cells decreases. In contrast, the activity of the endoplasmic reticulum, vacuole and extracellular region genes enhances. When Yaghooti grape is growing and developing, some of metabolic pathways were activated in the response to biotic and abiotic stresses. Gene co-expression network reconstruction showed that AGAMOUS is a key gene in compactness of Sistan grape cluster, because it influences on expression of GA gene which leads to increase cluster length and berries size

AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches

Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of commonly used metrics. Further, this paper explores the latest developments in ensemble methods and explainable AI, offering insights into future directions and potential breakthroughs. This review synthesizes current insights, offering a valuable guide for researchers, clinicians, and medical imaging experts. It highlights AI\u27s crucial role in improving the precision and speed of detecting key structural and functional irregularities in MRI. Our exploration of innovative techniques and trends furthers MRI technology development, aiming to refine diagnostics, tailor treatments, and elevate patient care outcomes

AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches

Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of commonly used metrics. Further, this paper explores the latest developments in ensemble methods and explainable AI, offering insights into future directions and potential breakthroughs. This review synthesizes current insights, offering a valuable guide for researchers, clinicians, and medical imaging experts. It highlights AI\u27s crucial role in improving the precision and speed of detecting key structural and functional irregularities in MRI. Our exploration of innovative techniques and trends furthers MRI technology development, aiming to refine diagnostics, tailor treatments, and elevate patient care outcomes

Use of Stem Cells in the Treatment of Myocardial Infarction

Considerable research has been done in the past few decades to treat ischemic heart disease (stroke). Although drug therapies can improve heart disease and reduce mortality in heart failure, none is able to regenerate damaged heart tissue. Therefore, stem cell-based therapies are considered as new approaches to correcting heart tissue remodeling. Since the depletion of cardiac muscle cells at the beginning of the myocardial infarction act as a stimulus for myocardial remodeling, the ability to replace these cells with their healthy counterparts is an effective treatment for many types of cardiovascular diseases. In this study, we reviewed the advances made in the treatment of myocardial infarction through cell therapy

Performance evaluation measures for protein complex prediction

Protein complexes play a dominant role in cellular organization and function. Prediction of protein complexes from the network of physical interactions between proteins (PPI networks) has thus become one of the important research areas. Recently, many computational approaches have been developed to identify these complexes. Various performance assessment measures have been proposed for evaluating the efficiency of these methods. However, there are many inconsistencies in the definitions and usage of the measures across the literature. To address this issue, we have gathered and presented the most important performance evaluation measures and developed a tool, named CompEvaluator, to critically assess the protein complex prediction methods. The tool and documentation are publicly available at https://sourceforge.net/projects/compevaluator/files/

Antifreeze Protein DetectionUsing Sequential Minimal Optimization Classifier

Various cold-adaptedorganisms produce antifreeze proteins (AFPs), which prevent the cell fluids from freezing.AFPs haveseveral important applications in increasing freeze tolerance of crop plants,maintain the tissue in frozen condition and producing cold-hardy plants using transgenictechnology. In this paper, we proposed a novel methodfor predicting AFPs usingSequential Minimal Optimization(SMO)classifier incorporation 4 types of features:hydropathy,physicochemical properties,amino acid composition and evolutionary profile. Testedby10-fold cross validation, our proposed method gains91.8accuracy. In addition, results reveal the better performance of our method in AFPs detection in comparison to the current state-of-the-art method

Discovering Common Pathogenic Mechanisms of COVID-19 and Parkinson Disease: An Integrated Bioinformatics Analysis.

Coronavirus disease 2019 (COVID-19) has emerged since December 2019 and was later characterized as a pandemic by WHO, imposing a major public health threat globally. Our study aimed to identify common signatures from different biological levels to enlighten the current unclear association between COVID-19 and Parkinsons disease (PD) as a number of possible links, and hypotheses were reported in the literature. We have analyzed transcriptome data from peripheral blood mononuclear cells (PBMCs) of both COVID-19 and PD patients, resulting in a total of 81 common differentially expressed genes (DEGs). The functional enrichment analysis of common DEGs are mostly involved in the complement system, type II interferon gamma (IFNG) signaling pathway, oxidative damage, microglia pathogen phagocytosis pathway, and GABAergic synapse. The protein-protein interaction network (PPIN) construction was carried out followed by hub detection, revealing 10 hub genes (MX1, IFI27, C1QC, C1QA, IFI6, NFIX, C1S, XAF1, IFI35, and ELANE). Some of the hub genes were associated with molecular mechanisms such as Lewy bodies-induced inflammation, microglia activation, and cytokine storm. We investigated regulatory elements of hub genes at transcription factor and miRNA levels. The major transcription factors regulating hub genes are SOX2, XAF1, RUNX1, MITF, and SPI1. We propose that these events may have important roles in the onset or progression of PD. To sum up, our analysis describes possible mechanisms linking COVID-19 and PD, elucidating some unknown clues in between

RNA-Protein Interaction Prediction sing euence Information

Many of RNA functions depend on interactions between RNA and proteins. So, understanding the molecular mechanism of RNA-protein interactions (RPIs) is a maor challenge in structural bioinformatics. In this paper, we proposed a novel method for predicting RNA-protein interactions based on sequence information. e used motif information and repetitive site in RNA and protein sequences as features to build a model to RPI prediction using a random forest classifier. Results of 0-fold cross-validation experiments on two non-redundant benchmark datasets show the good performance of proposed method in RPI detection. Our method achieved an accuracy of and Matthews correlation coefficient (MCC) of 76