A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis.

Background: DNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.

Methods: In the present study, we proposed an accurate predictive model with the help of data fusion at the decision level.

Exploring new subgroups for irritable bowel syndrome using a machine learning algorithm.

Irritable bowel syndrome (IBS) is a complicated gut-brain axis disorder that has typically been classified into subgroups based on the major abnormal stool consistency and frequency. The presence of components other than lower gastrointestinal (GI) symptoms, such as psychological burden, has also been observed in IBS manifestations. The purpose of this research is to redefine IBS subgroups based on upper GI symptoms and psychological factors in addition to lower GI symptoms using an unsupervised machine learning algorithm.

Hypoxic burden to guide CPAP treatment allocation in patients with obstructive sleep apnoea: a study of the ISAACC trial.

Background: Hypoxic burden (HB) has emerged as a strong predictor of cardiovascular risk in obstructive sleep apnoea (OSA). We aimed to assess the potential of HB to predict the cardiovascular benefit of treating OSA with continuous positive airway pressure (CPAP).

Methods: This was a analysis of the ISAACC trial (ClinicalTrials.

Re-investigation of functional gastrointestinal disorders utilizing a machine learning approach.

Background: Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high overlap among FGIDs in patients makes treatment and identifying underlying mechanisms challenging. Furthermore, disregarding psychological factors in the current classification, despite their approved relationship with GI symptoms, underlines the necessity of more investigation into grouping FGID patients.

Hypoxic Burden Based on Automatically Identified Desaturations Is Associated with Adverse Health Outcomes.

Recent studies have shown that sleep apnea-specific intermittent hypoxemia quantified by the hypoxic burden (HB) predicted cardiovascular disease (CVD)-related mortality in community-based and clinical cohorts. Calculation of HB is based on manual scoring of hypopneas and apneas, which is time-consuming and prone to interscorer variability. To validate a novel method to quantify the HB that is based on automatically scored desaturations.

Whole exome sequencing identified a novel LAMA2 frameshift variant causing merosin-deficient congenital muscular dystrophy in a patient with cardiomyopathy, and autism-like behavior.

Muscular dystrophy (MD) is a group of multiple muscle diseases, which causes severely impaired motor ability, degeneration and dysfunctions in the musculoskeletal system, respiratory failure and feeding difficulties. LAMA2-related MD is caused by pathogenic variants in the LAMA2 gene, encoding laminin a2 chain, a component of the skeletal muscle extracellular matrix protein laminin-α2β1γ1. We performed clinical examination and molecular genetic analysis in a patient with congenital MD (CMD), and autism-like phenotype.

Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization.

Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleterious and benign missense variants by means of a large number of features, including structure, sequence, interaction networks, gene disease associations as well as phenotypes. However, development of a reliable and accurate algorithm for merging heterogeneous information is highly needed as it could be captured all information of complex interactions on network that genes participate in.

Genetic variant effect prediction by supervised nonnegative matrix tri-factorization.

Discriminating between deleterious and neutral mutations among numerous non-synonymous single nucleotide variants (nsSNVs) that may be observed through whole exome sequencing (WES) is considered a great challenge. In this regard, many machine learning methods have been developed for the prediction of variant consequences based on the analysis of either protein amino acid sequences or protein structures or their integration with features extracted from various gene level data and phenotype information. Due to the availability of a high number of features and heterogeneity of sources, implementing a suitable integration method plays an important role in predictive models.

Analysis of sequential ultrasound frames for the measurement of hemodynamic stresses, critical bent buckling pressure, and critical buckling torque of human common carotid atherosclerosis.

Background: Structural properties of the arterial wall are important diagnostic parameters. The current study aimed at investigating the hemodynamic properties and intima-media thickness changes of the common carotid artery in human subjects with atherosclerosis in order to determine the relationships between these indices.

Methods: This study presented methods to detect instantaneous changes in the lumen diameter, intima media thickness, longitudinal movement and acceleration, and velocity of the left side of common carotid artery.

Selection of Optimal Bioinformatic Tools and Proper Reference for Reducing the Alignment Error in Targeted Sequencing Data.

Background: Careful design in the primary steps of a next-generation sequencing study is critical for obtaining successful results in downstream analysis.

Methods: In this study, a framework is proposed to evaluate and improve the sequence mapping in targeted regions of the reference genome. In this regard, simulated short reads were produced from the coding regions of the human genome and mapped to a Customized Target-Based Reference (CTBR) by the alignment tools that have been introduced recently.

Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profiles.

A new approach is presented to predict breast cancer recurrence through gene expression profiles using hidden Markov models (HMM). In this regard, 322 genes were selected from 44 published gene lists related to breast cancer prognosis. Afterwards, using gene set enrichment analysis, 922 gene sets were found from subsets of genes with the same biological meaning.

Vascular deformation in human atherosclerotic carotid artery evaluated by 2D analysis of ultrasonography.

Objectives: Common carotid artery (CCA) remodelling in the atherosclerosis process is an inherent necessary element that decreases the progress of significant lumen compromise. The present study used a semi-automated method to assess relationships of intima-media thickness (IMT), lumen diameter (LD) and inter-adventitial diameter (IAD) using ultrasound B-mode images of atherosclerotic carotid artery.

Methods: In the cross-sectional study, 120 male subjects (age range: 40-60 years) were classified into four research groups namely control, mild, moderate, and severe stenosis.

Design and Implementation of a Customable Automatic Vehicle Location System in Ambulances and Emergency Vehicle Systems.

Background: Automatic vehicle location (AVL) refers to a system that calculates the geographical location of any vehicle, i.e., latitude and longitude.

A novel feature selection method for microarray data classification based on hidden Markov model.

In this paper, a novel approach is introduced for integrating multiple feature selection criteria by using hidden Markov model (HMM). For this purpose, five feature selection ranking methods including Bhattacharyya distance, entropy, receiver operating characteristic curve, t-test, and Wilcoxon are used in the proposed topology of HMM. Here, we presented a strategy for constructing, learning and inferring the HMM for gene selection, which led to higher performance in cancer classification.

Physicochemical Position-Dependent Properties in the Protein Secondary Structures.

Background: Establishing theories for designing arbitrary protein structures is complicated and depends on understanding the principles for protein folding, which is affected by applied features. Computer algorithms can reach high precision and stability in computationally designed enzymes and binders by applying informative features obtained from natural structures.

Methods: In this study, a position-specific analysis of secondary structures (α-helix, β-strand, and tight turn) was performed to reveal novel features for protein structure prediction and protein design.

Applying Two Different Bioinformatic Approaches to Discover Novel Genes Associated with Hereditary Hearing Loss via Whole-Exome Sequencing: ENDEAVOUR and HomozygosityMapper.

Background: Hearing loss (HL) is a highly prevalent heterogeneous deficiency of sensory-neural system with involvement of several dozen genes. Whole-exome sequencing (WES) is capable of discovering known and novel genes involved with HL.

Materials And Methods: Two pedigrees with HL background from Khuzestan province of Iran were selected.

An Optimized Framework for Cancer Prediction Using Immunosignature.

Background: Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited.

Dual Function of Gold Nanoparticles in Synergism with Mitoxantrone and Microwave Hyperthermia Against Melanoma Cells.

Background: This study was performed to evaluate any synergetic effects of mitoxantrone (MX) and gold nanoparticles (GNPs) as dual therapeutic approach, along with microwave (MW) hyperthermia for melanoma cancer. Methods: Various tests were performed on the DFW melanoma cell line in the presence of MX and different concentrations of GNPs, with and without MW irradiation. MTT [3-(4,5-dimethylthiazol–2-yl)-2,5-iphenyltetrazolium bromide] assays were conducted to evaluate the effectiveness of the used therapeutic methods in terms of cell survival.

A novel feature ranking method for prediction of cancer stages using proteomics data.

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging.

Appraisal of different ultrasonography indices in patients with carotid artery atherosclerosis.

In this study a semi-automated image-processing based method was designed in which the parameters such as intima-media thickness (IMT), resistive index (RI), pulsatility index (PI), dicrotic notch index (DNI), and mean wavelet entropy (MWE) were evaluated in B-mode and Doppler ultrasound in patients presenting with carotid artery atherosclerosis. In a cross-sectional design, 144 men were divided into four groups of control, mild, moderate and severe stenosis subjects. In all individuals, far wall IMT, RI, PI, DNI, and MWE of the left common carotid artery (CCA) were extracted using the proposed method.

Prediction of metastasis in advanced colorectal carcinomas using CGH data.

Logistic Regression Model (LRM) and artificial neural networks (ANNs) as two nonlinear models have been used to establish a novel two-stage hybrid modeling procedure for prediction of metastasis in advanced colorectal carcinomas. Two different datasets were used in training and testing procedures. For the first stage of hybrid modeling procedure, LRM was used to evaluate the contribution of DNA sequence copy number aberrations detected by Comparative Genomic Hybridization in advanced colorectal carcinoma and its metastasis.

Stable Gene Signature Selection for Prediction of Breast Cancer Recurrence Using Joint Mutual Information.

In this experiment, a gene selection technique was proposed to select a robust gene signature from microarray data for prediction of breast cancer recurrence. In this regard, a hybrid scoring criterion was designed as linear combinations of the scores that were determined in the mutual information (MI) domain and protein-protein interactions network. Whereas, the MI-based score represents the complementary information between the selected genes for outcome prediction; and the number of connections in the PPI network between the selected genes builds the PPI-based score.