Biology subjects

Hashemi, A.

Publications and source records attributed to Hashemi, A..

11 recordsLinked to original sources

bioRxiv2025

Gene co-expression network reveals key hub genes associated with endometriosis using bulk RNA-seq

Endometriosis (EMs) is a complex and prevalent gynecological disorder with a significant genetic component, posing a major clinical challenge in reproductive medicine due to multifactorial inheritance patterns and the involvement of gene-environment interactions in pathophysiology. However, despite extensive research, reliable diagnostic biomarkers for EMs have yet to be identified. We utilized bulk transcriptome sequencing data obtained from the Gene Expression Omnibus to identify hub genes involved in EMs. This study was conducted using a system biology analysis, incorporating differential gene expression, meta-analysis of transcriptomic data, functional enrichment analysis, construction of gene co-expression networks, and comprehensive topological analysis to identify key regulatory genes. Bulk RNA-seq analysis revealed significant differential gene expression between healthy and EMs groups. Overall, 603 and 443 meta-genes were discovered using the Fisher and Invorm P-value combination methods, respectively. A total of 427 meta-genes were subjected to functional enrichment analysis, which revealed significant enrichment in several KEGG pathways related to EMs including "Adherens junction," "p53 signaling pathway," and "AMPK signaling pathway." Additionally, Gene Ontology analysis revealed key processes including "Regulation of Anatomical Structure Morphogenesis," Acetylglucosaminyltransferase Activity" and "Positive Regulation of Intracellular Signal". Co-expression network analysis identified the turquoise module as a critical functional module, within this significant module, the genes IGFBP7, IGFBP3, and NKAP were identified as EMs hub genes based on high connectivity and central roles in the network. The constructed protein-protein interaction network further highlighted STAR, PLCD3, RPAP2, MSI2, MAS1, TBX1, LIPT1, and SVIL, as key genes. These genes represented high centrality within the network, suggesting potential regulatory and functional significance in the molecular mechanisms underlying EMs. Notably, miR-143-3p, miR-340-5p, miR-410-3p, and miR-302b-5p were implicated in EMs-associated regulatory networks. This integrative approach significantly enhances our understanding of the molecular mechanisms underlying EMs and provides a robust foundation for the development of diagnostic biomarkers.

bioinformatics↗

bioRxiv2025

How forgiving are M/EEG inverse solutions to noise level misspecification? An excursion into the BSI-Zoo.

AO_SCPLOWBSTRACTC_SCPLOWBrain source imaging (BSI), also known as source localization, from magneto- and electroencephalographic (M/EEG) data, is a challenging ill-posed inverse problem. Accurate source estimation is sensitive to modeling parameters, such as regularization strength and noise level, where misconfigurations can lead to under- or overfitting. Different BSI methods, however, may vary in their robustness to suboptimal parameter choices. Here we conducted extensive simulations of brain sources superimposed by varying degrees of sensor noise to study the ranges of noise misspecification within which different BSI approaches can still localize well. Using the Earth Movers Distance (EMD) and other metrics, we compare the performance of smooth linear inverse solutions with that of sparse non-linear Bayesian learning solutions. Additionally, we assess the effectiveness of various noise estimation and cross-validation techniques to select hyperparameters close to those achieving optimal localization. Methods and experiments are made available within the BSI-Zoo Python package.

neuroscience↗

bioRxiv2023

Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise

The inverse problem in brain source imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG). One key challenge is the efficient recovery of sparse brain activity when the data is corrupted by structured noise that is low-rank noise. This is often the case when there are a few active sources of environmental noise and the MEG/EEG sensor noise is highly correlated. In this paper, we propose a novel robust empirical Bayesian framework which provides us a tractable algorithm for jointly estimating a low-rank noise covariance and brain source activity. Specifically, we use a factor analysis model for the structured noise, and infer a sparse set of variance parameters for source activity, while performing Variational Bayesian inference for the noise. One key aspect of this algorithm is that it does not require any additional baseline measurements to estimate the noise covariance from the sensor data. We perform exhaustive experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.

bioengineering↗

bioRxiv2023

Prediction Analysis of Preterm Neonates Mortality using Machine Learning Algorithms via Python Programming

Prediction analysis of preterm neonate mortality is necessary and significant for benchmarking and evaluating healthcare services in Hospitals and other medical centers. Application of artificial intelligence and machine learning models, which is a hot topic in medicine/healthcare and engineering, may improve physicians skill to predict the preterm neonatal deaths. The main purpose of this research article is to introduce a preterm neonatal mortality risk prediction by means of machine learning/ML predictive models to survive infants using supervised ML models if possible. Moreover, this paper presents some effective parameters and features which affect to survive the infants directly. It means, the obtained model has an accuracy of about 91.5% to predict the status of infant after delivery. After recognizing the critical status for an infant, physicians and other healthcare personnel can help to infant for possible surviving using special medical NICU cares. It has been tried to get some suitable models with high accuracy and comparing the results. In a word, a survival prediction analysis of preterm neonate mortality has been carried out using machine learning methods via Python programming (possible surviving infants after delivery in the hospital).

bioinformatics↗

bioRxiv2023

The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain

Across the cortical hierarchy, single neurons are characterized by differences in the extent to which they can sustain their firing rate over time (i.e., their "intrinsic timescale"). Previous studies have demonstrated that neurons in a given brain region mostly exhibit either short or long intrinsic timescales. In this study, we sought to identify populations of neurons that accumulate information over different timescales in the mouse brain and to characterize their functions in the context of a visual discrimination task. Thus, we separately examined the neural population dynamics of neurons with long or short intrinsic timescales across different brain regions. More specifically, we looked at the decoding performance of these neural populations aligned to different task variables (stimulus onset, movement). Taken together, our population-level findings support the hypothesis that long intrinsic timescale neurons encode abstract variables related to decision formation. Furthermore, we investigated whether there was a relationship between how well a single neuron represents the animals choice or stimuli and their intrinsic timescale. We did not observe any significant relationship between the decoding of these task variables and a single neurons intrinsic timescale. In summary, our findings support the idea that the long intrinsic timescale population of neurons, which appear at different levels of the cortical hierarchy, are primarily more involved in representing the decision variable.

neuroscience↗

bioRxiv2022

Distributed coding of evidence accumulation across the mouse brain using microcircuits with a diversity of timescales

The gradual accumulation of noisy evidence for or against options is the main step in the perceptual decision-making process. Using brain-wide electrophysiological recording in mice (Steinmetz et al., 2019), we examined neural correlates of evidence accumulation across brain areas. We demonstrated that the neurons with Drift-Diffusion-Model-like firing rate activity (i.e., evidence-sensitive ramping firing rate) were distributed across the brain. Exploring the underlying neural mechanism of evidence accumulation for the DDM-like neurons revealed different accumulation mechanisms (i.e. single and race) both within and across the brain areas. Our findings support the hypothesis that evidence accumulation is happening through multiple integration mechanisms in the brain. We further explored the timescale of the integration process in the single and race accumulator models. The results demonstrated that the accumulator microcircuits within each brain area had distinct properties in terms of their integration timescale, which were organized hierarchically across the brain. These findings support the existence of evidence accumulation over multiple timescales. Besides the variability of integration timescale across the brain, a heterogeneity of timescales was observed within each brain area as well. We demonstrated that this variability reflected the diversity of microcircuit parameters, such that accumulators with longer integration timescales had higher recurrent excitation strength.

neuroscience↗

bioRxiv2021

Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models

We consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. Crucial factors influencing the accuracy of the source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed the estimation of noise covariance matrices with full structure. To address this shortcoming, we develop hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, which are estimated jointly from data. As an extension to classical sparse Bayesian learning (SBL), where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using the majorization-maximization framework and Riemannian geometry, we derive an efficient algorithm for updating the noise covariance along the manifold of positive definite matrices. We demonstrate that our algorithm has guaranteed and fast convergence and validate it in simulations and with real MEG data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and full-structured. Our method has applications in many domains beyond biomagnetic inverse problems.

neuroscience↗

bioRxiv2021

Online spike sorting via deep contractive autoencoder

Spike sorting - the process of separating spikes from different neurons - is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neurons activity from background electrical noise based on the shapes of the waveforms (WFs) obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (DCAE) architecture. DCAEs are deep neural networks that can learn a latent state representation of their inputs. The main advantage of DCAE approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our DCAE-based online spike sorting algorithm achieves over 90% accuracy at sorting previously-unseen spike waveforms. Moreover, our approach produces superior results compared to several state-of-the-art offline spike-sorting procedures.

paleontology↗

bioRxiv2020

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms under the majorization-minimization (MM) framework. This unification perspective not only provides a useful theoretical framework for comparing different algorithms in terms of their convergence behavior, but also provides a principled recipe for constructing novel algorithms with specific properties by designing appropriate bounds of the Bayesian marginal likelihood function. Second, building on the MM principle, we propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio (SNR) settings. Third, precise knowledge of the noise level is a crucial requirement for accurate source reconstruction. Here we present a novel principled technique to accurately learn the noise variance from the data either jointly within the source reconstruction procedure or using one of two proposed cross-validation strategies. Empirically, we could show that the monotonous convergence behavior predicted from MM theory is confirmed in numerical experiments. Using simulations, we further demonstrate the advantage of LowSNR-BSI over conventional SBL in low-SNR regimes, and the advantage of learned noise levels over estimates derived from baseline data. To demonstrate the usefulness of our novel approach, we show neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.

neuroscience↗

bioRxiv2020

Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging.

bioengineering↗

bioRxiv2017

Sparse Tensor Decomposition For Haplotype Assembly Of Diploids And Polyploids

A framework that formulates haplotype assembly as sparse tensor decomposition is proposed. The problem is cast as that of decomposing a tensor having special structural constraints and missing a large fraction of its entries into a product of two factors, U and [Formula]; tensor [Formula] reveals haplotype information while U is a sparse matrix encoding the origin of erroneous sequencing reads. An algorithm, AltHap, which reconstructs haplotypes of either diploid or poly-ploid organisms by solving this decomposition problem is proposed. Starting from a judiciously selected initial point, AltHap alternates between two optimization tasks to recover U and [Formula] by relying on a modified gradient descent search that exploits salient structural properties of U and [Formula]. The performance and convergence properties of AltHap are theoretically analyzed and, in doing so, guarantees on the achievable minimum error correction scores and correct phasing rate are established. AltHap was tested in a number of different scenarios and was shown to compare favorably to state-of-the-art methods in applications to haplotype assembly of diploids, and significantly outperform existing techniques when applied to haplotype assembly of polyploids.

bioinformatics↗