Sirvan Khalighi

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Verified

Sirvan verified their affiliation via an institutional email.

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• Ph.D.
• Instructor at Emory University

6061 Background: In several cancers, including head and neck squamous cell carcinoma (HNSCC), the immunosuppressive components of the tumor microenvironment (TME) can impact the effectiveness of immune checkpoint inhibitors (ICI). One significant component of the TME is the extracellular matrix, which is rich in collagen fibers. In this work we use...

This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomi...

Background The role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on coll...

Endometrial cancer (EC) disproportionately affects African American (AA) women in terms of progression and death. In our study, we sought to employ computerized image and bioinformatic analysis to tease out morphologic and molecular differences in EC between AA and European-American (EA) populations. We identified the differences in immune cell spa...

Objectives: Matching treatment intensity to tumor biology is critical to precision oncology for head and neck squamous cell carcinoma (HNSCC) patients. We sought to identify biological features of tumor cell multinucleation, previously shown by us to correlate with survival in oropharyngeal (OP) SCC using a machine learning approach. Materials an...

Predicting recurrence for at-risk patients with Papillary Thyroid Carcinoma (PTC) could enable appropriate treatment plans for more aggressive subtypes and avoid unnecessary side effects of treatment for lower-risk patients. Recently, it has been shown that genomic information has complemented subvisual pathology-based images features by providing...

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological s...

Significance This study reveals that antiandrogen therapy induces viral mimicry responses that are crucial for antitumor activity. H3K9 trimethylation to silence endogenous repeat elements is essential for regaining heterochromatin stability and progression to antiandrogen resistance in prostate cancer. We found that the H3K9 trimethylation machine...

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently-mutated driver genes but therapeutic targeting of these oncogenes remains challenging. On the other hand, the vast number of genes mutated infrequently across cancers have received less attention, in part, due to a lack of understanding of their b...

Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease....

In the present study, artificial neural networks were used to predict extent of the chemical oxygen demand (COD) removal and FA degradation rate in bioreactor by Ralstonia eutropha. Initial FA concentration, recycling Substrate flow rate, aeration rate and system’s temperature were used as inputs to the network. Feedforward artificial neural networ...

In real-world applications the assumption of independent and identical distribution (i.i.d.) is no longer consistent. To alleviate the significant mismatch between source and target domains, importance weighting import vector machine (IWIVM), which is an adaptive classifier, is proposed. This adaptive probabilistic classification method, which is s...

To facilitate the performance comparison of new methods for sleep patterns analysis, datasets with quality content, publicly-available, are very important and useful. We introduce an open-access comprehensive sleep dataset, called ISRUC-Sleep. The data were obtained from human adults, including healthy subjects, subjects with sleep disorders, and s...

The conventional iris recognition methods do not perform well for the datasets where the eye image may contain nonideal data such as specular reflection, off-angle view, eyelid, eyelashes and other artifacts. This paper gives contributions for a reliable iris recognition method using a new scale-, shift- and rotationinvariant feature-extraction met...

To improve applicability of automatic sleep staging an efficient subject-independent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess...

Current automatic sleep stage classification (ASSC) methods that rely on polysomnographic (PSG) signals suffer from inter-subject differences that make them unreliable in facing with new and different subjects. A novel adaptive sleep scoring method based on unsupervised domain adaptation, aiming to be robust to inter-subject variability, is propose...

Intelligent car navigation systems are planned to assist drivers and route them automatically in roads with sufficient security and correctness. Landmark-based car navigation is a widely used technique in automotive and robot navigation. In this paper, we improved a wireless landmark-based car navigation (WLCN) algorithm to operate in multi-agent (...

A new feature extraction method for iris recognition in non-subsampled contourlet transform (NSCT) domain is proposed. To extract the features a two-level NSCT, which is a shift-invariant transform, and a rotation-invariant gray level co-occurrence matrix (GLCM) with 3 different orientations are applied on both spatial image and NSCT frequency subb...

This paper analyses some of the challenges in automatic multiclass sleep stage classification. Six electroencephalographic (EEG) and two electrooculographic (EOG) channels were used in this study. A set of significant features are selected by a minimum-redundancy maximum-relevance (mRMR) criterion and then classified using support vector machine (S...

This paper analyses some of the challenges in automatic multiclass sleep stage classification. Six electroencephalographic (EEG) and two electrooculographic (EOG) channels were used in this study. A set of significant features are selected by a minimum-redundancy maximum-relevance (mRMR) criterion and then classified using support vector machine (S...

In this paper, a novel algorithm is proposed with application in sleep/awake detection and in multiclass sleep stage classification (awake, non rapid eye movement (NREM) sleep and REM sleep). In turn, NREM is further divided into three stages denoted here by S1, S2, and S3. Six electroencephalographic (EEG) and two electro-oculographic (EOG) channe...

We propose a new nonblind multiresolution watermarking method for still images based on the contourlet transform (CT). In our approach, the watermark is a grayscale image which is embedded into the highest frequency subband of the host image in its contourlet domain. We demonstrate that in comparison to other methods, this method enables us to embe...

In this paper, we propose a new multiresolution watermarking method for still images based on the Contourlet Transform (CT). In our algorithm, the watermark is a grayscale image which is embedded into the host image in its contourlet domain. We demonstrate that in comparison with other methods, the proposed method enables us to embed more amount of...

In this paper, we propose a novel OCR system which can recognize and calculate handwritten Persian arithmetic expressions without using a keyboard or a memory to store the intermediate results. Our system is composed of two major phases: character recognition and calculation. The recognition phase is based on a new approach for feature extraction f...

In this paper we propose a novel OCR system which can recognize and calculate handwritten Persian arithmetic expressions without using a keyboard or a memory to store the intermediate results. Our research is composed of two major phases: character recognition and calculation. The recognition phase is based on a new approach for feature extraction....

Landmark-based car navigation is a widely used technique for automotive and robot navigation. Wireless landmarks have some key features such as robustness and simple detection that make them suitable for automotive navigation. In this paper, a light-weight embedded algorithm for high speed car navigation in the roads with branches is presented whic...

Automatic car navigation systems have been used to guide humans or even automatically route them in roads with sufficient a security and correctness. Landmark based car navigation is a widely used technique for automotive and robot navigation. Wireless landmarks have some key features such as robustness and simple detection that make them suitable...