Understanding Big Data in Neurosurgery.

Big data refers to a large amount of data generated and distributed across diverse data sources from open, private, social, and Internet of Things (IoT). Understanding and harnessing the big data in the biomedical sciences, specifically in neurosurgery, is crucial as it can lead to breakthroughs in understanding complex neurological disorders, optimizing surgical interventions, evaluating long-term patient outcomes, and developing predictive models of disease progression, enabling personalized treatment plans tailored to the genetic, molecular, and environmental factors unique to each patient. Furthermore, Big data analytics can facilitate a deeper understanding of the socioeconomic factors contributing to neurological disorders, leading to more effective public health strategies and interventions.

Declaration of Computational Neurosurgery.

Computational neurosurgery is a novel and disruptive field where artificial intelligence and computational modeling are used to improve the diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. The field aims to bring new knowledge to clinical neurosciences and inform on the profound questions related to the human brain by applying augmented intelligence, where the power of artificial intelligence and computational inference can enhance human expertise. This transformative field requires the articulation of ethical considerations that will enable scientists, engineers, and clinical neuroscientists, including neurosurgeons, to ensure that the use of such a powerful application is conducted based on the highest moral and ethical standards with a patient-centric approach to predict and prevent mistakes.

GENet: A Graph-Based Model Leveraging Histone Marks and Transcription Factors for Enhanced Gene Expression Prediction.

Understanding the regulatory mechanisms of gene expression is a crucial objective in genomics. Although the DNA sequence near the transcription start site (TSS) offers valuable insights, recent methods suggest that analyzing only the surrounding DNA may not suffice to accurately predict gene expression levels. We developed GENet (Gene Expression Network from Histone and Transcription Factor Integration), a novel approach that integrates essential regulatory signals from transcription factors and histone modifications into a graph-based model.

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view.

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective.

Increasing stage and depth of invasion (DOI) in patients with tongue cancer during the COVID-19 pandemic: A time series study.

Background And Aims: The outbreak of the Coronavirus disease 2019 (COVID-19) pandemic had a significant effect on the diagnosis and treatment of head and neck cancers. Therefore, in this study, we decided to discuss the impact of COVID-19 on the stage and histological characteristics of patients with tongue cancer from March 2020 to March 2021 and compared to the previous 3 years.

Methods: In this time series study, patients diagnosed with squamous cell carcinoma of the operated tongue cancer were divided into two groups.

A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals.

KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition.

Here we developed , a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, provides a parallel downloading framework powered by which reduces the downloading time significantly.

A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments.

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.

A Survey on Deep Learning Event Extraction: Approaches and Applications.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study.

Copy Number Variation (CNV) refers to a type of structural genomic alteration in which a segment of chromosome is duplicated or deleted. To date, many CNVs have been identified as causative genetic elements for several diseases and phenotypes. However, performing a CNV-based genome-wide association study is challenging due to inconsistency in length and occurrence of CNVs across different individuals under investigation.

Deep reinforcement learning guided graph neural networks for brain network analysis.

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed.

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.

Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity.

Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, "ConvNet", detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise.

Impact of the COVID-19 pandemic on the stage and the type of surgical treatment of laryngeal cancer.

Purpose: Changes in the entire health care system during COVID-19 epidemic have affected the management of patients with head and neck cancer and posed several clinical challenges for ENT surgeons. Therefore, the present study aimed to investigate the effect of COVID-19 on the stage and the type of surgical treatments used in laryngeal cancer (including total laryngectomy, supracricoid partial laryngectomy (SCPL) and transoral laser microsurgery (TLM)) and also to compare the results of April 2020 to April 2021 with the previous year.

Materials And Methods: This cross-sectional study was performed on all patients with a diagnosis of laryngeal cancer who underwent surgery in the tertiary care center from April 2020 to April 2021 and the year before the pandemic in the same time.

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer.

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes.

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis.

Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences.

Proposing a novel community detection approach to identify cointeracting genomic regions.

Modern next generation sequencing technologies produce huge amounts of genome-wide data that allow researchers to have a deeper understanding of genomics of organisms. Despite these huge amounts of data, our understanding of the transcriptional regulatory networks is still incomplete. Conformation dependent chromosome interaction maps technologies (Hi-C) have enabled us to detect elements in the genome which interact with each other and regulate the genes.