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Topic Review
Cell-Type Annotation
Multicellular organisms consist of cells that can be categorized by their function and morphology. Single-cell transcriptomics makes it possible to individually profile thousands of cells in multiple tissues and organisms within a single experiment. Determining and labeling cell types or states in single cell transcriptomic data is known as cell-type annotation or identification. Several methods are employed for cell-type annotation, including signature scoring, supervised learning, cell-integration-based label transfer, and semi-supervised annotation. Considering the lineage relationships among cell types, hierarchical classification methods are crucial for accurately identifying cell types and subtypes at an optimal clustering resolution. The use of well-curated reference datasets, implementation of quality control measures, and careful consideration of cluster resolutions heavily influence the reliability of cell-type annotation. The aim of cell-type annotation is to gain insights into cell heterogeneity in various biological processes and diseases, with the potential to drive improvements in therapeutic interventions.
  • 654
  • 08 Aug 2023
Topic Review
Genome-Scale Metabolic Modelling
Genome-scale metabolic models (GEMs) aim to systematically encode knowledge of the metabolism of an organism. GEMs are composed of different layers of information and are constructed with a combination of automated approaches and manual curation based on the available literature and experimental data. These models not only encode existing knowledge about an organism, but can also generate new knowledge through various analytical methods. The latter are mostly focused on the assessment of reaction fluxes through the metabolic network in different conditions.
  • 636
  • 28 Jan 2023
Topic Review
Computational Biology in Drug Design
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, researchers propose a methodology for integrating various computational techniques into new drug discovery and design.
  • 630
  • 28 Nov 2022
Topic Review
Genome by Multidimensional Scaling
The positions of enhancers and promoters on genomic DNA remain poorly understood. Chromosomes cannot be observed during the cell division cycle because the genome forms a chromatin structure and spreads within the nucleus. However, high-throughput chromosome conformation capture (Hi-C) measures the physical interactions of genomes. In previous studies, DNA extrusion loops  were directly derived from Hi-C heat maps. By using Multidimensional Scaling (MDS), we can easily locate enhancers and promoters more precisely.
  • 595
  • 31 Oct 2021
Topic Review
Classification Algorithms for Unifloral Honeys
Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst. 
  • 583
  • 05 Jul 2021
Topic Review
Imaging Techniques for Cardiac Function
Cardiac imaging techniques include a variety of distinct applications with which we can visualize cardiac function non-invasively. Through different applications of physical entities such as sound waves, X-rays, magnetic fields, and nuclear energy, along with highly sophisticated computer hardware and software, it is now possible to reconstruct the dynamic aspect of cardiac function in many forms, from static images to high-definition videos and real-time three-dimensional projections.
  • 547
  • 19 Nov 2021
Biography
Lilach Soreq
Lilach was a 3 years Alzheimer’s Society Research Fellow at UCL ION London UK (then 3 years RoseTrees fellow) studying human brain aging. She obtained her B.Sc. in computer science, M.Sc in developmental biology, and her Ph.D. in neurobiology studying RNA regulation in Parkinson’s disease resulting in more than ten first-author papers. Her post-doctoral training was funded by the competitive
  • 531
  • 10 Jan 2023
Topic Review
Real-World Driver Stress Recognition and Diagnosis
Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches.
  • 516
  • 19 Jun 2023
Topic Review
Transformer Architecture and Attention Mechanisms in Genome Data
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. 
  • 498
  • 26 Jul 2023
Topic Review
Global Trends in Cancer Nanotechnology
This study presents a new way to investigate comprehensive trends in cancer nanotechnology research in different countries, institutions, and journals providing critical insights to prevention, diagnosis, and therapy. This paper applies the qualitative method of bibliometric analysis on cancer nanotechnology using the PubMed database during the years 2000-2021. Inspired by hybrid medical models and content-based and bibliometric features for machine learning models, our results show cancer nanotechnology studies have expanded exponentially since 2010. The highest production of articles in cancer nanotechnology is mainly from US institutions, with several countries notably the USA, China, UK, India, and Iran as concentrated focal points as centers of cancer nanotechnology research, especially in the last five years. The analysis shows the greatest overlap between nanotechnology and DNA, RNA, iron oxide or mesoporous silica, breast cancer, and cancer diagnosis and cancer treatment. Moreover, more than 50% of information related to the keywords, authors, institutions, journals, and countries are considerably investigated in the form of publications from the top 100 journals. This study has the potentials to provide past and current lines of research that can unmask comprehensive trends in cancer nanotechnology, key research topics, or pmost productive countries and authors in the field.
  • 489
  • 10 Sep 2021
Topic Review
Hydrotropism
Hydrotropism is the movement or growth of a plant towards water. It is a type of tropism, or directional growth response, that is triggered by water. Plants are able to detect water through various stimuli, including changes in moisture levels and changes in water potential. 
  • 476
  • 23 Feb 2023
Topic Review
State-of the-Art Constraint-Based Modeling of Microbial Metabolism
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. 
  • 474
  • 03 Jan 2024
Topic Review
Molecular Dynamics Simulations for DNAs
DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. Molecular dynamics (MD) simulations can generally reproduce the behavior of DNAs in a computer, providing detailed structural and dynamical insights that enhancing our comprehension of relevant experimental data. MD simulations using classical force fields such as AMBER and CHARMM have provided highly detailed and flexible descriptions of DNA dynamics, including structural transformations, stability of non-canonical conformations, salt ion cohesion effects, twist-stretch coupling of stress, flexibility under methylation modifications, and interactions with other macromolecules. It is always fascinating to obtain microscopic insights into DNA dynamics through MD simulations. However, the innumerable degrees of freedom, interconnected in complex ways, can make it practically impossible to detect DNA dynamics on biologically relevant time scales and length scales using currently available computer hardware
  • 454
  • 28 Jun 2023
Topic Review
Deep Learning for Protein-Protein Interaction
Deep learning is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities.
  • 444
  • 04 Jul 2023
Topic Review
A Taxonomic Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information.
  • 399
  • 20 Jun 2023
Topic Review
Approaches to Cardiovascular and Respiratory Systems Modelling
'Medicine in silico' has been strongly encouraged due to ethical and legal limitations related to animal experiments and investigations conducted on patients. Computer models, particularly the very complex ones (virtual patients—VP), can be used in medical education and biomedical research as well as in clinical applications. Simpler patient-specific models may aid medical procedures. However, computer models are unfit for medical devices testing. Hybrid (i.e., numerical–physical) models do not have this disadvantage.
  • 391
  • 20 Jun 2022
Topic Review
Unique Properties of the Immune System
The human body is unquestionably one of the most complex systems known to humanity. There are three main regulation systems in the human body (the nervous system, the endocrine system and the immune system). These three systems are integrated into one ultimate information communication network within the human body. However, each regulation system has its specific roles and unique properties. Consequently, each of these regulation systems has served as inspiration for computational models to efficiently solve real-world problems. An overview of these models and their applications is presented.
  • 386
  • 01 Feb 2023
Topic Review
Kinases/Protein Phosphatases in Signaling Pathways Activation
Optimizing physical training regimens to increase muscle aerobic capacity requires an understanding of the internal processes that occur during exercise that initiate subsequent adaptation. During exercise, muscle cells undergo a series of metabolic events that trigger downstream signaling pathways and induce the expression of many genes in working muscle fibers. There are a number of studies that show the dependence of changes in the activity of AMP-activated protein kinase (AMPK), one of the mediators of cellular signaling pathways, on the duration and intensity of single exercises. The activity of various AMPK isoforms can change in different directions, increasing for some isoforms and decreasing for others, depending on the intensity and duration of the load.
  • 385
  • 11 Jul 2023
Topic Review
Application of GANs in Gene Expression Data Augmentation
A generative adversarial network (GAN) is essentially a two-player game composed of a generator and a discriminator. The generator’s role is to create synthetic data, while the discriminator’s task is to distinguish between real and generated data. During the training process, the generator strives to produce data that the discriminator cannot differentiate from the real data, whereas the discriminator continually improves its ability to distinguish real from generated data. This adversarial training regimen imbues GANs with the capability to model complex data distributions and produce high-quality synthetic data. Notably, their application to gene expression data systems is a fascinating and rapidly growing focus area.
  • 382
  • 21 Jul 2023
Topic Review
Deep Learning in Whole Slide Imaging for Cancer
The significant progress made in the field of cancer prognosis using whole slide images (WSIs) is encouraging, indicating a promising future for cancer diagnosis and management. The ability to accurately predict survival rates and recurrence risk using deep learning methods has significant implications for clinical practice and patient care. As more sophisticated models and techniques are developed, the potential to revolutionize the field of oncology is immense.
  • 350
  • 02 Aug 2023
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