Gluconeogenesis unraveled: A proteomic Odyssey with machine learning.

The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It's extremely important to anticipate gluconeogenesis rates accurately to recognize metabolic disorders and create efficient treatment strategies. The implementation of deep learning and machine learning methods to forecast complex biological processes has been gaining popularity in recent years.

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes.

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.

Background: 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.

Objective: Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.

Methodology: The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms.

BBB-PEP-prediction: improved computational model for identification of blood-brain barrier peptides using blending position relative composition specific features and ensemble modeling.

BBPs have the potential to facilitate the delivery of drugs to the brain, opening up new avenues for the development of treatments targeting diseases of the central nervous system (CNS). The obstacle faced in central nervous system disorders stems from the formidable task of traversing the blood-brain barrier (BBB) for pharmaceutical agents. Nearly 98% of small molecule-based drugs and nearly 100% of large molecule-based drugs encounter difficulties in successfully penetrating the BBB.

Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets.

Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances.

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features.

Objective: The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information.

Methods: Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification.

Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.

Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despite still being poorly understood.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually.

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes.

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging.

Green synthesis of FeO nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression.

The synthesis of metal nanoparticles using green chemistry methods has gained significant attention in the field of landscape enhancement. Researchers have paid close attention to the development of very effective green chemistry approaches for the production of metal nanoparticles (NPs). The primary goal is to create an environmentally sustainable technique for generating NPs.

Latest insights on separation and storage of carbon compounds in buildings towards sustainable environment: Recent innovations, challenges, future perspectives and application of machine learning.

Throughout the past few decades, scientific agencies have paid a lot of attention to environmental issues such as acid rain, water poisoning, and global warming. In order to solve these environmental problems, metal-organic frameworks (MOFs), which are made up of metal ions and/or clusters attached to organic ligands, have shown some promise. With a focus on the usage of MOFs, this paper examines the most recent developments, difficulties, and potential future directions in the separation and storage of carbon compounds in buildings for a sustainable environment.

iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models.

Background: Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification.

Objective: The modification also triggers lung cancer in humans.

Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging.

Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used.

IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.

. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition.

Application of aluminum oxide nanoparticles in asphalt cement toward non-polluted green environment using linear regression.

In order to decrease the greenhouse gas emissions generated by regular Portland cement (OPC), additional cementitious ingredients have been frequently employed, even while building road bases. OPC's susceptibility to moisture and lack of flexibility make it ineffective for stabilizing road bases. This research used alkali-activated materials (AAM) with fly ash to investigate the mechanical properties of cold asphalt binder (freeze-thaw cycles) including the compressive, flexural strength, workability and porosity of cement.

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features.

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease.

Impact of cybersecurity measures on improving institutional governance and digitalization for sustainable healthcare.

Digitalization in healthcare through advanced methods, tools, and the Internet are prominent social development factors. However, hackers and malpractices through cybercrimes made this digitalization worrisome for policymakers. In this study, the role of E-Government Development as a proxy for digitalization and corruption prevalence has been analyzed in Healthcare sustainability in developing and underdeveloped countries of Asia from 2015 to 2021.

DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers.

Background: Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans.

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations.

The abnormal growth of human healthy cells is called cancer. One of the major types of cancer is sarcoma, mostly found in human bones and soft tissue cells. It commonly occurs in children.

Application of nano remediation of mine polluted in acid mine drainage water using machine learning model.

Acid mine drainage (AMD) is the term used to describe drainage from coal mines with high sulfur-bearing rocks. The oxidative weathering of metal sulfides leads to AMD. The acidic environment corrodes more harmful compounds in the soil, which is spread throughout the working area.

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells.

Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances.

It is necessary to determine whether synthetic dyes are present in food since their excessive use has detrimental effects on human health. For the simultaneous assessment of tartrazine and Patent Blue V, a novel electrochemical sensing platform was developed. As a result, two artificial azo colorants (Tartrazine and Patent Blue V) with toxic azo groups (-NN-) and other carcinogenic aromatic ring structures were examined.

Automatic computer science domain multiple-choice questions generation based on informative sentences.

Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge.

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.

Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency.

The Effects of Transformational Leadership, Organizational Innovation, Work Stressors, and Creativity on Employee Performance in SMEs.

Purpose Of The Study: The significance of creativity and performance in the workplace has been illustrated on various occasions. This study aims to find out if there is a link between transformative leadership, organizational innovation, psychological issues such as hindrance and challenge stressors, and employee creativity and employee performance. There is still a lack of awareness of the factors that influence employee performance in small and medium businesses (SMEs) in Pakistan.