Human Microbiome plays a critical role in health and the environment. Colorectal cancer (CRC) is the most common cause of death in many countries, and hence early diagnosis of CRC may help in increasing the survival rate. Tracking changes in the microbiome structure of human gut opens new gates towards the detection and prediction of the risk of CRC. Recently, machine learning became a powerful

Metagenomics is a rapidly emerging field that is concerned with the study of microbial communities 'microbiomes' on both levels of taxonomic classification and functional annotation. Targeted amplicon (16S rRNA) and whole genome shotgun (WGS) sequencing are the two main sequencing strategies in metagenomics. As amplicon sequencing provides a cheap way to classify the composition of a microbial

Transforming growth factor beta (TGF-β) acts as a tumor-suppressing cytokine in healthy tissues and non-malignant tumors. Yet, in malignancy, TGF-β can exert the opposite effects that can promote proliferation of cancer cells. C-Kit plays a prominent role in stem cell activation and liver regeneration after injury. However, little is known about the cross-talk between TGF-β and C-Kit and its role

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral

Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data

Skin cancer is one of the leading causes of death globally. Early diagnosis of skin lesion significantly increases the prevalence of recovery. Automatic classification of the skin lesion is a challenging task to provide clinicians with the ability to differentiate between different kind of lesion categories and recommend the suitable treatment. Recently, Deep Convolutional Neural Networks have

The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these

Introduction: Cancer is one of the most difficult challenges faced by humanity due to its many associated issues, such as inability to prevent diseases, treatment safety, and high mortality rate. In cancer, a variety of cellular signaling is activated to ensure malignancy transformation, angiogenesis and metastasis. The most efficient signaling pathway in cancer is mitogen-activated protein kinase

Background: Currently, formal mechanisms for bioinformatics support are limited. The H3Africa Bioinformatics Network has implemented a public and freely available Helpdesk (HD), which provides generic bioinformatics support to researchers through an online ticketing platform. The following article reports on the H3ABioNet HD (H3A-HD)'s development, outlining its design, management, usage and

Non-Synonymous Single-Nucleotide Variants (nsSNVs) and mutations can create a diversity effect on proteins as changing genotype and phenotype, which interrupts its stability. The alterations in the protein stability may cause diseases like cancer. Discovering of nsSNVs and mutations can be a useful tool for diagnosing the disease at a beginning stage. Many studies introduced the various predicting