COVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.

Nipah virus (NiV) is a zoonotic paramyxovirus of the Henipavirus genus first identified in Malaysia in 1998. Henipavirus have bat reservoir hosts and have been isolated from fruit bats found across Oceania, Asia, and Africa. Bat-to-human transmission is thought to be the primary mode of human NiV infection, although multiple intermediate hosts are described. Human infections with NiV were originally described as a syndrome of fever and rapid neurological decline following contact with swine. More recent outbreaks describe a syndrome with prominent respiratory symptoms and human-to-human transmission. Nearly annual outbreaks have been described since 1998 with case fatality rates reaching greater than 90%. To prevent the spreading of the Nipah virus and turning it into a new pandemic, we must be armed with a ready-made vaccine to save the time consuming that vaccine takes until production. Here we in this paper, we analyzed the whole Nipah virus proteome to find out the most antigenic, non-allergic, and immune inducing epitopes to construct different vaccines that undergone deep investigation to reveal the most appropriate vaccine to immunize humanity from this probably pandemic.

Coronavirus, the current pandemic that takes lives of 1,936,632 until the time of the paper. Scientists over the world exhibited all efforts to fight the virus. Many supplements developed to fight coronavirus by supporting the immune system which is the strongest worrier against it for now. One of the neglected strategies to enhance immunity is music therapy. Music therapy can drive immunity into different conditions. Not only able to boost and suppress immunity but also many physiological and psychological diseases are treated with music therapy. It is more than just entertainment, and research shows that music therapy may be helpful and cost-effective to support standard therapy. This systemic review tries to explore the effect of music therapy and arrives at a conclusion suggesting routine procedures to support the patient with music therapy. However, the idea of using music therapy in improving the immune system must undergo clinical and medical confirmation to be used

Predicting the target of unknown or/and drugs under investigation from data of already identified drugs is very important not only for the understanding of various drug and molecular interaction processes but also for the development of novel drugs. Here we introduce TarDict, a RandomForestClassifier based-software predict the target pathway or protein based on SMILES of chemical. TarDict receives SMILES and returns a list of the possible similar drug, then export list to the user the target that drug contribute in. Training data set of 20442 entry and testing reveal %95 accuracy.

Due to the ability to diagnose diseases early and evaluate the effectiveness of medicinal drugs, single nucleotide polymorphism (SNP) identification receives significant interest. Detection and diagnosis of genetic variation through skill-less computational tools would help researchers reducing the severity of such health complications and improving well-tailored therapies using discovered and previously known information. We introduce SNPector, which is a standalone SNP inspection software, which can be used to diagnose gene pathogenicity and drug reaction in naked genomic sequences. It identifies and extracts gene-related SNPs, and reports their genomic position, associated phenotype disorder, associated diseases, linkage disequilibrium, in addition to various drug reaction information. SNPector detects and verifies the existence of an SNP in a given DNA sequence based on different clinically relevant SNP databases, such as NCBI ClinVar, AWESOME, and PharmGKB, and generates highly informative visualizations of the recovered information.

In 2019, estimated New Cases 268.600, Breast cancer has one of the most common cancers and is one of the world’s leading causes of death for women. Classification and data mining is an efficient way to classify information. Particularly in the medical field where prediction techniques are commonly used for early detection and effective treatment in diagnosis and research.These paper tests models for the mammogram analysis of breast cancer information from 23 of the more widely used machine learning algorithms such as Decision Tree, Random forest, K-nearest neighbors and support vector machine. The spontaneously splits results are distributed from a replicated 10-fold cross-validation method. The accuracy calculated by Regression Metrics such as Mean Absolute Error, Mean Squared Error, R2 Score and Clustering Metrics such as Adjusted Rand Index, Homogeneity, V-measure.accuracy has been checked F-Measure, AUC, and Cross-Validation. Thus, proper identification of patients with breast cancer would create care opportunities, for example, the supervision and the implementation of intervention plans could benefit the quality of long-term care. Experimental results reveal that the maximum precision 100%with the lowest error rate is obtained with Ada-boost Classifier.