SUMMARY: In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. AVAILABILITY AND IMPLEMENTATION: The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).

Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present non-profit, academic and publicly available iOS application, PAS-SNP, which invites global users to freely download it on iPhone & iPad devices, effortlessly adopt it's easy to use interface and search for SNPs, genes and related diseases. PAS-SNP is developed with Swift multi-paradigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNP-disease combinations. It includes SNPs released and organized by the Genome-wide Association Study (GWAS). PAS-SNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.

Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate disease-treatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multi-purpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PAS-Code, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PAS-Code is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobile-based tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.

Over the recent years, enormous progress has been made in mapping complex traits in humans. These studies have demonstrated that there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern resources. Our focus here is to create a comprehensive database with mobile access to authentic somatic mutations in cancer and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present a non-commercial, academic and publically available iOS app i.e., PAS-MUT, which invites global users to freely download it on iPhone & iPad devices, quickly adopt its easy to use interface and search for somatic mutations, genes and related diseases. PAS-MUT is developed with Swift multi-paradigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses MySQL database and web servers. PAS-MUT database includes over a million authentic somatic mutations released global, including available at the COSMIC, CNVD, SwissVar, and ClinVar etc. PAS-MUT is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.

UNLABELLED: A major challenge for mass spectrometric-based lipidomics, aiming at describing all lipid species in a biological sample, lies in the computational and bioinformatic processing of the large amount of data that arises after data acquisition. Lipid-Pro is a software tool that supports the identification of lipids by interpreting large datasets generated by liquid chromatography--tandem mass spectrometry (LC-MS/MS) using the advanced data-independent acquisition mode MS(E). In the MS(E) mode, the instrument fragments all molecular ions generated from a sample and records time-resolved molecular ion data as well as fragment ion data for every detectable molecular ion. Lipid-Pro matches the retention time-aligned mass-to-charge ratio data of molecular- and fragment ions with a lipid database and generates a report on all identified lipid species. For generation of the lipid database, Lipid-Pro provides a module for construction of lipid species and their fragments using a flexible building block approach. Hence, Lipid-Pro is an easy to use analysis tool to interpret complex MS(E) lipidomics data and also offers a module to generate a user-specific lipid database. AVAILABILITY AND IMPLEMENTATION: Lipid-Pro is freely available at: http://www.neurogenetics.biozentrum.uni-wuerzburg.de/en/project/services/lipidpro/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. RESULTS: The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. CONCLUSIONS: The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.