ClassifyCNV: a tool for clinical annotation of copy-number variants

MatchMiner: An open source computational platform for matching genomic profiles to precision cancer medicine clinical trials

Here we are going to discuss variant calling on human datasets using GATK Best practices pipeline

Decentralized distributed database of genomic and clinical data.

An Artificial Neural Network-based discriminator for validating clinically significant genomic variants

MTBBench is a benchmark designed to evaluate the reasoning capabilities of multimodal large language models (LLMs) in complex clinical decision-making scenarios. It focuses on two core challenges in oncology: multimodal integration (e.g., pathology, genomics, radiology) and longitudinal reasoning across patient timelines.

The National Biomedical Imaging Archive (NBIA) is a free and open source service and software application that enables users to securely store, search, and download diagnostic medical images, providing a searchable national repository integrating in vivo cancer images with clinical and genomic data. Using role-based security, NBIA provides web-based access to de-identified DICOM images, image markup, annotations, and rich meta data.

Source for the HL7 Genomics work group's "Clinical Genomics-Reporting" FHIR implementation guide

The Genomic Epidemiology Application Ontology describes the genomics, laboratory, clinical and epidemiological contextual information required to support data sharing and integration for foodborne infectious disease surveillance and outbreak investigations.

This guidance creates a scalable environment in AWS to prepare genomic, clinical, mutation, expression and imaging data for large-scale analysis and perform interactive queries against a data lake. The solution also demonstrates the use of Amazon Omics for multi-modal analysis.

A platform for matching patient-specific genomic profiles to precision cancer medicine clinical trials

A pipeline for comprehensive genomic analyses of Mycobacterium tuberculosis with a focus on clinical decision making as well as research

Research pipeline for exploring clinically relevant genomic variants

PAnno is a Pharmacogenomics Annotation tool for clinical genomic testing.

EcoliTyper: A species-optimized computational pipeline for comprehensive genotyping and surveillance of Escherichia coli. Perfect for clinical microbiology, outbreak investigations, and genomic research.

Glue between Clinical Genomics apps

Machine learning system for predicting genetic disorders using genomic, clinical, and demographic data. Implements robust preprocessing, feature selection, and multi-model classification (RF, XGBoost, LightGBM, CatBoost) with cross-validation to support early, data-driven genetic risk assessment.

A Nextflow pipeline for viral genomics (Influenza/RSV) supporting Illumina and Nanopore sequencing of clinical and wastewater samples.

Creating analytic cohorts from AACR Project GENIE BPC clinically annotated genomic data

Evaluation of multiple genomic language model for clinical VEP

A clinical genomics-guided prioritizing strategy enables accurately selecting proper cancer cell lines for biomedical research

A web application to facilitate the clinical interpretation of genetic and genomic variants from exome sequencing projects

Integrating Genomic Data and Pathological Images to Effectively Predict Breast Cancer Clinical Outcome

A platform for matching patient-specific genomic profiles to precision cancer medicine clinical trials

Functions for working with the Human Phenotype Ontology data

Decentralized database of genomic and clinical data on the blockchain

Integrating bioinformatics and functional genomics in the clinical classification of genetic variation: Hereditary breast and ovarian cancer as a paradigm

Importance of Cancer Subtype prediction: Cancer is a heterogeneous disease caused by chemical, physical, or genetic factors. Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. Bioinformatics approaches have gradually taken the place of clinical observations and pathological experiments. The development of high-throughput genome analysis techniques on the research of cancer subtypes plays an important role in the analysis and clinical treatment of various kinds of cancers. Omics dataset: The process of mapping and sequencing the human genome began, new technologies have made it possible to obtain a huge number of molecular measurements within a tissue or cell. These technologies can be applied to a biological system of interest to obtain a snapshot of the underlying biology at a resolution that has never before been possible. Broadly speaking, the scientific fields associated with measuring such biological molecules in a high-throughput way are called omics.Omics are novel, comprehensive approaches for analysis of complete genetic or molecular profiles of humans and other organisms. the types of omics data that can be used to develop an omics-based test are discussed below: genomics, proteomics, transcriptomics and metabolomics. Importance of Omics Data with respect to Cancer Prediction: Accurately predicting cancer prognosis is necessary to choose precise strategies of treatment for patients. One of effective approaches in the prediction is the integration of multi-omics data, which reduces the impact of noise within single omics data. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years.Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. Approaches of omics data concatenation: 1.Integrative NMF 2.Similarity Network Fusion 3.Joint Non Negative Matrix Factorization Deep Learning: Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Hyperparameter tuning: Hyperparameter tuning works by running multiple trials in a single training job. Each trial is a complete execution of your training application with values for your chosen hyperparameters, set within limits you specify. The AI Platform Training training service keeps track of the results of each trial and makes adjustments for subsequent trials. When the job is finished,you can get a summary of all the trials along with the most effective configuration of values according to the criteria you specify.

Pan-cancer association of a centrosome amplification gene expression signature with genomic alterations and clinical outcome

Evaluation of the Ultima Genomics UG 100™ sequencing platform for clinical services