A neoantigen is a novel peptide (protein fragment) that is produced by cancer cells due to mutations, including gene fusions, that alter the DNA sequence in a way that generates unique proteins not found in normal cells. Because these mutated proteins are unique to the tumor, they are recognized as "foreign" by the immune system. Neoantigens are valuable in immunotherapy because they can serve as specific targets for the immune system, allowing treatments to selectively attack cancer cells while sparing normal tissue. By stimulating an immune response specifically against these neoantigens, therapies like cancer vaccines or T-cell-based treatments can be developed to enhance the body’s natural defense mechanisms, making neoantigens a promising avenue for personalized cancer treatment.

Creating a fusion database is essential in cancer genomics and personalized medicine, as it enables the identification of crucial biomarkers, enhances diagnostic accuracy, and supports therapeutic development. Gene fusions, where parts of two previously separate genes merge, can produce abnormal proteins that drive cancer. Cataloging these fusion events in a database helps researchers identify specific biomarkers linked to cancer types and design more targeted treatments. Additionally, fusion events may lead to unique peptide sequences, known as neoantigens, which are found only in cancer cells. These neoantigens can be targeted by the immune system, making fusion databases valuable in designing personalized immunotherapies like cancer vaccines or T-cell therapies. Some gene fusions also create oncogenic proteins that promote tumor growth, such as the BCR-ABL fusion in chronic myeloid leukemia. Including such information in a database aids in identifying potential therapeutic targets and predicting treatment efficacy. On the diagnostic side, known gene fusions serve as reliable markers, helping clinicians better classify cancer types and choose the most effective treatments. Finally, fusion databases provide a critical reference for researchers studying fusion mechanisms, their impact on disease progression, and their prevalence across cancers, ultimately fueling the discovery of novel treatments and therapies.

Database Fusion

## Overview

Database Fusion is a dataset for object detection tasks - it contains Pipes annotations for 2,942 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Description of 24 fusion gene datasets included in FusionHub server.

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

Functional annotation database of human fusion genes.FusionGDB 2.0 has updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with human genomic features across cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of protein feature retention of individual fusion partner genes in protein level.

This file contains all proteins used in the generation of fusion. This file is also a prerequisite to generate fusion organism similarities with https://bitbucket.org/bromberglab/fusion-organism_sim.

There is a growing demand for commodities (elements, compounds, minerals) used in today's advanced technologies. Critical minerals are usually found in ore deposits that are deemed vital to economic and national security. National Geochemical Database on Ore Deposits: New data featuring fusion digestion (NGDOD) analytical methods contains chemistry and geologic information for 16,579 ore and ore-related rock and mineral samples from mineral deposits and mining districts in the United States. Geochemical data sets from various mineral deposits were submitted by geologists of the Systems Approach to Critical Minerals Inventory, Research, and Assessment project within the U.S. Geological Survey Mineral Resource Program. The data sets represent 22 mineral system types and 85 mineral deposit types, and were derived by using fusion digestion analytical methods that provide a near complete digestion and decomposition of the analyzed samples. The NGDOD also includes data from 966 rock samples of the Global Geochemical Database for Critical Minerals in Archived Ore Samples (2020) and 370 rock samples of the National Geochemical Database on Ore Deposits: Legacy data (2021). Much of this data has been compiled and is additionally found within smaller contributions. Please see references for the original sources of the data. The data may also have been utilized in interpretive manuscripts that have been published in peer-reviewed journals or other U.S. Geological Survey reports that can be found through cursory literature searches using authors and locations.

Data Fusion Market size was valued at USD 17.55 Billion in 2024 and is projected to reach USD 54.66 Billion by 2032, growing at a CAGR of 15.26% from 2026 to 2032.Growing Adoption of Big Data Analytics and IoT Technologies Across Industries: The explosion of Big Data and the widespread deployment of Internet of Things (IoT) technologies serve as the fundamental fuel for the Data Fusion Market. IoT devices from industrial sensors and smart meters to wearable tech generate continuous, high volume, and varied data streams.Increasing Need for Real Time Data Processing and Decision Making in Businesses: In today's hyper competitive and rapidly evolving business environment, the ability to make real time decisions is non negotiable, driving strong demand for Data Fusion solutions.

In this competition, you will have to develop an algorithm for automatic categorization of products by their name and attributes, even in conditions of incomplete marking.

The category system is arranged in the form of a hierarchical tree (up to 5 levels of nesting), and product data comes from many trading platforms, which creates a number of difficulties:

• Product marking is incomplete and imperfect.
• The attributes of the same products may differ on different platforms or be absent altogether.
• As the catalog expands, new platforms and categories may appear that did not exist before.

In this competition, we invite participants to try their hand at setting up a task as close to the real one as possible:

• The task is complicated by the fact that in the training sample, categories are marked only for half of the products, and in the test sample there will be categories for which there are no marked examples.
• At the same time, you are provided with a complete list of possible categories in advance - will you be able to take them all into account and accurately assign products to the right classes?
• LLMs (Large Language Models) look promising for such tasks – but will they really help? This is a great chance to test different approaches!

Comprehensive dataset containing 53 verified Fusion locations in United States with complete contact information, ratings, reviews, and location data.

Portal provides an easy access to a comprehensive database designed for storing, displaying and annotating gene fusion events detected from NGS data. It can query a database of somatic fusion genes events predicted and annotated starting from paired-end RNA-seq data.

The global Data Fusion market is projected to reach a valuation of USD 15.3 billion by 2033, growing at a compound annual growth rate (CAGR) of 12.5% from 2025 to 2033.

Background Gene fusions can be used as tools for functional prediction and also as evolutionary markers. Fused genes often show a scattered phyletic distribution, which suggests a role for processes other than vertical inheritance in their evolution. Results The evolutionary history of gene fusions was studied by phylogenetic analysis of the domains in the fused proteins and the orthologous domains that form stand-alone proteins. Clustering of fusion components from phylogenetically distant species was construed as evidence of dissemination of the fused genes by horizontal transfer. Of the 51 examined gene fusions that are represented in at least two of the three primary kingdoms (Bacteria, Archaea and Eukaryota), 31 were most probably disseminated by cross-kingdom horizontal gene transfer, whereas 14 appeared to have evolved independently in different kingdoms and two were probably inherited from the common ancestor of modern life forms. On many occasions, the evolutionary scenario also involves one or more secondary fissions of the fusion gene. For approximately half of the fusions, stand-alone forms of the fusion components are encoded by juxtaposed genes, which are known or predicted to belong to the same operon in some of the prokaryotic genomes. This indicates that evolution of gene fusions often, if not always, involves an intermediate stage, during which the future fusion components exist as juxtaposed and co-regulated, but still distinct, genes within operons. Conclusion These findings suggest a major role for horizontal transfer of gene fusions in the evolution of protein-domain architectures, but also indicate that independent fusions of the same pair of domains in distant species is not uncommon, which suggests positive selection for the multidomain architectures.

Market Analysis for Operation Data Fusion Operation Data Fusion (ODF) is a market experiencing significant growth, projected to reach a value of $XX million by 2033, with a CAGR of XX%. Key drivers include the increasing volume of data, advancements in data management technologies, and the need for improved decision-making. ODF enables organizations to integrate diverse data sources and extract meaningful insights, enhancing operational efficiency and competitiveness. The market consists of various segments based on application (large enterprises and SMEs) and type (managed services and professional services). Prominent players include Thomson Reuters, AGT International, ESRI, LexisNexis, and Palantir Technologies, among others. North America is a significant region, contributing a major share to the market, followed by Europe and Asia Pacific. The market is expected to expand rapidly in developing regions, driven by increasing data adoption and the need for data-driven decision-making. However, the cost of data integration and concerns over data privacy may present some challenges.

Data Fusion Market was valued at USD 32.28 billion in 2024 and is expected to reach USD 132.58 billion by 2030 with a CAGR of 26.36%.

speciated pm2.5 monitoring data and total pm2.5 monitoring data. This dataset is associated with the following publication: Rundel, C., E. Schliep, A. Gelfand, and D. Holland. A data fusion approach for spatial analysis of speciated PM2:5 across time. Annals of Applied Statistics. Institute of Mathematical Statistics, Beachwood, OH, USA, 26(0): 515-526, (2015).

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission. GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration. GeoThermalCloud.jl includes: - site data - simulation scripts - jupyter notebooks - intermediate results - code outputs - summary figures - readme markdown files GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites: - Brady: geothermal exploration of the Brady geothermal site, Nevada - SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region - GreatBasin: geothermal exploration of the Great Basin region, Nevada Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.

In 2024, Market Research Intellect valued the Data Fusion Solutions Market Report at USD 3.5 billion, with expectations to reach USD 8.4 billion by 2033 at a CAGR of 10.5%.Understand drivers of market demand, strategic innovations, and the role of top competitors.

Study Population Patients were adults (≥ 18 years) who underwent lumbar fusion for degenerative pathologies, including spondylolisthesis, spondylosis, spinal stenosis, and disc disease. Non-instrumented procedures were defined as posterior in situ fusions. Instrumented procedures were limited to posterolateral lumbar fusions, posterior lumbar interbody fusions (PLIF), and transforaminal lumbar interbody fusion (TLIF) allowing for comparison of reoperation rates between instrumented and non-instrumented strategies. Data Collection and Cleaning 3,262 patient encounters from January 1, 2009 to November 30, 2024 involving the lumbar spine were initially identified from the institutional operative database. A systematic cleaning process was then applied: 1. Encounters prior to 2012 were excluded due to inconsistent ASA score recording. 2. Cases were restricted to those performed by the two primary spine surgeons. 3. Anterior, lateral, or oblique approaches were excluded, as well as cervical, thoracic, or thoracolumbar fusions, restricting the population to posterior lumbar procedures. 4. Cases without fusion related keywords (“fusion”, “instrumentation”, “hardware”, “in situ”) and containing only decompression-related terms (laminectomy, foraminotomy, discectomy, kyphoplasty, coccygectomy) were removed. Subsequent encounters for these patients were excluded to limit analysis to primary lumbar fusion cases. 5. Iliac or pelvic fixation were excluded to restrict the sample to procedures with S1 as the most caudal level, 6. Initial encounters consisting of wound washouts, CSF leak repairs, or incision and drainage were excluded, along with their subsequent encounters. 7. Index procedures with hardware removal or fusion mass exploration without documentation of the original fusion were excluded. 8. Index procedures performed in 2022 or later were excluded; retaining only follow-up visits through November 30st, 2024. 515 cases remained, including both primary fusions (393) and subsequent reoperations.

Three ET datasets were generated to evaluate the potential integration of Landsat and Sentinel-2 data for improved ET mapping. The first ET dataset was generated by linear interpolation (Lint) of Landsat-based ET fraction (ETf) images of before and after the selected image dates. The second ET dataset was generated using the regular SSEBop approach using the Landsat image only (Lonly). The third ET dataset was generated from the proposed Landsat-Sentinel data fusion (L-S) approach by applying ETf images from Landsat and Sentinel. The scripts (two) used to generate these three ET datasets are included – one script for processing SSEBop model to generate ET maps from Lonly and another script for generating ET maps from Lint and L-S approach.