Matching Search Research Articles

Improvement of Criminisi’s Stripe Noise Suppression Method for Side-Scan Sonar Images

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results.

Human behaviour simulation for promoting usefulness and user-centric values in parametric design

Although contemporary architectural design education adopts the parametric design approach, which involves complex iterations between the arrangement of form components and building performance, this method limits students' exploration of the optimal matches between generated form and the behaviours of prospective users. Therefore, as a solution, this study hypothesises the effects of human behaviour simulation on students' exploration of user-centric values in the parametric design process. This paper compares the self-evaluation scores of 90 undergraduate students before and after simulation. Data were collected during an authentic digital design course that examined shopfront façade prototypes. Statistical analysis and interpretation indicate that simulation better facilitates students' search for matches between initially value-isolated forms, created using a parametric design tool, and useful user-centric values. However, simulation is not relevant for discovering unexpected forms. This study addresses the unknown affordances in simulation when assigning user-related value to form generation that lacks value.

Development of fuzzy search method for creating an efficient information search system in text data

The object of research is the processes of effective search for information in a set of textual data. The subject of the research is the fuzzy search method, which will allow to effectively solve the problem of searching for information in a set of textual data. The paper considers the process of developing a fuzzy search method, which consists of 9 consecutive steps and is required for a quick search for matches in a large set of text data. Based on this method, it is proposed to create a fuzzy search system that will solve the problem of finding the most relevant documents from a set of such documents. The proposed fuzzy search method combines the advantages of algorithms based on deterministic finite automata and algorithms based on dynamic programming for calculating the Damerau-Levenshtein distance. Such a combination allows to implement the symbol similarity table in an optimal way. As part of the work, an approach for creating a symbol similarity table was proposed and an example of such a table was created for symbols from the English alphabet, which allows to find the degree of similarity between two symbols with constant asymptotics and to convert the current symbol into its basic counterpart. For document filtering, a metric was developed to evaluate the correspondence of text data to a search phrase, which simultaneously takes into account the number of found and not found characters and the number of found and not found words. The Damerau-Levenstein algorithm allows to find the edit distance between two words, taking into account the following types of errors: substitution, addition, deletion, and transposition of characters. The work proposed a modification of this algorithm by using a similarity table to more accurately estimate the editing distance between two words. The developed method makes it possible to create a fuzzy search system that will help find the desired results faster and increase the relevance of the obtained results by sorting them according to the values of the proposed test data similarity metric.

Analysis of String Matching Application on Serial Number Using Boyer Moore Algorithm

Nowadays, technology has become the most important pillar in business management. The rapid development of technology has a significant impact on various aspects of business, from operational efficiency to marketing strategies. Applications are very important in a company or agency. With an information system, companies and agencies can easily guarantee the quality of information that will be presented for decision-making. Now, much information can be easily obtained quickly, thanks to information technology. The speed and accuracy of information delivery is a challenge for all producers in running their business. Boyer-Moore algorithm is one of the algorithms that can be used in the Barcode Generator application to scan barcode product serial numbers. The Boyer-Moore algorithm method functions to find sequence numbers. The development process requires several stages of investigation in the form of data collection techniques, problem identification, application of the Boyer-Moore algorithm, implementation, and system testing. This iterative process makes the application of string matching with the Boyer-Moore algorithm technique into a very accurate application suitable for text search. This process is done by giving a pattern to the text. Therefore, the final result of string matching text search using the Boyer-Moore algorithm technique requires nine iterations. In the 9th iteration, the text and pattern conditions are matched or sequential. From the results of the manual computational search analysis work of applying the Boyer Moore string matching algorithm, several stages of the process are made, namely iterations 1 to 9, as a search step to determine string matches. In addition, patterns can be used with the number of shifts of patterns or text up to 13 times.

Functional annotation of a divergent genome using sequence and structure-based similarity

BackgroundMicrosporidia are a large taxon of intracellular pathogens characterized by extraordinarily streamlined genomes with unusually high sequence divergence and many species-specific adaptations. These unique factors pose challenges for traditional genome annotation methods based on sequence similarity. As a result, many of the microsporidian genomes sequenced to date contain numerous genes of unknown function. Recent innovations in rapid and accurate structure prediction and comparison, together with the growing amount of data in structural databases, provide new opportunities to assist in the functional annotation of newly sequenced genomes.ResultsIn this study, we established a workflow that combines sequence and structure-based functional gene annotation approaches employing a ChimeraX plugin named ANNOTEX (Annotation Extension for ChimeraX), allowing for visual inspection and manual curation. We employed this workflow on a high-quality telomere-to-telomere sequenced tetraploid genome of Vairimorpha necatrix. First, the 3080 predicted protein-coding DNA sequences, of which 89% were confirmed with RNA sequencing data, were used as input. Next, ColabFold was used to create protein structure predictions, followed by a Foldseek search for structural matching to the PDB and AlphaFold databases. The subsequent manual curation, using sequence and structure-based hits, increased the accuracy and quality of the functional genome annotation compared to results using only traditional annotation tools. Our workflow resulted in a comprehensive description of the V. necatrix genome, along with a structural summary of the most prevalent protein groups, such as the ricin B lectin family. In addition, and to test our tool, we identified the functions of several previously uncharacterized Encephalitozoon cuniculi genes.ConclusionWe provide a new functional annotation tool for divergent organisms and employ it on a newly sequenced, high-quality microsporidian genome to shed light on this uncharacterized intracellular pathogen of Lepidoptera. The addition of a structure-based annotation approach can serve as a valuable template for studying other microsporidian or similarly divergent species.

Natural Language Processing for Financial Regulation

This article provides an understanding of Natural Language Processing techniques in the framework of financial regulation, more specifically in order to perform semantic matching search between rules and policy when no dataset is available for supervised learning. We outline how to outperform simple pre-trained sentences-transformer models using freely available resources and explain the mathematical concepts behind the key building blocks of Natural Language Processing.

UniCarb-DB: An MS/MS Experimental Glycomic Fragmentation Database.

Glycosylation is a unique posttranslational modification that dynamically shapes the surface of cells. Glycans attached to proteins or lipids in a cell or tissue are studied as a whole and collectively designated as a glycome. UniCarb-DB is a glycomic spectral library of tandem mass spectrometry (MS/MS) fragment data. The current version of the database consists of over 1500 entries and over 1000 unique structures. Each entry contains parent ion information with associated MS/MS spectra, metadata about the original publication, experimental conditions, and biological origin. Each structure is also associated with the GlyTouCan glycan structure repository allowing easy access to other glycomic resources. The database can be directly utilized by mass spectrometry (MS) experimentalists through the conversion of data generated by MS into structural information. Flexible online search tools along with a downloadable version of the database are easily incorporated in either commercial or open-access MS software. This chapter highlights UniCarb-DB online search tool to browse differences of isomeric structures between spectra, a peak matching search between user-generated MS/MS spectra and spectra stored in UniCarb-DB and more advanced MS tools for combined quantitative and qualitative glycomics.

The concept of “the Lamb of God” («ὁ ἀµνὸς τοῦ θεοῦ») in English, German and Russian translations of the Gospel of John

The interpretation of biblical concepts is a complex translation problem, the method of literal translation cannot solve it. The search for lexical matches in the target language is not always systematic. A comprehensive solution to this problem is possible within the framework of the conceptual theory of metaphor, which allows you to rethink the process of translating biblical concepts. The article explores the implementation of the concept «the Lamb of God» («ὁ ἀµνὸς τοῦ θεοῦ») in the translations of the Gospel of John into English, German and Russian in the light of modern research on the conceptual theory of metaphor. The purpose of the study is to analyze the degree of implementation of the concept «the Lamb of God» («ὁ ἀµνὸς τοῦ θεοῦ») in translations of the Gospel of John. The work uses the MIPVU method to identify metaphors in the original text of the Gospel of John, as well as methods of analysis and synthesis to assess the adequacy of the translation of the concept « the Lamb of God» in the context of the biblical conceptual field. As a result of the study, it was revealed that in the English, German and Russian translations of the Gospel of John, the concept of the «the Lamb of God» («ὁ ἀμνὸς τοῦ θεοῦ») of the original text is not fully implemented. The absence of a separate lexical unit leads to confusion in the translations of the «Lamb» («ἀμνὸς») from the Gospel of John and the «Lamb» («ἀρνίον») from the Apocalypse, which distorts the meaning of the translations. The application of the MIPVU method to identify metaphors in the biblical text, as well as an integrated approach to the translation of biblical concepts, is a developing direction in modern translation studies. Modern advances in the field of metaphor theory make it possible to create new translations of the Bible that are accessible to readers who are not familiar with the biblical conceptual field.

Using Genetic Algorithm for DNA Profile Matching

The DNA is used in forensic investigations to identify suspects and victims at crime scenes. However, manual matching of DNA profiles is difficult and error-prone, especially in large databases. In Iraq, technology for DNA matching is limited, making manual matching the only option. Regenerate. In this work, we propose a Genetic Algorithm (GA) for DNA dataset matching to provide simple and user-friendly software to be used by law enforcement agencies in Iraq. The genetic algorithm is a type of heuristic search method used in computing science and artificial intelligence. It is based on the theory of natural selection and evolutionary biology and is used to find the best solutions to search problems. Genetic algorithm is robust for searching through big, complicated datasets. Thus, in this paper, the GA is the algorithm of choice to achieve the goal of DNA matching search. The used dataset is actual data that have been collected from the Ministry of Interior at the Basra Investigation Center. Finally, the python simulation results show 100% accuracy where the proposed method managed to find the DNAs under consideration precisely.

Virus impacted community adaptation in oligotrophic groundwater environment revealed by Hi-C coupled metagenomic and viromic study

Viruses play a crucial role in microbial mortality, diversity and biogeochemical cycles. Groundwater is the largest global freshwater and one of the most oligotrophic aquatic systems on Earth, but how microbial and viral communities are shaped in this special habitat is largely unexplored. In this study, we collected groundwater samples from 23 to 60 m aquifers at Yinchuan Plain, China. In total, 1920 non-reductant viral contigs were retrieved from metagenomes and viromes constructed by Illumina and Nanopore hybrid sequencing. Only 3% of them could be clustered with known viruses, most of which were Caudoviricetes. Coupling 1.2 Tb Hi-C sequencing with CRISPR matching and homology search, we connected 469 viruses with their hosts while some viral clusters presented a broad-host-range trait. Meanwhile, a large proportion of biosynthesis related auxiliary metabolism genes were identified. Those characteristics might benefit viruses for a better survival in this special oligotrophic environment. Additionally, the groundwater virome showed genomic features distinct from those of the open ocean and wastewater treatment facilities in GC distribution and unannotated gene compositions. This paper expands the current knowledge of the global viromic records and serves as a foundation for a more thorough understanding of viruses in groundwater.

Stress Detection Based on social media Blogs

Major psychological health issues like stress, anxiety, etc. are a direct outcome of technology development and a huge increase in social media usage. It is possible to examine these problems and develop prevention plans. As a result of people's great desire to support SMEs, social media monitoring of blogs is essential to resolving these serious issues (Social Media Environments). Psychologists used conventional techniques like surveys and interviews, but these procedures are time-consuming and hysterical. In this paper, we reviewed a variety of stress detection techniques and discovered that they were unsuccessful for identifying stress in social media. In this study, we presented a method for effectively detecting stress in people by utilising the ontology. Using the ontology for stress detection among people and recommending the essential safety measures to stop users from committing suicide is what we presented as an effective stress detection approach in this study. The term matching search method known as ontology is utilised in social media to more accurately detect communications shared by users that are connected to stress.

Abstract 1067: MatchMiner: An open-source AI precision medicine trial matching platform

Abstract As the number of precision medicine (PM) trials and patient genomic data has grown, it has become challenging for clinicians and trial staff to identify PM trial options for patients. Several trial matching software platforms have been developed to match genomic data from patients with PM trials, but these existing platforms are proprietary and are not easily accessible for adoption by institutions. At Dana-Farber Cancer Institute (DFCI), we have addressed this challenge by developing our own open-source institutional trial matching software, MatchMiner. MatchMiner algorithmically matches patient genomic and clinical data with PM trial eligibility data. Trial eligibility data is manually curated into a human-readable markup language, called clinical trial markup language (CTML), for matching with patient genomic data. MatchMiner has 2 main modes of clinical use: (1) patient-centric, where clinicians search for trial matches for individual patients and (2) trial-centric, where trial staff identify patients that match their trial’s genomic eligibility. We recently described MatchMiner’s usage at DFCI and since our report, we have added 90 additional trial consents facilitated by MatchMiner (>250 trial consents, called MatchMiner consents [MMC]). Here, we describe new characteristics of our MMC including which user mode (patient-centric or trial-centric) was used to match the consent, genomic alterations and cancer types that matched to eligibility criteria, and whether the patient went onto trial. MMCs were mostly identified by patient-centric mode (70%), genomic alterations and cancer types among MMC were diverse (n=55 genes and n=20 cancer types), and 87% of MMC went on trial. Among MMCs, the most common altered genes leading to trial eligibility were ERBB2 and KRAS in breast cancer and lung cancer, which is consistent with the number of therapies targeting ERBB2 and KRAS. MMCs also included patients with rare cancer types, like extraskeletal myxoid chondrosarcoma, as well as rare genomic alterations, such as NTRK fusions. Thus, MatchMiner has been successful at facilitating PM trial matching for a broad range of genomic alterations and cancer types at DFCI. MatchMiner matches patients to trials as soon as their genomic report is available, however, many patients are not yet ready to enroll onto a trial because their cancer is responding to the standard of care or they are in a remission period. To address this problem, we are evaluating the use of artificial intelligence (AI) to identify patients that may be ready for a new treatment option. After trial matches have been generated by MatchMiner, radiology scan text from patients’ tumor scans is run through a natural language processing (NLP) model to identify patients who are more likely to be ready to enroll onto a trial. By using NLP to filter trial matches, we hope to improve MatchMiner’s efficiency of finding trial matches and provide more timely trial options for patients. Citation Format: Harry Klein, Tali Mazor, Matthew Galvin, Jason Hansel, Emily Mallaber, Pavel Trukhanov, Joyce Yu, James Lindsay, Kenneth Kehl, Michael Hassett, Ethan Cerami. MatchMiner: An open-source AI precision medicine trial matching platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1067.

An INS/Geomagnetic Integrated Navigation Method for Coarse Estimation of Positioning Error and Search Area Adaption Applied to High-Speed Aircraft

The geomagnet aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft navigation. It can overcome the shortcomings of pure inertial navigation error accumulation over time and achieve long-term, autonomous, and all-weather navigation. The traditional iterative closest contour point (ICCP) algorithm, however, requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this article proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the aircraft flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this article can reach within 200 m.

Hypoxia Pathway in Osteoporosis: Laboratory Data for Clinical Prospects.

The hypoxia pathway not only regulates the organism to adapt to the special environment, such as short-term hypoxia in the plateau under normal physiological conditions, but also plays an important role in the occurrence and development of various diseases such as cancer, cardiovascular diseases, osteoporosis. Bone, as a special organ of the body, is in a relatively low oxygen environment, in which the expression of hypoxia-inducible factor (HIF)-related molecules maintains the necessary conditions for bone development. Osteoporosis disease with iron overload endangers individuals, families and society, and bone homeostasis disorder is linked to some extent with hypoxia pathway abnormality, so it is urgent to clarify the hypoxia pathway in osteoporosis to guide clinical medication efficiently. Based on this background, using the keywords "hypoxia/HIF, osteoporosis, osteoblasts, osteoclasts, osteocytes, iron/iron metabolism", a matching search was carried out through the Pubmed and Web Of Science databases, then the papers related to this review were screened, summarized and sorted. This review summarizes the relationship and regulation between the hypoxia pathway and osteoporosis (also including osteoblasts, osteoclasts, osteocytes) by arranging the references on the latest research progress, introduces briefly the application of hyperbaric oxygen therapy in osteoporosis symptoms (mechanical stimulation induces skeletal response to hypoxic signal activation), hypoxic-related drugs used in iron accumulation/osteoporosis model study, and also puts forward the prospects of future research.

Efficient [formula omitted]-closest entity matching over heterogeneous information networks

This work investigates a novel m-closest entity (mCE) matching problem over geographic heterogeneous information networks (Geo-HINs). That is, given a Geo-HIN G and m query graphs {q1,q2,…,qm}, mCE matching aims to find a group of geographic entities (geo-entities) whose patterns match the query graphs {q1,q2,…,qm} correspondingly, for which the maximum distance between any geo-entity pair (i.e., the diameter) in the group is minimized. As a fundamental problem, the mCE matching can be applied for many scenarios, e.g., travel itinerary recommendation and city planning. The existing works have not simultaneously considered the characteristics of patterns matching and spatial search so that they cannot solve our problem, which is computationally expensive. To solve this problem efficiently, we propose a unified framework named Fuzzy−Exact framework to process entity matching and spatial search comprehensively, in which pruning abilities at non-spatial and spatial layers are cooperatively explored. Two mutually adaptive auxiliary data structures named Arc−Tree and Arc−Forest are devised to maintain the intermediate search results which are exploited to enhance the search process between non-spatial and spatial layers. Experimental results demonstrate that our algorithm can outperform the baseline methods by 2 orders of magnitude on runtime.

Optimal Transport-Based Patch Matching for Image Style Transfer

State-of-the-art image style transfer methods have achieved impressive results by using neural networks. However, neural style transfer (NST) methods either ignore the local details of the style image by using the global statistics for style modeling or cannot fully use shallow features of neural networks, leading to the synthesized image having fewer details. In this study, we proposed a new patch-based style transfer method that directly operates in the image pixel domain without using any neural networks, achieving fascinating style transfer results with rich image details. The proposed method was derived from classic texture synthesis methods. Most previous methods rely on nearest neighbor search (NNS) for patch matching. However, this greedy strategy cannot guarantee the similarity of patch distributions between the synthesized image and the style image, which limits the expressiveness of textures. We solved this problem by proposing an optimal patch matching algorithm formed on the Optimal Transport (OT) theory, which theoretically guarantees the similarity of the patch distributions and gives a flexible style modeling method. Various qualitative and quantitative experiments demonstrated that the proposed method achieves better synthesized results than state-of-the-art style transfer methods, including NST and classic methods based on texture synthesis.

Finding the presence of borrowings in scientific works based on Markov chains

The study aims to develop optimal approaches to the search for borrowings in scientific works. The article discusses the stages of searching for the presence of borrowings, such as preprocessing, rough filtering of texts, searching for similar texts, and searching for borrowings. The main focus is on the description of approaches and techniques that can be effectively implemented at each stage. For example, for the preprocessing stage, it may be converting text characters from uppercase to lowercase, removing punctuation marks, and removing stop words. For the stage of rough text filtering, it is filters by topic and word frequency. It may be calculating the importance of words in the context of the text and representing the word as a vector in multidimensional space to determine the proximity measure for the stage of finding similar texts. Finally, it is a search for an exact match, paraphrases and a measure of similarity of expressions for the stage of finding borrowings. The scientific novelty lies in using Markov chains to find the similarity of texts for the second and third stages of the search for borrowings proposed by authors. As a result, the example shows the technique of using Markov chains for text representation, searching for the most frequently occurring words, building a graph of a Markov chain of words, and the prospects for using Markov chains of texts for rough filtering and searching for similar texts.

Diverse viruses of marine archaea discovered using metagenomics.

During the past decade, metagenomics became a method of choice for the discovery of novel viruses. However, host assignment for uncultured viruses remains challenging, especially for archaeal viruses, which are grossly undersampled compared to viruses of bacteria and eukaryotes. Here, we assessed the utility of CRISPR spacer targeting, tRNA gene matching and homology searches for viral signature proteins, such as major capsid proteins, for the assignment of archaeal hosts and validated these approaches on metaviromes from Yangshan Harbor (YSH). We report 35 new genomes of viruses which could be confidently assigned to hosts representing diverse lineages of marine archaea. We show that the archaeal YSH virome is highly diverse, with some viruses enriching the previously described virus groups, such as magroviruses of Marine Group II Archaea (Poseidoniales), and others representing novel groups of marine archaeal viruses. Metagenomic recruitment of Tara Oceans datasets on the YSH viral genomes demonstrated the presence of YSH Poseidoniales and Nitrososphaeria viruses in the global oceans, but also revealed the endemic YSH-specific viral lineages. Furthermore, our results highlight the relationship between the soil and marine thaumarchaeal viruses. We propose three new families within the class Caudoviricetes for the classification of the five complete viral genomes predicted to replicate in marine Poseidoniales and Nitrososphaeria, two ecologically important and widespread archaeal groups. This study illustrates the utility of viral metagenomics in exploring the archaeal virome and provides new insights into the diversity, distribution and evolution of marine archaeal viruses.

PELATIHAN PEMBUATAN WORKSHEET INTERAKTIF DENGAN WIZER.ME UNTUK MENGOPTIMALKAN PEMBELAJARAN DI SD NEGERI 26 MATARAM

As an effort to improve teacher competence in developing interactive and interesting learning resources, training on making interactive worksheets was carried out with wizer.me. Wizer.me has full features for making online assignments. In terms of appearance, this platform is dynamic but simple. There are several features provided by wizer.me including community, worksheets, create new worksheets, learners, and coffee room. This community service activity is divided into three stages, namely the preparation stage, the implementation stage, and the evaluation stage. This service is targeted at the teachers of SDN 26 Mataram, totaling 14 people. The training was conducive because 14 participants were guided by 5 members of the service team so that if there were difficulties, they could be resolved immediately. Participants were also enthusiastic about participating in the training because the presenters presented in a communicative language and guided step by step while making sure the participants had gone through each step well. The Service Team has also prepared emails that can be used as well as questions so that participants only need to input them. Some participants were also active in asking if there was something they didn't understand. Given the limited time, there are only 5 types of questions that can be practiced by participants, including multiple-choice, blank, matching, fill-on image, and word search puzzles.

Enhancing Administrative Claims Data: Feasibility, Validation and Application of Linking Medicare Claims Data and National Marrow Donor Program Search Data.

Administrative claims data provide real-world service utilization of acute myeloid leukemia (AML) treatment, but lacks insight into treatment delays or barriers. The National Marrow Donor Program (NMDP)/Be The Match Search (Search) data contains information on donor search, but lacks information on treatment received if allogeneic hematopoietic cell transplant (HCT) is not performed. We hypothesized that linking these two data sets would create a rich resource to define factors associated with receiving HCT that could not be evaluated with either data set alone. A subset of 2010-2016 Medicare administrative claims data was linked with Search data. A total of 5,351 patients with AML age 65-74 years (HCT = 607, no HCT = 4,744) were identified using Medicare. These patients were then linked to 93,800 records with a donor search between 2009 and 2016. Patient date of birth, sex, disease, ZIP code, transplant center/hospital, and diagnosis date were used for matching. Exploratory analysis was conducted to identify predictors associated with receiving HCT for patients with AML who received a search. The data sets were successfully linked, showing high sensitivity and specificity. The final cohort included 5,085 patients with AML (HCT = 533, no HCT = 4,552). Of 97 patients who received HCT without a matched search, more than 85% received a related donor HCT. Of those not receiving HCT, 609 had a matched NMDP search and 3,943 did not have a matched NMDP search. Multivariate analysis showed time to search, age, diagnosis year, race/ethnicity, and neighborhood education status associated with receiving HCT. Methods herein demonstrate the feasibility of linking Search and Medicare data. Similar methods may be applied to answer critical questions regarding barriers to HCT, thereby identifying areas to improve access to care.