Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and not able to handle a large number of datasets with thousands of experiments. Parallel processing can be used to address the above-mentioned issues. Hadoop is a well-known and ideal distributed file system framework that provides a parallel environment to run the experiment. In this research, for the first time, the capability of Hadoop and statistical power of R have been leveraged to parallelize the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on cluster containing 5 nodes, while each node has 16 cores and 16 GB memory. It compares efficiency and the performance of parallelized RMA using Hadoop with parallelized RMA using affyPara package as well as sequential RMA. The result shows the speed-up rate of the proposed approach outperforms the sequential approach and affyPara approach.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896349 | PMC |
| http://dx.doi.org/10.1155/2018/9391635 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Federated Collaborative Learning with Sparse Gradients for Heterogeneous Data on Resource-Constrained Devices.
Entropy (Basel)
December 2024
Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng 475001, China.
Federated learning enables devices to train models collaboratively while protecting data privacy. However, the computing power, memory, and communication capabilities of IoT devices are limited, making it difficult to train large-scale models on these devices. To train large models on resource-constrained devices, federated split learning allows for parallel training of multiple devices by dividing the model into different devices.
View Article and Find Full Text PDFState-of-the-Art Trends in Data Compression: COMPROMISE Case Study.
Entropy (Basel)
November 2024
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia.
After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight.
View Article and Find Full Text PDFMIC: Breast Cancer Multi-label Diagnostic Framework Based on Multi-modal Fusion Interaction.
J Imaging Inform Med
January 2025
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
The automated diagnosis of low-resolution and difficult-to-recognize breast ultrasound images through multi-modal fusion holds significant clinical value. However, prevailing fusion methods predominantly rely on image modalities, neglecting the textual pathology information, and only benign and malignant diagnosis of breast tumors is not satisfying for clinical applications. Consequently, this paper proposes a novel multi-modal fusion interactive diagnostic framework, termed the MIC framework, to achieve the multi-label classification of breast cancer, namely benign-malignant classification and breast imaging reporting and data system (BI-RADS) 3, 4a, 4b, 4c, and 5 gradings.
View Article and Find Full Text PDFJapanese waka translation supported by internet of things and artificial intelligence technology.
Sci Rep
January 2025
School of Foreign Languages, Quanzhou Normal University, Quanzhou, 362000, Fujian, China.
With the advancement of internet of things (IoT) and artificial intelligence (AI) technology, access to large-scale bilingual parallel data has become more efficient, thereby accelerating the development and application of machine translation. Given the increasing cultural exchanges between China and Japan, many scholars have begun to study the Chinese translation of Japanese waka poetry. Based on this, the study first explores the structure of waka and the current state of its Chinese translations, analyzing existing translation disputes and introducing a data collection method for waka using IoT.
View Article and Find Full Text PDFVolumetric measurement of manually drawn segmentations in cone beam computed tomography images of newly formed bone after sinus floor augmentation with bovine-derived bone substitutes.
J Stomatol Oral Maxillofac Surg
January 2025
Center for Oral and Maxillofacial Surgery, Faculty of Medicine/Dental Medicine, Danube Private University, Krems, Austria. Electronic address:
Precise volumetric measurement of newly formed bone after maxillary sinus floor augmentation (MSFA) can help clinicians in planning for dental implants. This study aimed to introduce a novel modular framework to facilitate volumetric calculations based on manually drawn segmentations of user-defined areas of interest on cone-beam computed tomography (CBCT) images MATERIAL & METHODS: Two interconnected networks for manual segmentation of a defined volume of interest and dental implant volume calculation, respectively, were used in parallel. The volume data of dental implant manufacturers were used for reference.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!