4 research outputs found
Predicting Breast Cancer with Machine Learning
No full textAlthough early diagnosis is essential for maximizing survival rates of breast cancer patients, conventional diagnostic techniques like biopsies and mammograms are invasive and expensive. This study explores machine learning techniques to identify breast tumors as benign or malignant. The Wisconsin Breast Cancer database has been used to testify the methods.Biomedical and Health InformaticsWang, Xiaolian
Fine-grained entity typing system - design and analysis
No full textNamed entity recognition (NER) is a natural language processing (NLP) task that involves identifying mentions (spans of text) denoting entities in a given text document and assigning them a semantic category/type from a given taxonomy. It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01The student, Pavankumar Reddy Muddireddy, accepted the attached license on 2018-04-23 at 17:30.The student, Pavankumar Reddy Muddireddy, submitted this Thesis for approval on 2018-04-23 at 17:42.This Thesis was approved for publication on 2018-04-24 at 09:20.DSpace SAF Submission Ingestion Package generated from Vireo submission #12436 on 2018-08-31 at 17:21:20Made available in DSpace on 2018-09-04T20:36:52Z (GMT). No. of bitstreams: 2
MUDDIREDDY-THESIS-2018.pdf: 446805 bytes, checksum: 30dc454cac28cfff02a4762ba75d774a (MD5)
LICENSE.txt: 4224 bytes, checksum: b8203e25ef7f7ebfd07f9694a6abae27 (MD5)
Previous issue date: 2018-04-24Embargo set by: Seth Robbins for item 107298
Lift date: 2020-09-04T20:37:00Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 107298
Lift date: 2020-09-04T20:42:08Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 107298 on 2020-09-05T09:15:29Z
The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy
No full textRNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. Availability: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench.© The Author(s) 201
