Visual Exploration and Knowledge Discovery from Biomedical Dark Data

• URL: http://arxiv.org/abs/2009.13059v1
• Date: Mon, 28 Sep 2020 04:27:05 GMT
• Title: Visual Exploration and Knowledge Discovery from Biomedical Dark Data
• Authors: Shashwat Aggarwal, Ramesh Singh
• Abstract summary: We employ a natural language processing based pipeline to discover knowledge out of the biomedical dark data. We aim to proffer a potential solution to overcome the problem of analyzing overwhelming amounts of information.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data visualization techniques proffer efficient means to organize and present data in graphically appealing formats, which not only speeds up the process of decision making and pattern recognition but also enables decision-makers to fully understand data insights and make informed decisions. Over time, with the rise in technological and computational resources, there has been an exponential increase in the world's scientific knowledge. However, most of it lacks structure and cannot be easily categorized and imported into regular databases. This type of data is often termed as Dark Data. Data visualization techniques provide a promising solution to explore such data by allowing quick comprehension of information, the discovery of emerging trends, identification of relationships and patterns, etc. In this empirical research study, we use the rich corpus of PubMed comprising of more than 30 million citations from biomedical literature to visually explore and understand the underlying key-insights using various information visualization techniques. We employ a natural language processing based pipeline to discover knowledge out of the biomedical dark data. The pipeline comprises of different lexical analysis techniques like Topic Modeling to extract inherent topics and major focus areas, Network Graphs to study the relationships between various entities like scientific documents and journals, researchers, and, keywords and terms, etc. With this analytical research, we aim to proffer a potential solution to overcome the problem of analyzing overwhelming amounts of information and diminish the limitation of human cognition and perception in handling and examining such large volumes of data.

Related papers

• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Enhancing Biomedical Lay Summarisation with External Knowledge Graphs [28.956500948255677]
  We investigate the effectiveness of three different approaches for incorporating knowledge graphs within lay summarisation models. Our results confirm that integrating graph-based domain knowledge can significantly benefit lay summarisation by substantially increasing the readability of generated text.
  arXiv  Detail & Related papers  (2023-10-24T10:25:21Z)
• Multimodal Deep Learning for Scientific Imaging Interpretation [0.0]
  This study presents a novel methodology to linguistically emulate and evaluate human-like interactions with Scanning Electron Microscopy (SEM) images. Our approach distills insights from both textual and visual data harvested from peer-reviewed articles. Our model (GlassLLaVA) excels in crafting accurate interpretations, identifying key features, and detecting defects in previously unseen SEM images.
  arXiv  Detail & Related papers  (2023-09-21T20:09:22Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [36.58189530598098]
  It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data. graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system.
  arXiv  Detail & Related papers  (2021-05-27T13:32:45Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• A Literature Review of Recent Graph Embedding Techniques for Biomedical Data [36.446560017794845]
  Many graph-based learning methods have been proposed to analyze such type of data. The main difficulty is how to handle high dimensionality and sparsity of the biomedical graphs. graph embedding methods provide an effective and efficient way to address the above issues.
  arXiv  Detail & Related papers  (2021-01-17T01:53:50Z)
• Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain [1.9004296236396943]
  We present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools. We generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain.
  arXiv  Detail & Related papers  (2020-10-28T08:31:40Z)
• A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [77.34726150561087]
  We survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.
  arXiv  Detail & Related papers  (2020-10-26T16:08:13Z)
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications [89.78089494738002]
  We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
  arXiv  Detail & Related papers  (2020-02-02T13:17:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.