Deep Learning in Mining Biological Data

• URL: http://arxiv.org/abs/2003.00108v1
• Date: Fri, 28 Feb 2020 23:14:27 GMT
• Title: Deep Learning in Mining Biological Data
• Authors: Mufti Mahmud, M Shamim Kaiser, Amir Hussain
• Abstract summary: Deep learning (DL) has been successfully applied to solve many complex pattern recognition problems. This article provides applications of DL to biological sequences, images, and signals data. It also outlines some open research challenges in mining biological data.
• Score: 7.834172629639729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in three types (i.e., sequences, images, and signals), these data are huge in amount and complex in nature. Mining such an enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. Highlighting the role of DL in recognizing patterns in biological data, this article provides - applications of DL to biological sequences, images, and signals data; overview of open access sources of these data; description of open source DL tools applicable on these data; and comparison of these tools from qualitative and quantitative perspectives. At the end, it outlines some open research challenges in mining biological data and puts forward a number of possible future perspectives.

Related papers

• EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications [0.2826977330147589]
  We propose a web-based end-to-end pipeline that is capable of preprocessing, training, evaluating, and visualizing machine learning models. Our library assists in recognizing, classifying, clustering, and predicting a wide range of multi-modal, multi-sensor datasets.
  arXiv  Detail & Related papers  (2024-03-27T02:24:38Z)
• Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery [47.28191501836041]
  In this paper, we employ a Reinforcement Learning framework to simulate the cognitive processes of humans. We also deploy a Member-to-Leader Multi-Agent framework to extract and fuse features from multi-modal information. We demonstrate the performance of our approach in both the 3D and 2D domains by employing the OS-MN40, OS-MN40-Miss, and Cifar10 datasets.
  arXiv  Detail & Related papers  (2023-08-26T07:55:32Z)
• Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization [3.6393183544320236]
  Speech recognition has become an important challenge when using deep learning (DL) It requires large-scale training datasets and high computational and storage resources. Deep transfer learning (DTL) has been introduced to overcome these issues.
  arXiv  Detail & Related papers  (2023-04-27T21:08:05Z)
• Deep Learning in Healthcare: An In-Depth Analysis [1.892561703051693]
  We provide a review of Deep Learning models and their broad application in bioinformatics and healthcare. We also go over some of the key challenges that still exist and can show up while conducting DL research.
  arXiv  Detail & Related papers  (2023-02-12T20:55:34Z)
• Systems for Parallel and Distributed Large-Model Deep Learning Training [7.106986689736828]
  Some recent Transformer models span hundreds of billions of learnable parameters. These designs have introduced new scale-driven systems challenges for the DL space. This survey will explore the large-model training systems landscape, highlighting key challenges and the various techniques that have been used to address them.
  arXiv  Detail & Related papers  (2023-01-06T19:17:29Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• 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)
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review [33.40031994803646]
  This survey aims to present a systematic overview in DL-based multimodal RS data fusion. Sub-fields in the multimodal RS data fusion are reviewed in terms of to-be-fused data modalities. The remaining challenges and potential future directions are highlighted.
  arXiv  Detail & Related papers  (2022-05-03T09:08:16Z)
• Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods [96.92041573661407]
  Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Many large-scale datasets are highly sensitive, such as healthcare data, and are not widely available to the machine learning community. Generating synthetic data with privacy guarantees provides one such solution.
  arXiv  Detail & Related papers  (2020-12-08T17:26:10Z)
• Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review [68.15472610671748]
  Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
  arXiv  Detail & Related papers  (2020-05-23T20:57:12Z)
• Deep Learning for Community Detection: Progress, Challenges and Opportunities [79.26787486888549]
  Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks. This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
  arXiv  Detail & Related papers  (2020-05-17T11:22:11Z)

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.