Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook

• URL: http://arxiv.org/abs/2310.03086v1
• Date: Mon, 2 Oct 2023 07:53:05 GMT
• Title: Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook
• Authors: Suresh Kumar, Dhanyashri Guruparan, Pavithren Aaron, Philemon Telajan, Kavinesh Mahadevan, Dinesh Davagandhi, Ong Xin Yue
• Abstract summary: We examine the history, advantages, and challenges of deep learning in computational biology. Our focus is on two primary applications: DNA sequence classification and prediction, as well as protein structure prediction from sequence data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning has become a powerful tool in computational biology, revolutionising the analysis and interpretation of biological data over time. In our article review, we delve into various aspects of deep learning in computational biology. Specifically, we examine its history, advantages, and challenges. Our focus is on two primary applications: DNA sequence classification and prediction, as well as protein structure prediction from sequence data. Additionally, we provide insights into the outlook for this field. To fully harness the potential of deep learning in computational biology, it is crucial to address the challenges that come with it. These challenges include the requirement for large, labelled datasets and the interpretability of deep learning models. The use of deep learning in the analysis of DNA sequences has brought about a significant transformation in the detection of genomic variants and the analysis of gene expression. This has greatly contributed to the advancement of personalised medicine and drug discovery. Convolutional neural networks (CNNs) have been shown to be highly accurate in predicting genetic variations and gene expression levels. Deep learning techniques are used for analysing epigenetic data, including DNA methylation and histone modifications. This provides valuable insights into metabolic conditions and gene regulation. The field of protein structure prediction has been significantly impacted by deep learning, which has enabled accurate determination of the three-dimensional shape of proteins and prediction of their interactions. The future of deep learning in computational biology looks promising. With the development of advanced deep learning models and interpretation techniques, there is potential to overcome current challenges and further our understanding of biological systems.

Related papers

• A Review of Artificial Intelligence based Biological-Tree Construction: Priorities, Methods, Applications and Trends [43.12448177569722]
  Biological tree analysis serves as a pivotal tool in uncovering the evolutionary and differentiation relationships among organisms, genes, and cells. Traditional tree inference methods, while foundational in early studies, face increasing limitations in processing the large-scale, complex datasets. Recent advances in deep learning offer promising solutions, providing enhanced data processing and pattern recognition capabilities.
  arXiv  Detail & Related papers  (2024-10-07T08:00:41Z)
• VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling [60.91599380893732]
  VQDNA is a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning. By leveraging vector-quantized codebooks as learnable vocabulary, VQDNA can adaptively tokenize genomes into pattern-aware embeddings.
  arXiv  Detail & Related papers  (2024-05-13T20:15:03Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Phylogeny-informed fitness estimation [58.720142291102135]
  We propose phylogeny-informed fitness estimation, which exploits a population's phylogeny to estimate fitness evaluations. Our results indicate that phylogeny-informed fitness estimation can mitigate the drawbacks of down-sampled lexicase. This work serves as an initial step toward improving evolutionary algorithms by exploiting runtime phylogenetic analysis.
  arXiv  Detail & Related papers  (2023-06-06T19:05:01Z)
• Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction [2.838553480267889]
  We construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction. KDGene introduces an interaction module between the embeddings of entities and relations to tensor decomposition, which can effectively enhance the information interaction in biological knowledge.
  arXiv  Detail & Related papers  (2023-02-18T13:57:44Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• A Review of Deep Learning Techniques for Protein Function Prediction [0.0]
  This review paper analyzes the recent developments in approaches for the task of predicting protein function using deep learning. We highlight the emergence of the modern State of The Art (SOTA) deep learning models which have achieved groundbreaking results in the field of computer vision, natural language processing and multi-modal learning.
  arXiv  Detail & Related papers  (2022-10-27T20:30:25Z)
• Application of Machine Learning in understanding plant virus pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus interplay and management [1.949912057689623]
  Deep learning algorithms further promote the application of machine learning in several field of biology including plant virology. Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning.
  arXiv  Detail & Related papers  (2021-12-03T16:25:26Z)
• Modeling Protein Using Large-scale Pretrain Language Model [12.568452480689578]
  Interdisciplinary researchers have begun to leverage deep learning methods to model large biological datasets. Inspired by the similarity between natural language and protein sequences, we use large-scale language models to model evolutionary-scale protein sequences. Our model can accurately capture evolution information from pretraining on evolutionary-scale individual sequences.
  arXiv  Detail & Related papers  (2021-08-17T04:13:11Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)

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.