Towards Structured Prediction in Bioinformatics with Deep Learning

• URL: http://arxiv.org/abs/2008.11546v1
• Date: Tue, 25 Aug 2020 02:52:18 GMT
• Title: Towards Structured Prediction in Bioinformatics with Deep Learning
• Authors: Yu Li
• Abstract summary: In bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformatics. We demonstrate our ideas with six projects from four bioinformatics subfields.
• Score: 11.055292483959414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classification or regression problems, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classification but has much broader applications, considering that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-specific constraints and dependency within the labeling space, the straightforward application of existing deep learning models can lead to unsatisfactory results. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design the problem-specific deep learning architectures or methods by considering the structured labeling space and problem constraints, either explicitly or implicitly. We demonstrate our ideas with six projects from four bioinformatics subfields, including sequencing analysis, structure prediction, function annotation, and network analysis. The structured outputs cover 1D signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. With the help of the above ideas, all of our methods can achieve SOTA performance on the corresponding problems. The success of these projects motivates us to extend our work towards other more challenging but important problems, such as health-care problems, which can directly benefit people's health and wellness.

Related papers

• Learning to refine domain knowledge for biological network inference [2.209921757303168]
  Perturbation experiments allow biologists to discover causal relationships between variables of interest. The sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. We propose an amortized algorithm for refining domain knowledge, based on data observations.
  arXiv  Detail & Related papers  (2024-10-18T12:53:23Z)
• Learning Representations for Reasoning: Generalizing Across Diverse Structures [5.031093893882575]
  We aim to push the boundary of reasoning models by devising algorithms that generalize across knowledge and query structures. Our library treats structured data as first-class citizens and removes the barrier for developing algorithms on structured data.
  arXiv  Detail & Related papers  (2024-10-16T20:23:37Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations [1.9580473532948401]
  This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process. We ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality. Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models.
  arXiv  Detail & Related papers  (2023-10-24T19:50:41Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [50.22269760171131]
  The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. This text is concerned with exposing pre-defined regularities through unified geometric principles. It provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers.
  arXiv  Detail & Related papers  (2021-04-27T21:09:51Z)
• Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond [114.39616146985001]
  In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of Bi-Level Optimization (BLO) Then we construct a value-function-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies.
  arXiv  Detail & Related papers  (2021-01-27T16:20:23Z)
• Structure preserving deep learning [1.2263454117570958]
  deep learning has risen to the foreground as a topic of massive interest. There are multiple challenging mathematical problems involved in applying deep learning. A growing effort to mathematically understand the structure in existing deep learning methods.
  arXiv  Detail & Related papers  (2020-06-05T10:59:09Z)

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.