In this post, we examine applications of deep learning to three key biomedical problems:   patient classification, fundamental biological processes, and treatment of patients. The objective is to predict whether deep learning will transform these tasks.

The post is based on the very comprehensive paper “Opportunities and obstacles for deep learning in biology and medicine”.

The paper places a high bar i.e. on the lines of Andy Grove’s inflection point to refer to a change in technologies or environment that requires a business to be fundamentally reshaped.

The three classes of applications are described as follows:

Disease and patient categorization: the accurate classification of diseases and disease subtypes. In oncology, current “gold standard” approaches include histology, which requires interpretation by experts, or assessment of molecular markers such as cell surface receptors or gene expression.

Fundamental biological study: application of deep learning to fundamental biological questions using methods based on leveraging large amounts.

Treatment of patients: new methods to recommend patient treatments, predict treatment outcomes, and guide the development of new therapies.

Within these, areas where deep learning plays a part for biology and medicine are

Deep learning and patient categorization

  • Imaging applications in healthcare
  • Electronic health records
  • Challenges and opportunities in patient categorization

Deep learning to study the fundamental biological processes underlying human disease

  • Gene expression
  • Splicing
  • Transcription factors and RNA-binding proteins
  • Promoters, enhancers, and related epigenomic tasks
  • Micro-RNA binding
  • Protein secondary and tertiary structure
  • Morphological phenotypes
  • Single-cell data
  • Metagenomics
  • Sequencing and variant calling

The impact of deep learning in treating disease and developing new treatments

  • Clinical decision making
  • Drug repositioning
  • Drug development

There are a number of areas that impact deep learning in biology and medicine

These include:

  • Evaluation
  • Evaluation metrics for imbalanced classification
  • Formulation of classification labels
  • Formulation of a performance upper bound
  • Interpretation and explainable results
  • Hardware limitations and scaling
  • Data, code, and model sharing
  • Multimodal, multi-task, and transfer learning

I found two particularly interesting aspects: interpretability and data limitations. As per the paper:

  • deep learning lags behind…

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