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Deep learning, fueled by big data and advanced algorithms, is transforming artificial intelligence and revolutionizing pharma research. This study offers practical guidance on selecting suitable deep learning frameworks for life science applications.

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Area of Science:

  • Artificial Intelligence
  • Life Sciences
  • Pharmaceutical Research

Background:

  • Deep learning (DL) has significantly advanced artificial intelligence (AI) in recent years.
  • DL is poised to automate complex tasks and drive innovation in research, particularly in data-intensive fields like pharma and life sciences.
  • The growth of DL applications is attributed to big data availability, algorithmic progress, and increased computational power.

Purpose of the Study:

  • To introduce typical applications of deep learning in life science and pharma research.
  • To present underlying software frameworks for DL applications.
  • To provide practical criteria and suggestions for selecting suitable DL frameworks for production-ready solutions.

Main Methods:

  • Review of typical deep learning applications in image analysis (e.g., high-content screening, digital pathology).
  • Discussion of software frameworks enabling DL applications.
  • Development of practical criteria for framework selection based on real-world experience.

Main Results:

  • Deep learning excels in perception-based tasks, such as automated image analysis, previously challenging for traditional machine learning.
  • Various software frameworks exist for DL, each with distinct strengths and weaknesses.
  • A baseline for selecting future-proof and cost-effective DL frameworks is proposed.

Conclusions:

  • Deep learning is a transformative technology for the pharma and life sciences industries.
  • Careful selection of DL frameworks is crucial for successful and efficient development of research applications.
  • The study provides valuable insights for researchers and developers navigating the DL landscape.