Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the develo...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
The field of data analysis, preparation, and machine learning is rapidly expanding, offering numerous libraries and resources for exploration. Researchers gain knowledge through various channels, but few resources provide a comprehensive framework fo...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
This chapter presents a practical guide for conducting sentiment analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse ...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm of biology. These networks consist of transcription factors and their associations with genes they regula...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgenc...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Building and analyzing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility ...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing o...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it pos...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becom...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Absorption, distribution, metabolism, excretion (ADME) are key properties of a small molecule that govern pharmacokinetic profiles and impact its efficacy and safety. Computational methods such as machine learning and artificial intelligence have gai...