Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …
the drug discovery process. It narrows down the search space for interactions by suggesting …
MDHGI: matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction
Recently, a growing number of biological research and scientific experiments have
demonstrated that microRNA (miRNA) affects the development of human complex diseases …
demonstrated that microRNA (miRNA) affects the development of human complex diseases …
Drug repositioning based on comprehensive similarity measures and bi-random walk algorithm
Motivation: Drug repositioning, which aims to identify new indications for existing drugs,
offers a promising alternative to reduce the total time and cost of traditional drug …
offers a promising alternative to reduce the total time and cost of traditional drug …
A Bayesian machine learning approach for drug target identification using diverse data types
NS Madhukar, PK Khade, L Huang, K Gayvert… - Nature …, 2019 - nature.com
Drug target identification is a crucial step in development, yet is also among the most
complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that …
complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that …
Biomedical data and computational models for drug repositioning: a comprehensive review
Drug repositioning can drastically decrease the cost and duration taken by traditional drug
research and development while avoiding the occurrence of unforeseen adverse events …
research and development while avoiding the occurrence of unforeseen adverse events …
Computational drug repositioning using low-rank matrix approximation and randomized algorithms
Motivation Computational drug repositioning is an important and efficient approach towards
identifying novel treatments for diseases in drug discovery. The emergence of large-scale …
identifying novel treatments for diseases in drug discovery. The emergence of large-scale …
Drug repositioning by integrating target information through a heterogeneous network model
Motivation: The emergence of network medicine not only offers more opportunities for better
and more complete understanding of the molecular complexities of diseases, but also …
and more complete understanding of the molecular complexities of diseases, but also …
DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Motivation Finding computationally drug–target interactions (DTIs) is a convenient strategy
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …
development. Computational prediction of DTIs can effectively complement experimental …