Write in a clean editor, then format for Artificial Intelligence in the Life Sciences in one click — DocuGuru applies the official Elsevier template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Artificial Intelligence in the Life Sciences is a peer-reviewed journal published by Elsevier, covering Computational Drug Discovery Methods, Machine Learning in Materials Science, Cell Image Analysis Techniques.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Artificial Intelligence in the Life Sciences 12 (2023) 45–58.
Formats any DOI in Artificial Intelligence in the Life Sciences style. No sign-up. |
| Publishes research in | Computational Drug Discovery Methods Machine Learning in Materials Science Cell Image Analysis Techniques Bioinformatics and Genomic Networks Genetics, Bioinformatics, and Biomedical Research |
| ISSN | 2667-3185 |
| Citation impact (2-yr) | 2.01 |
| h-index | 20 |
| i10-index | 47 |
| Total citations | 1,887 |
| Article processing charge | $1,450 |
| Open access | Yes |
| Top institutions publishing here | University of Bonn |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfills humans’ basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like India. Agriculture contributes 15.4% in the GDP…
The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), artificial potential field (APF), simulated annealing (SA), ant colony optimization (ACO), artificial bee colony algorithm (ABC), harmony search algorithm (HS), bat algorithm (BA), cell decomposition (CD) and firefly…
While Relative Binding Free Energy (RBFE) calculations have become a mainstay in lead optimization programs, the computational expense of performing these calculations has limited their broader application. Active learning (AL), a machine learning method used to direct a search iteratively, has explored larger chemical libraries using RBFE calculations. While AL has been successfully applied, there…
Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab-based experiments…
A rising amount of research demonstrates that artificial intelligence and machine learning approaches can provide an essential basis for the drug design and discovery process. Deep learning algorithms are being developed in response to recent advances in computer technology as part of the creation of therapeutically relevant medications for the treatment of a variety of…
