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Rubhashri Gurunathan, Rajaram Abhirami, Muthuraja Arun Pravin, Sanjeev Kumar Singh
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Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
10.1038/nbt.3300 · 2015
Transcriptional regulatory networks underlying gene expression changes in Huntington’s disease
10.15252/msb.20167435 · 2018
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets
10.15252/msb.20178124 · 2018
Deep learning in drug discovery: an integrative review and future challenges
10.1007/s10462-022-10306-1 · 2023
Incorporating machine learning into established bioinformatics frameworks
10.3390/ijms22062903 · 2021
High-throughput screening of natural product and synthetic molecule libraries for antibacterial drug discovery
10.3390/metabo13050625 · 2023
Artificial intelligence in healthcare: transforming the practice of medicine
10.7861/fhj.2021-0095 · 2021
Generalizing RNA velocity to transient cell states through dynamical modeling
10.1038/s41587-020-0591-3 · 2020
Pattern classification with polynomial learning machines
2006
Exploring the artificial intelligence and its impact in pharmaceutical sciences: insights toward the horizons where technology meets tradition
10.1111/cbdd.14639 · 2024
QTL analysis of yield traits in an advanced backcross population derived from a cultivated Andean × wild common bean (Phaseolus vulgarisL.) cross
10.1007/s00122-006-0217-2 · 2006
The role of AI in drug discovery: challenges, opportunities, and strategies
10.3390/ph16060891 · 2023
Decision trees: from efficient prediction to responsible AI
10.3389/frai.2023.1124553 · 2023
Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research
10.1186/s12859-015-0472-9 · 2015
Support vector machines for classification and regression
10.1039/b918972f · 2010
Artificial intelligence enabled ChatGPT and large language models in drug target discovery, drug discovery, and development
10.1016/j.omtn.2023.08.009 · 2023
The role of artificial neural networks on target validation in drug discovery and development
2016
Omics data integration in cancer research: recent advances and future directions
2021
Unresolved referenced work
2016
A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
2010
Effectively utilizing publicly available databases for cancer target evaluation
10.1093/narcan/zcad035 · 2023
Machine learning in drug discovery: a review
10.1007/s10462-021-10058-4 · 2022
Artificial intelligence in drug discovery: applications and techniques
10.1093/bib/bbab430 · 2022
Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions
10.1093/bib/bbab476 · 2022
Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms
10.1371/journal.pone.0300717 · 2024
Dermatologist-level classification of skin cancer with deep neural networks
10.1038/nature21056 · 2017
Advances in drug delivery systems, challenges and future directions
10.1016/j.heliyon.2023.e17488 · 2023
Prioritizing target-disease associations with novel safety and efficacy scoring methods
10.1038/s41598-019-46293-7 · 2019
The druggable genome and the importance of target class diversity
2017
Artificial intelligence in omics
10.1016/j.gpb.2023.01.002 · 2022
Population diversity and health disparities in drug target discovery
2022
Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery
10.3390/ph16091259 · 2023
Health natural language processing: methodology development and applications
10.2196/23898 · 2021
Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study
10.1186/s12910-024-01062-8 · 2024
A high-throughput metabolomics method to predict high concentration cytotoxicity of drugs from low concentration profiles
10.1007/s11306-011-0386-0 · 2012
The diversity of the human genome and its implications for personalized medicine
2018
AI-driven drug discovery: accelerating the development of novel therapeutics in biopharmaceuticals
10.60087/jklst.vol3.n3.p.206-224 · 2024
A landscape of pharmacogenomic interactions in cancer
10.1016/j.cell.2016.06.017 · 2016
Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches
2024
Metascape provides a biologist-oriented resource for the analysis of systems-level datasets
10.1038/s41467-019-09234-6 · doi-reference
Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents
10.1093/nar/gkab953 · doi-reference
The impact of pricing schemes on cloud computing and distributed systems
10.60087/jklst.v3.n3.p206-224 · doi-reference
OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space
10.1093/nar/gky510 · doi-reference
Deep biomarkers of aging and longevity: from research to applications
10.18632/aging.102475 · doi-reference
Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis
10.1016/j.neuron.2021.12.019 · doi-reference
Target identification among known drugs by deep learning from heterogeneous networks
10.1039/c9sc04336e · doi-reference
DeepLigand: accurate prediction of MHC class I ligands using peptide embedding
10.1093/bioinformatics/btz330 · doi-reference
Industrializing AI/ML during the end-to-end drug discovery process
10.1016/j.sbi.2023.102528 · doi-reference
Biomedical big data technologies, applications, and challenges for precision medicine: a review
10.1002/gch2.202300163 · doi-reference
Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy
10.1126/scitranslmed.abl5654 · doi-reference
Revolutionizing drug discovery: the impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry
10.1016/j.ipha.2024.02.009 · doi-reference
Recent advances in targeting the “undruggable” proteins: from drug discovery to clinical trials
10.1038/s41392-023-01589-z · doi-reference
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
10.1186/s12859-023-05262-8 · doi-reference
AI based drug screening process: from data mining to candidate drug validation
10.5376/bm.2024.15.0005 · doi-reference
Distributed high-performance computing methods for accelerating deep learning training
10.60087/jklst.v3.n3.p108-126 · doi-reference
Tools for target identification and validation
10.1016/j.cbpa.2004.06.001 · doi-reference
A new view of transcriptome complexity and regulation through the lens of local splicing variations
10.7554/elife.11752 · doi-reference
AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements
10.1186/s43088-024-00503-y · doi-reference
Impact of data pre-processing techniques on recurrent neural network performance in context of real-time drilling logs in an automated prediction framework
10.1016/j.petrol.2021.109760 · doi-reference
iNGNN-DTI:: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models
10.1093/bioinformatics/btae135 · doi-reference
Why 90% of clinical drug development fails and how to improve it?
10.1016/j.apsb.2022.02.002 · doi-reference
Network medicine in the age of biomedical big data
10.3389/fgene.2019.00294 · doi-reference
Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders
10.1016/j.bbrc.2023.09.064 · doi-reference
Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors
10.1016/j.ymeth.2023.09.010 · doi-reference
10.1007/s11030-021-10326-z
10.1007/s11030-021-10326-z · doi-reference
Target identification and mechanism of action in chemical biology and drug discovery
10.1038/nchembio.1199 · doi-reference
A knowledge graph to interpret clinical proteomics data
10.1038/s41587-021-01145-6 · doi-reference
Natural language processing in medicine and ophthalmology: a review for the 21st-century clinician
10.1016/j.apjo.2024.100084 · doi-reference
Effectiveness of artificial intelligence for personalized medicine in neoplasms: a systematic review
10.1155/2022/7842566 · doi-reference
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
10.1039/d2sc05709c · doi-reference
Artificial intelligence and machine learning in precision and genomic medicine
10.1007/s12032-022-01711-1 · doi-reference
AI-powered therapeutic target discovery
10.1016/j.tips.2023.06.010 · doi-reference
Identification of therapeutic targets for amyotrophic lateral sclerosis using pandaomics—An AI-enabled biological target discovery platform
10.3389/fnagi.2022.914017 · doi-reference
Deep reinforcement learning for de novo drug design
10.1126/sciadv.aap7885 · doi-reference
A universal SNP and small-indel variant caller using deep neural networks
10.1038/nbt.4235 · doi-reference
Genomics is failing on diversity
10.1038/538161a · doi-reference
Artificial intelligence in drug discovery and development
10.1016/j.drudis.2020.10.010 · doi-reference
MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation
10.1093/nar/gkae253 · doi-reference
GEMINI: integrative exploration of genetic variation and genome annotations
10.1371/journal.pcbi.1003153 · doi-reference