These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
22. Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models. Wang M; Greenberg M; Forkert ND; Chekouo T; Afriyie G; Ismail Z; Smith EE; Sajobi TT BMC Med Res Methodol; 2022 Nov; 22(1):284. PubMed ID: 36324086 [TBL] [Abstract][Full Text] [Related]
23. Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning. Gupta R; Kleinjans J; Caiment F BMC Cancer; 2021 Aug; 21(1):962. PubMed ID: 34445986 [TBL] [Abstract][Full Text] [Related]
24. Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets. Zorn KM; Lane TR; Russo DP; Clark AM; Makarov V; Ekins S Mol Pharm; 2019 Apr; 16(4):1620-1632. PubMed ID: 30779585 [TBL] [Abstract][Full Text] [Related]
25. Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity. Balraadjsing S; Peijnenburg WJGM; Vijver MG Chemosphere; 2022 Nov; 307(Pt 2):135930. PubMed ID: 35961453 [TBL] [Abstract][Full Text] [Related]
26. A clinical text classification paradigm using weak supervision and deep representation. Wang Y; Sohn S; Liu S; Shen F; Wang L; Atkinson EJ; Amin S; Liu H BMC Med Inform Decis Mak; 2019 Jan; 19(1):1. PubMed ID: 30616584 [TBL] [Abstract][Full Text] [Related]
27. Machine learning algorithms for prediction of entrapment efficiency in nanomaterials. Fahmy OM; Eissa RA; Mohamed HH; Eissa NG; Elsabahy M Methods; 2023 Oct; 218():133-140. PubMed ID: 37595853 [TBL] [Abstract][Full Text] [Related]
28. A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency. Liu Y; Fan R; Yi J; Cui Q; Cui C Comput Biol Med; 2023 Oct; 165():107476. PubMed ID: 37696181 [TBL] [Abstract][Full Text] [Related]
29. Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling. Tuladhar A; Gill S; Ismail Z; Forkert ND; J Biomed Inform; 2020 Jun; 106():103424. PubMed ID: 32335226 [TBL] [Abstract][Full Text] [Related]
30. A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development. Turkovic E; Vasiljevic I; Parojcic J Int J Pharm; 2024 Jun; 658():124188. PubMed ID: 38705248 [TBL] [Abstract][Full Text] [Related]
32. Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features. Herbuela VRDM; Karita T; Furukawa Y; Wada Y; Toya A; Senba S; Onishi E; Saeki T PLoS One; 2022; 17(6):e0269472. PubMed ID: 35771797 [TBL] [Abstract][Full Text] [Related]
33. Review of machine learning and deep learning models for toxicity prediction. Guo W; Liu J; Dong F; Song M; Li Z; Khan MKH; Patterson TA; Hong H Exp Biol Med (Maywood); 2023 Nov; 248(21):1952-1973. PubMed ID: 38057999 [TBL] [Abstract][Full Text] [Related]
34. Application of machine learning classifiers to X-ray diffraction imaging with medically relevant phantoms. Stryker S; Kapadia AJ; Greenberg JA Med Phys; 2022 Jan; 49(1):532-546. PubMed ID: 34799852 [TBL] [Abstract][Full Text] [Related]
35. Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets. Cha GW; Moon HJ; Kim YM; Hong WH; Hwang JH; Park WJ; Kim YC Int J Environ Res Public Health; 2020 Sep; 17(19):. PubMed ID: 32987874 [TBL] [Abstract][Full Text] [Related]
36. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Das J; Kumar S; Mishra DC; Chaturvedi KK; Paul RK; Kairi A Front Genet; 2022; 13():1085332. PubMed ID: 36699447 [TBL] [Abstract][Full Text] [Related]
37. A deep neural network-based approach for prediction of mutagenicity of compounds. Kumar R; Khan FU; Sharma A; Siddiqui MH; Aziz IB; Kamal MA; Ashraf GM; Alghamdi BS; Uddin MS Environ Sci Pollut Res Int; 2021 Sep; 28(34):47641-47650. PubMed ID: 33895950 [TBL] [Abstract][Full Text] [Related]
38. Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS". Mancini A; Vito L; Marcelli E; Piangerelli M; De Leone R; Pucciarelli S; Merelli E BMC Bioinformatics; 2020 Aug; 21(Suppl 10):347. PubMed ID: 32838752 [TBL] [Abstract][Full Text] [Related]
39. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673 [TBL] [Abstract][Full Text] [Related]
40. An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits. Wang T; Russo DP; Demokritou P; Jia X; Huang H; Yang X; Zhu H Nano Lett; 2024 Aug; 24(33):10228-10236. PubMed ID: 39120132 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]