Academic literature on the topic 'In silico drug prediction'
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Journal articles on the topic "In silico drug prediction"
Dmitriev, Alexander V., Anastassia V. Rudik, Dmitry A. Karasev, Pavel V. Pogodin, Alexey A. Lagunin, Dmitry A. Filimonov, and Vladimir V. Poroikov. "In Silico Prediction of Drug–Drug Interactions Mediated by Cytochrome P450 Isoforms." Pharmaceutics 13, no. 4 (April 13, 2021): 538. http://dx.doi.org/10.3390/pharmaceutics13040538.
Full textHutter, M. "In Silico Prediction of Drug Properties." Current Medicinal Chemistry 16, no. 2 (January 1, 2009): 189–202. http://dx.doi.org/10.2174/092986709787002736.
Full textGhislat, Ghita, Taufiq Rahman, and Pedro J. Ballester. "Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit." Biomolecules 10, no. 11 (November 19, 2020): 1570. http://dx.doi.org/10.3390/biom10111570.
Full textParanjpe, Pankaj V., George M. Grass, and Patrick J. Sinko. "In Silico Tools for Drug Absorption Prediction." American Journal of Drug Delivery 1, no. 2 (2003): 133–48. http://dx.doi.org/10.2165/00137696-200301020-00005.
Full textCarbonell, Pablo, and Jean-Yves Trosset. "Overcoming drug resistance through in silico prediction." Drug Discovery Today: Technologies 11 (March 2014): 101–7. http://dx.doi.org/10.1016/j.ddtec.2014.03.012.
Full textDmitriev, Alexander V., Alexey A. Lagunin, Dmitry А. Karasev, Anastasia V. Rudik, Pavel V. Pogodin, Dmitry A. Filimonov, and Vladimir V. Poroikov. "Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes." Current Topics in Medicinal Chemistry 19, no. 5 (April 18, 2019): 319–36. http://dx.doi.org/10.2174/1568026619666190123160406.
Full textSharma, S., K. Daniel, V. Daniel, and L. Sharma. "IN-SILICO PRELIMINARY DOCKING SCREENING OF SOME ANTI-ALZHEIMER DRUGS." INDIAN DRUGS 53, no. 06 (June 28, 2016): 74–79. http://dx.doi.org/10.53879/id.53.06.10429.
Full textDewulf, Pieter, Michiel Stock, and Bernard De Baets. "Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects." Pharmaceuticals 14, no. 5 (May 2, 2021): 429. http://dx.doi.org/10.3390/ph14050429.
Full textWang, Xiao, Chen, and Wang. "In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method." International Journal of Molecular Sciences 20, no. 17 (August 22, 2019): 4106. http://dx.doi.org/10.3390/ijms20174106.
Full textAndrade, Carolina, Diego Silva, and Rodolpho Braga. "In silico Prediction of Drug Metabolism by P450." Current Drug Metabolism 15, no. 5 (November 26, 2014): 514–25. http://dx.doi.org/10.2174/1389200215666140908102530.
Full textDissertations / Theses on the topic "In silico drug prediction"
Ahlin, Gustav. "In vitro and in silico prediction of drug-drug interactions with transport proteins." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-107492.
Full textCarrió, Gaspar Pau 1982. "Development of advanced strategies for the prediction of toxicity endpoints in drug development." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/328418.
Full textLa manca de seguretat és una de les raons principals per la qual els candidats a fàrmacs són descartats. La fase en què els possibles efectes tòxics són identificats és crítica: descartar un candidat en fase clínica implica la pèrdua d'anys d'esforços i enormes inversions econòmiques. Encara pitjor és identificar efectes tòxics un cop el fàrmac està comercialitzat, quan es poden haver produït greus efectes secundaris en pacients. Per aquestes raons hi ha la necessitat de desenvolupar mètodes capaços d'avaluar la seguretat dels candidats a fàrmacs en les primeres etapes. Entre aquests, els mètodes in silico tenen molts avantatges, com no requerir la disponibilitat del compost, no perdre cap quantitat en cas que ja s'hagi sintetitzat, ser ràpid, econòmic i no fer ús de l'experimentació amb animals. Per desgràcia, els mètodes de predicció in silico aplicats a criteris d'avaluació de toxicitat no produeixen els resultats adequats. Les raons són objecte de debat, però raons probables són la complexitat dels fenòmens biològics en estudi i la gran diversitat estructural els fàrmacs candidats, entre d'altres. L'objectiu d'aquesta tesi és millorar els mètodes de predicció in silico emprats en l’avaluació de criteris d'interès en el desenvolupament de fàrmacs amb especial èmfasi en els de toxicitat. Presentem una nova metodologia general anomenada ADAN (Applicability Domain Analysis) per avaluar la fiabilitat de les prediccions obtingudes amb mètodes in silico. A més, proposem una estratègia unificada de l’ús de mètodes de predicció in silico emprats en aquest camp; com alertes estructurals, read-across, QSAR local i global. La estratègia incorpora criteris racionals per la seva utilització. Els bons resultats obtinguts amb dades representatives confirmen la validesa de les metodologies.
Vildhede, Anna. "In vitro and in silico Predictions of Hepatic Transporter-Mediated Drug Clearance and Drug-Drug Interactions in vivo." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-241376.
Full textBergström, Christel A. S. "Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption." Doctoral thesis, Uppsala University, Department of Pharmacy, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3593.
Full textNew effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the number of new drugs undergoing clinical trial has not augmented at the same pace, which in part has been attributed to poor absorption of the compounds.
The main objective of this thesis was to investigate whether computer-based models devised from calculated molecular descriptors can be used to predict aqueous drug solubility, an important property influencing the absorption process. For this purpose, both experimental and computational studies were performed. A new small-scale shake flask method for experimental solubility determination of crystalline compounds was devised. This method was used to experimentally determine solubility values used for the computational model development and to investigate the pH-dependent solubility of drugs. In the computer-based studies, rapidly calculated molecular descriptors were used to predict aqueous solubility and the melting point, a solid state characteristic of importance for the solubility. To predict the absorption process, drug permeability across the intestinal epithelium was also modeled.
The results show that high quality solubility data of crystalline compounds can be obtained by the small-scale shake flask method in a microtiter plate format. The experimentally determined pH-dependent solubility profiles deviated largely from the profiles predicted by a traditionally used relationship, highlighting the risk of data extrapolation. The in silico solubility models identified the non-polar surface area and partitioned total surface areas as potential new molecular descriptors for solubility. General solubility models of high accuracy were obtained when combining the surface area descriptors with descriptors for electron distribution, connectivity, flexibility and polarity. The used descriptors proved to be related to the solvation of the molecule rather than to solid state properties. The surface area descriptors were also valid for permeability predictions, and the use of the solubility and permeability models in concert resulted in an excellent theoretical absorption classification. To summarize, the experimental and computational models devised in this thesis are improved absorption screening tools applicable to the lead optimization in the drug discovery process.
Carlert, Sara. "Investigation and Prediction of Small Intestinal Precipitation of Poorly Soluble Drugs : a Study Involving in silico, in vitro and in vivo Assessment." Doctoral thesis, Uppsala universitet, Institutionen för farmaci, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-178053.
Full textWeston, Anne. "In silico prediction of protein function." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412922.
Full textSandelin, Albin. "In silico prediction of CIS-regulatory elements /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-879-3/.
Full textCereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.
Full textEl cribado virtual es un método quimioinformático que consiste en la criba de moléculas bioactivas de grandes bases de datos de moléculas pequeñas. Esto permite a los investigadores ahorrarse el coste de probar experimentalmente cientos o miles de compuestos candidatos, reduciéndolos hasta cantidades manejables. Para la validación de los métodos de cribado virtual hacen falta bibliotecas de moléculas señuelo. El software DecoyFinder fue desarrollado como aplicación gráfica de fácil uso para la construcción de bibliotecas de moléculas señuelo, y fue posteriormente ampliado con los hallazgos de investigación posterior sobre la construcción i rendimiento de bibliotecas de moléculas señuelo. El Protein Data Bank (PDB) es muy útil porque proporciona estructuras tridimensionales para complejos proteina-ligando, y por tanto, información sobre como interactúan. Para los métodos de cribado virtual que dependen de ellas, es extremadamente importante su fiabilidad. VHELIBS fue desarrollado como herramienta para inspeccionar e identificar, fácil e intuitivamente, las estructuras fiables del PDB, basándose en como de bueno es su encaje con sus correspondientes mapas de densidad electrónica. Mientras que el cribado virtual intenta encontrar nuevas moléculas bioactivas para determinadas dianas, el enfoque inverso también se utiliza: a partir de una molécula, buscar dianas donde presente actividad biológica no documentada. Este cribado inverso es conocido en inglés como “in silico target fishing”, o pesca de dianas “in silico”, y es especialmente útil en el ámbito de la reutilización de fármacos. Al comenzar esta tesis, no había ninguna plataforma de “target fishing” de libre acceso, y aunque durante los años se han desarrollado algunas, en todos los casos su predicción de bioactividad es cualitativa. Por eso se desarrolló una plataforma propia de “target fishing” de libre acceso, con la implementación de un nuevo método que proporciona la primera predicción cuantitativa de bioactividad para este tipo de plataforma.
Virtual screening is a cheminformatics method that consists of screening large small-molecule databases for bioactive molecules. This enables the researcher to avoid the cost of experimentally testing hundreds or thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. For their validation, virtual screening approaches need decoy molecule libraries. DecoyFinder was developed as an easy to use graphical application for decoy library building, and later updated after some research into decoy library building and their performance when used for 2D similarity approaches. The Protein Data Bank (PDB) is very useful because it provides 3D structures for protein-ligand complexes and, therefore, information on how certain ligands bind and interact with their targets. For virtual screening apporaches relying on these structures, it is of the utmost importance that the data available on the PDB for the ligand and its binding site are reliable. VHELIBS was developed as a tool to easily and intuitively inspect and identify reliable PDB structures based on the goodness of fitting between ligands and binding sites and their corresponding electron density map. While virtual screening aims to find new bioactive molecules for certain targets, the opposite approach is also used: starting from a given molecule, to search for a biological target for which it presents previously undocumented bioactivity. This reverse screening is known as in silico or computational target fishing or reverse pharmacognosy, and it is specially useful for drug repurposing or repositioning. When this thesis was started, there were no freely available target fishing platforms, but some have been developed during the years. However, they are qualitative in the nature of their activity prediction, and thus we set out to develop a freely accessible target fishing web service implementing a novel method which provides the first quantitative activity prediction: Anglerfish.
Acoca, Stephane. "In silico methods in drug discovery and development." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110376.
Full textLes méthodes the modélisation sont devenues un outil inestimable dans le processus de découverte et de développement de nouveaux médicaments. Au cours de cette thèse va être décrit le développement et l'application de méthodes utilisés à chaque stage de la découverte et du développement de produits pharmaceutiques. Le Chapitre 2 est un aperçu sur l'utilisation de méthodes computationnelles vers le développement de deux nouveaux inhibiteurs des protéines Bcl-2, Obatoclax et ABT-737, en développement pour le traitement du Cancer. L'étude propose certains mécanismes d'ABT-737 qui expliquent ca sélectivité envers les membres de la famille Bcl-2. De plus, nous proposons un mécanisme d'attachement pour Obatoclax qui conforme aux données expérimentales. Le Chapitre suivant adresse l'utilisation du dépistage virtuel pour l'identification de nouvelles molécules mère. La Ligase de l'Edition d'ARN du Trypanosoma brucei a été choisie comme cible pour le développement de traitements contre des infections dû au Trypanosome et C35 a été identifié comme nouvel inhibiteur de l'enzyme. En outre, notre recherche démontre que l'action de C35 s'étends a l'inhibition de plusieurs enzymes nécessaires pour le mécanisme d'édition de l'ARN en plus de compromettre l'intégrité du complexe multi-protéinique qui l'effectue. Le Chapitre suivant prends regard a l'utilisation de donnes dérivant de la spectrométrie de masse pour but d'accélérer la découverte de molécules bioactives venant de sources naturelles. Nous avons développé un algorithme qui analyse les données MS/MS pour but de dériver la formule moléculaire du composé. Le nouvel algorithme a obtenu un taux de succès s'élevant à 95% sur un ensemble test de 91 molécules. Le dernier Chapitre de la thèse explore l'utilisation de simulations de dynamique moléculaire pour générer en ensemble conformationel de protéines cible pour son utilisation dans le dépistage virtuel. Les ensembles conformationel ont étés généré pour une série test obtenu d'un répertoire attitré 'Directory for Useful Decoys'. Les résultats démontrent que les ensembles conformationel dérivés de la dynamique moléculaire ont apporté des améliorations remarquables sur deux des cibles testées dû à une capacité accrue de placement approprié des molécules dans un site qui est autrement très restreint. Le dernier Chapitre de cette thèse est une discussion générale sur le travail accomplie et une proposition sur la manière dont tous les éléments sont intégrer dans un protocole de découverte et de développement de produits pharmaceutiques.
Kundu, Kousik [Verfasser], and Rolf [Akademischer Betreuer] Backofen. "In Silico Prediction of Modular Domain-Peptide Interactions." Freiburg : Universität, 2015. http://d-nb.info/1115861883/34.
Full textBooks on the topic "In silico drug prediction"
Benfenati, Emilio, ed. In Silico Methods for Predicting Drug Toxicity. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1960-5.
Full textBenfenati, Emilio, ed. In Silico Methods for Predicting Drug Toxicity. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3609-0.
Full textKortagere, Sandhya, ed. In Silico Models for Drug Discovery. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-342-8.
Full textKortagere, Sandhya. In silico models for drug discovery. New York: Humana Press, 2013.
Find full textDressman, J. B., and C. Reppas. Oral drug absorption: Prediction and assessment. 2nd ed. New York: Informa Healthcare USA, 2010.
Find full textB, Dressman J., and Lennernäs Hans, eds. Oral drug absorption: Prediction and assessment. New York: Marcel Dekker, 2000.
Find full textGabriele, Cruciani, ed. Molecular interaction fields: Applications in drug discovery and ADME prediction. Weinheim: Wiley-VCH, 2005.
Find full textHinderling, P. H. Drug distribution in the body: In vitro prediction and physiological interpretation. Stuttgart: Gustav Fischer, 1988.
Find full textStumpf, Walter E. Drug localization in tissues and cells: Receptor microscopic autoradiography : a basis for tissue and cellular pharmacokinetics, drug targeting, delivery, and prediction. Chapel Hill, NC: IDDC-Press, 2003.
Find full textM, Greenwood B., and De Cock Kevin, eds. New and resurgent infections: Prediction, detection, and management of tomorrow's epidemics. Chichester: J. Wiley & Sons, 1998.
Find full textBook chapters on the topic "In silico drug prediction"
Ali, Mohammed A., Rachel Hemingway, and Martin A. Ott. "In Silico Drug Degradation Prediction." In Methods in Pharmacology and Toxicology, 53–73. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7686-7_3.
Full textCronin, Mark T. D. "Chapter 2. In Silico Tools for Toxicity Prediction." In Drug Discovery, 9–25. Cambridge: Royal Society of Chemistry, 2011. http://dx.doi.org/10.1039/9781849733045-00009.
Full textYu, Lawrence X., Christopher D. Ellison, and Ajaz S. Hussain. "Predicting Human Oral Bioavailability Using in Silico Models." In Applications of Pharmacokinetic Principles in Drug Development, 53–74. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9216-1_3.
Full textPonting, David J., Michael J. Burns, Robert S. Foster, Rachel Hemingway, Grace Kocks, Donna S. MacMillan, Andrew L. Shannon-Little, Rachael E. Tennant, Jessica R. Tidmarsh, and David J. Yeo. "Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity." In Methods in Molecular Biology, 435–78. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1960-5_17.
Full textBachmann, Kenneth, and Sean Ekins. "The Potential of In Silico and In Vitro Approaches to Predict In Vivo Drug-Drug Interactions and ADMET/TOX Properties." In Predictive Approaches in Drug Discovery and Development, 307–29. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118230275.ch13.
Full textCutinho, Pretisha Flora, C. H. S. Venkataramana, and B. V. Suma. "In Silico Hit Identification, Drug Repurposing, Pharmacokinetic and Toxicity Prediction of c-Met Kinase Inhibitors for Cancer Therapy." In Special Publications, 54–59. Cambridge: Royal Society of Chemistry, 2019. http://dx.doi.org/10.1039/9781839160783-00054.
Full textGupta, Praveen Kumar, Mohammed Haseeb Nawaz, Shyam Shankar Mishra, Kruthika Parappa, Akhil Silla, and Raju Hanumegowda. "New Age Approaches to Predictive Healthcare Using In Silico Drug Design and Internet of Things (IoT)." In Sustainable and Energy Efficient Computing Paradigms for Society, 127–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51070-1_8.
Full textSingstad, Bjørn Jostein, Bendik Steinsvåg Dalen, Sandhya Sihra, Nickolas Forsch, and Samuel Wall. "Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers." In Computational Physiology, 91–109. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05164-7_8.
Full textAmberg, Alexander. "In Silico Methods." In Drug Discovery and Evaluation, 801–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-29804-5_43.
Full textMatter, Hans, and Wolfgang Schmider. "In-Silico ADME Modeling." In Drug Discovery and Evaluation, 409–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-29804-5_20.
Full textConference papers on the topic "In silico drug prediction"
Llopis-Lorente, Jordi, Beatriz Trenor, and Javier Saiz. "Prediction of Drug-Induced Arrhythmogenic Risk Using In Silico Populations of Models." In 2021 Computing in Cardiology (CinC). IEEE, 2021. http://dx.doi.org/10.23919/cinc53138.2021.9662679.
Full textEl-Khouly, Omar A., Dina I. A. Othman, Amany S. Mostafa, and Mohammed A. M. Massoud. "Thiazolopyrimidine as a Promising Anticancer Pharmacophore: In Silico Drug Design, Molecular Docking and ADMET Prediction Studies." In ECMC 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/ecmc2022-13313.
Full textDeb, Subrata, and Anthony Reeves. "<em>In Silico</em> prediction of biopharmaceutical features of remdesivir: A serendipitous drug for COVID-19." In 6th International Electronic Conference on Medicinal Chemistry. Basel, Switzerland: MDPI, 2020. http://dx.doi.org/10.3390/ecmc2020-07301.
Full textAzmi, Muhammad Bilal. "In Silico Basis to Understand the Molecular Interaction of Human NNATGene With Therapeutic Compounds of Anorexia Nervosa." In INTERNATIONAL CONFERENCE ON BIOLOGICAL RESEARCH AND APPLIED SCIENCE. Jinnah University for Women, Karachi,Pakistan, 2022. http://dx.doi.org/10.37962/ibras/2022/1-2.
Full textHuang, Hung-Jin, Fuu-Jen Tsai, Jing-Gung Chung, Chang-Hai Tsai, Yuan-Man Hsu, Tin-Yun Ho, Yea-Huey Chang, Da-Tian Bau, Ming-Hsui Tsai, and Calvin Yu-Chian Chen. "Drug Design for XRCC4 in Silico." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5304961.
Full textKumar, Dhananjay, Anshul Sarvate, Sakshi Singh, and Puja Priya. "Comparative modelling and in-silico drug designing." In 2013 IEEE Conference on Information & Communication Technologies (ICT). IEEE, 2013. http://dx.doi.org/10.1109/cict.2013.6558165.
Full textHayati, Hamideh, and Yu Feng. "A Precise Scale-Up Method to Predict Particle Delivered Dose in a Human Respiratory System Using Rat Deposition Data: An In Silico Study." In 2020 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dmd2020-9060.
Full textBaba, Waqas, and Sajid Maqsood. "Novel antihypertensive and anticholesterolemic peptides from peptic hydrolysates of camel whey proteins." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/qecs2081.
Full textChen, Chien-Yu, Da-Tian Bau, Ming-Hsui Tsai, Yuan-Man Hsu, Tin-Yun Ho, Hung-Jin Huang, Yea-Huey Chang, Fuu-Jen Tsai, Chang-Hai Tsai, and Calvin Yu-Chian Chen. "Drug Design for AMP-Activated Protein Kinase Agonists in Silico." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5304901.
Full textPezoulas, Vasileios, Nikos Tachos, and Dimitrios Fotiadis. "Generation of Virtual Patients for in Silico Cardiomyopathies Drug Development." In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2019. http://dx.doi.org/10.1109/bibe.2019.00126.
Full textReports on the topic "In silico drug prediction"
Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.
Full textNilmeier, J., J. Fattebert, M. Jacobson, and C. Kalyanaraman. Quantum mechanical approaches to in silico enzyme characterization and drug design. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1034511.
Full textHsieh, John C. F., Robert L. Jernigan, and Susan J. Lamont. Host-Pathogen Protein-Protein Interaction Prediction Using an in silico Model. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-224.
Full textWashburn, Ammon Joseph, Thomas James Sherman, Marian Anghel, Cristina Garcia Cardona, and Jason David Gans. Prediction of Drug Response in Cancerous Cell Lines Using Machine Learning Algorithms. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1396149.
Full textMarusich, Julie, Timothy Lefever, Scott Novak, Bruce Blough, and Jenny Wiley. Prediction and Prevention of Prescription Drug Abuse: Role of Preclinical Assessment of Substance Abuse Liability. Research Triangle Park, NC: RTI Press, July 2013. http://dx.doi.org/10.3768/rtipress.2013.op.0014.1307.
Full textLiu, Xiaopei, Dan Liu, and Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.
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