نتائج البحث

In Silico Modeling of Fabry Disease Pathophysiology for the Identification of Early Cellular Damage Biomarker Candidates.

• Authors : Gervas-Arruga J; Takeda Development Center Americas Inc., Cambridge, MA 02142, USA.; Barba-Romero MÁ

Subjects: Fabry Disease*/Fabry Disease*/Fabry Disease*/metabolism ; Fabry Disease*/Fabry Disease*/Fabry Disease*/pathology ; Biomarkers*

• Source: International journal of molecular sciences [Int J Mol Sci] 2024 Sep 25; Vol. 25 (19). Date of Electronic Publication: 2024 Sep 25.Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067

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SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

• Authors : Gjoni, Ketrin; Pollard, Katherine S

Subjects: Biological Sciences; Bioinformatics and Computational Biology; Genetics

• Source: Bioinformatics. 40(6)

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Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

• Authors : Kress GT; NeuroDetect, Cambridge, MA, USA. .; Chan F

Subjects: Machine Learning* ; Epilepsy*/Epilepsy*/Epilepsy*/diagnosis ; Epilepsy*/Epilepsy*/Epilepsy*/genetics

• Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2022 Nov 09; Vol. 22 (1), pp. 290. Date of Electronic Publication: 2022 Nov 09.Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947

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In silico vaccine design: Targeting highly epitopic regions of Clostridium perfringens type D epsilon toxin and Clostridium novyi type B alpha toxin for optimal immunogenicity

• Authors : Nastaran Ashoori; Mohammad Mehdi Ranjbar; Romana Schirhagl

Subjects: Vaccines; Machine learning; In silico

• Source: Computational and Structural Biotechnology Journal, Vol 25, Iss , Pp 153-164 (2024)

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CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis.

• Authors : Godinez-Macias, Karla; Winzeler, Elizabeth

Subjects: Machine learning; Neglected disease; Scaffold clustering

• Source: Journal of Cheminformatics. 16(1)

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Fused in silico and bioactivity evaluation method for drug discovery: T001-10027877 was identified as an antiproliferative agent that targets EGFRT790M/C797S/L858R and EGFRT790M/L858R.

• Authors : Wang, Linxiao¹ (AUTHOR) ; Huang, Xiaoling¹ (AUTHOR); Xu, Shidi¹ (AUTHOR)

• Source: BMC Chemistry. 8/27/2024, Vol. 18 Issue 1, p1-14. 14p.

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In Silico Screening Accelerates Nanocarrier Design for Efficient mRNA Delivery.

• Authors : Henser‐Brownhill, Tristan¹ (AUTHOR) ; Martin, Liam¹ (AUTHOR); Samangouei, Parisa¹ (AUTHOR)

• Source: Advanced Science. 8/14/2024, Vol. 11 Issue 30, p1-13. 13p.

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Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures.

• Authors : Umemori, Yuki¹ (AUTHOR) ; Handa, Koichi¹ (AUTHOR) ; Yoshimura, Saki¹ (AUTHOR)

• Source: Biomolecules (2218-273X). May2024, Vol. 14 Issue 5, p535. 13p.

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Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.

• Authors : Marques, Lara^1,2,3 (AUTHOR) ; Costa, Bárbara^1,2,3 (AUTHOR) ; Pereira, Mariana^1,2,4 (AUTHOR)

• Source: Pharmaceutics. Mar2024, Vol. 16 Issue 3, p332. 39p.

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Fused in silico and bioactivity evaluation method for drug discovery: T001-10027877 was identified as an antiproliferative agent that targets EGFRT790M/C797S/L858R and EGFRT790M/L858R

• Authors : Linxiao Wang; Xiaoling Huang; Shidi Xu

Subjects: EGFRT790M/C797S/L858R and EGFRT790M/L858R; Machine learning; Virtual screening

• Source: BMC Chemistry, Vol 18, Iss 1, Pp 1-14 (2024)

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