Search Results

Development of a machine‐learning model to identify the impacts of pesticides characteristics on soil microbial communities from high‐throughput sequencing data.

• Authors : Ke, Mingjing¹ (AUTHOR); Xu, Nuohan¹ (AUTHOR); Zhang, Zhenyan¹ (AUTHOR)

• Source: Environmental Microbiology. Nov2022, Vol. 24 Issue 11, p5561-5573. 13p.

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Numeric Lyndon-based feature embedding of sequencing reads for machine learning approaches.

• Authors : Bonizzoni, P.¹ (AUTHOR) ; Costantini, M.¹ (AUTHOR) ; De Felice, C.² (AUTHOR)

Subjects: MACHINE learning; PROTEIN structure prediction; NUCLEOTIDE sequencing

• Source: Information Sciences. Aug2022, Vol. 607, p458-476. 19p.

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Machine learning for microfluidic design and control.

• Authors : McIntyre, David^1,2 (AUTHOR); Lashkaripour, Ali^3,4 (AUTHOR); Fordyce, Polly^3,4,5 (AUTHOR)

Subjects: *MICROFLUIDICS; *MACHINE learning; *MICROFLUIDIC devices

• Source: Lab on a Chip. 8/21/2022, Vol. 22 Issue 16, p2925-2937. 13p.

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AutoDC: an automatic machine learning framework for disease classification.

• Authors : Bai, Yang¹ (AUTHOR); Li, Yang¹ (AUTHOR); Shen, Yu¹ (AUTHOR)

Subjects: *MACHINE learning; *NOSOLOGY; *GENE expression

• Source: Bioinformatics. Jul2022, Vol. 38 Issue 13, p3415-3421. 7p.

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Using random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan.

• Authors : Wang, Chia-Chi; Hung, Yu-Ting; Chou, Che-Yu

Subjects: RANDOM forest algorithms; NUCLEOTIDE sequencing; MACHINE learningTAIWANWORLD Health Organization

• Source: Veterinary Research; 2/6/2023, Vol. 54 Issue 1, p1-10, 10p

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Applications of artificial intelligence multiomics in precision oncology.

• Authors : Srivastava, Ruby

Subjects: ARTIFICIAL intelligence; MULTIOMICS; MACHINE learning

• Source: Journal of Cancer Research & Clinical Oncology; Jan2023, Vol. 149 Issue 1, p503-510, 8p

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EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning.

• Authors : Zhou, Bailing; Ding, Maolin; Feng, Jing

Subjects: WEB servers; DEEP learning; LINCRNA

• Source: Briefings in Bioinformatics; Jan2023, Vol. 24 Issue 1, p1-8, 8p

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BWA-MEME: BWA-MEM emulated with a machine learning approach.

• Authors : Jung, Youngmok¹ (AUTHOR); Han, Dongsu¹ (AUTHOR)

Subjects: *MACHINE learning; *WEB servers; *NUCLEOTIDE sequencing

• Source: Bioinformatics. May2022, Vol. 38 Issue 9, p2404-2413. 10p.

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machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

• Authors : Sunuwar, Janak¹ (AUTHOR); Azad, Rajeev K¹ (AUTHOR)

Subjects: DRUG resistance in bacteria; MACHINE learning; DRUG resistance

• Source: Briefings in Bioinformatics. Nov2021, Vol. 22 Issue 6, p1-8. 8p.

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KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency.

• Authors : An, Bingxing¹ (AUTHOR); Liang, Mang¹ (AUTHOR); Chang, Tianpeng¹ (AUTHOR)

Subjects: *FORECASTING; MACHINE learning; NUCLEOTIDE sequencing

• Source: Briefings in Bioinformatics. Nov2021, Vol. 22 Issue 6, p1-11. 11p.

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