Autonomous Greenhouse Challenge, Second Edition (2019) by S. (Silke) Hemming (9171014)

“…The teams developed their own intelligent algorithms and used them to determine the set points for climate, irrigation and a number of cultivation-related parameters and control the production of cherry tomato crop remotely. …”

Data Sheet 1_Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model.pdf by Xiaosong Zhu (11721357)

“…Introduction<p>The thickness of ground substrate in shallow mountainous areas is a crucial indicator for substrate investigations and a key factor in evaluating substrate quality and function. Reliable data acquisition methods are essential for effective investigation.…”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…If you are interested in trying machine learning, the files Features.hdf5 and Target.hdf5 contain the data munged in a convenient form for standard supervised machine learning algorithms.</p><p dir="ltr"><br></p><p dir="ltr">Some large files are in the binary format hdf5 for efficiency in space and read-in. …”

Data_Sheet_1_Ab initio Designed Antimicrobial Peptides Against Gram-Negative Bacteria.docx by Shravani S. Bobde (11711819)

“…We designed eight novel AMPs, termed PHNX peptides, using ab initio computational design (database filtering technology combined with the novel positional analysis on APD3 dataset of AMPs with activity against Gram-negative bacteria) and assessed their theoretical function using published machine learning algorithms, and finally, validated their activity in our laboratory. …”

Image_1_Ab initio Designed Antimicrobial Peptides Against Gram-Negative Bacteria.TIFF by Shravani S. Bobde (11711819)

“…We designed eight novel AMPs, termed PHNX peptides, using ab initio computational design (database filtering technology combined with the novel positional analysis on APD3 dataset of AMPs with activity against Gram-negative bacteria) and assessed their theoretical function using published machine learning algorithms, and finally, validated their activity in our laboratory. …”

<b>Leveraging protected areas for dual goals of biodiversity conservation and zoonotic</b> <b>risk reduction</b> by Li Yang (13558573)

“…Each approach was run using both the Additive Benefit Function (ABF) and Core-Area Zonation (CAZ) algorithms.…”

Data_Sheet_1_Can CHA2DS2-VASc and HAS–BLED Foresee the Presence of Cerebral Microbleeds, Lacunar and Non-Lacunar Infarcts in Elderly Patients With Atrial Fibrillation? Data From St... by Elisa Bianconi (12539614)

“…Our results are a very first step toward the attempt to identify those elderly patients with AF who would benefit most from brain MRI in risk stratification.…”

EXASCALE COMPUTING AND ITS IMPACT ON HIGH-PERFORMANCE COMPUTING by Jace Marden (22025762)

“…Possibilities are still not completely explored, exascale may result in a greater understanding of medical and materials science, more powerful algorithms for artificial intelligence (AI) and Machine Learning (ML), or the ability to create functional mimics of the human brain for neurological and possible cybernetic developments. …”

Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Pyroptosis activity was quantified by five complementary algorithms, while Monocle2 and Slingshot were employed for pseudotime trajectory reconstruction, and SCENIC was applied for transcription factor network analysis. …”

Bootstrap Embedding For Large Molecular Systems by Hong-Zhou Ye (6634700)

“…We introduce several new algorithmic developments that significantly reduce the computational cost of BE, while maintaining its accuracy. …”

Data_Sheet_1_High-Resolution Metagenomics of Human Gut Microbiota Generated by Nanopore and Illumina Hybrid Metagenome Assembly.xlsx by Lianwei Ye (6849110)

“…<p>Metagenome assembly is a core yet methodologically challenging step for taxonomic classification and functional annotation of a microbiome. …”

Data_Sheet_4_High-Resolution Metagenomics of Human Gut Microbiota Generated by Nanopore and Illumina Hybrid Metagenome Assembly.xlsx by Lianwei Ye (6849110)

“…<p>Metagenome assembly is a core yet methodologically challenging step for taxonomic classification and functional annotation of a microbiome. …”

Data_Sheet_2_High-Resolution Metagenomics of Human Gut Microbiota Generated by Nanopore and Illumina Hybrid Metagenome Assembly.xlsx by Lianwei Ye (6849110)

“…<p>Metagenome assembly is a core yet methodologically challenging step for taxonomic classification and functional annotation of a microbiome. …”

Data_Sheet_3_High-Resolution Metagenomics of Human Gut Microbiota Generated by Nanopore and Illumina Hybrid Metagenome Assembly.xlsx by Lianwei Ye (6849110)

“…<p>Metagenome assembly is a core yet methodologically challenging step for taxonomic classification and functional annotation of a microbiome. …”