Performance of HFKG (triplet selection) on multiple clients divided by different datasets. by Chunjuan Li (7890182)

CorrEA: A Web Server for Optimizing Correlations between Calculated Energies and Activities in Ligand–Receptor Systems Considering Multiple Binding Site Conformations by Sergio Alfaro (9765813)

“…CorrEA performs a genetic algorithm (GA) selection to extract a representative complex for each ligand that better adjusts the global correlation between calculated docking energy values and experimental logarithmic biological activities. …”

PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors by Francisca Joseli Freitas de Sousa (22640155)

“…In this study, we developed PI3K-Seeker, a web server based on a two-stage prediction process to find new PI3K inhibitors. …”

PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors by Francisca Joseli Freitas de Sousa (22640155)

“…In this study, we developed PI3K-Seeker, a web server based on a two-stage prediction process to find new PI3K inhibitors. …”

PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors by Francisca Joseli Freitas de Sousa (22640155)

“…In this study, we developed PI3K-Seeker, a web server based on a two-stage prediction process to find new PI3K inhibitors. …”

The process of FL. by Pan Wang (198097)

Table 1 - by Mahsa Alem (22446685)

“…The machine learning prediction algorithm of the studied server classifies positive numbers in the SVM score as IFN-γ cytokine epitope (seventh column of the table) and negative numbers as non-toxins (eighth column). …”

Toward effortless digital histopathology through cloud-based multi virtual staining: Proof-of-concept. by Mehdi Ounissi (22470332)

“…Computations are performed on a back-end server (via slurm), with the user only required to upload the H&E slide and initiate the algorithm through the browser. …”

DataSheet1_Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning.zip by Xingwang Wang (2439220)

“…Finally, the drug-gene interactions were predicted using the DrugRep server.</p>Results<p>Interferon-stimulated gene 15 (ISG15) was identified by intersecting DEGs, advanced machine learning-selected genes and key module genes from WGCNA. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …”

Data Sheet 1_Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destinat... by Mikko Strahlendorff (20458865)

“…The Copernicus Global Land Monitoring Service’s Soil Water Index (SWI) satellite-based observations from 2015 to 2023 at 10,000 locations in Europe were used as the predictand (target parameter) to train an artificial intelligence (AI) model to predict soil wetness with XGBoost (eXtreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) implementations of gradient boosting algorithms. The locations were selected as a representative set of points from the Land Use/Cover Area Frame Survey (LUCAS) sites, which helped evaluate the characteristics of distinct locations used in fitting to represent diverse landscapes across Europe. …”

Processing of Published Data and Construction of the Core UVmap Reference by David Adams (10283936)

“…Annotation of cell types (ACT): a convenient web server for cell type annotation. Genome Med 15, 91 (2023). 12. …”