The results of fitting a phenomenological spiking neuronal model (the adaptive exponential integrate-and-fire model) to capture experimental recordings with multiple traces. حسب Máté Mohácsi (20469514)

Overall rankings of optimization algorithms. حسب Máté Mohácsi (20469514)

Variable selection by RFE. حسب Xiaoli Shen (2208601)

De Novo Inverse Design Superhard C–N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm حسب Guanjian Cheng (21176131)

"…Through multiple searches, 38 novel and stable C–N superhard materials not present in major computational materials databases were identified. …"

Construction and validation of the risk model based on MRGs. حسب Xuefen Lin (17785367)

Establishment of a senescence-related graft survival prognostic model. حسب Xihao Shen (20347942)

Construction of non-invasive diagnostic model and nomogram of AR. حسب Xihao Shen (20347942)

Features of 13 pSRGs obtained by machine learning methods. حسب Xihao Shen (20347942)

Overview of the TIPP pipeline. حسب Chengze Shen (17601731)

Supplementary file 3_Optimising the selection of welfare indicators in farm animals.docx حسب Jon Day (19128586)

"…</p>Discussion<p>The enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. …"

Supplementary file 2_Optimising the selection of welfare indicators in farm animals.docx حسب Jon Day (19128586)

"…</p>Discussion<p>The enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. …"

Supplementary file 6_Optimising the selection of welfare indicators in farm animals.docx حسب Jon Day (19128586)

"…</p>Discussion<p>The enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. …"

Supplementary file 1_Optimising the selection of welfare indicators in farm animals.docx حسب Jon Day (19128586)

"…</p>Discussion<p>The enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. …"

Supplementary file 4_Optimising the selection of welfare indicators in farm animals.docx حسب Jon Day (19128586)

"…</p>Discussion<p>The enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. …"

Supplementary file 5_Optimising the selection of welfare indicators in farm animals.docx حسب Jon Day (19128586)

"…</p>Discussion<p>The enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. …"

The operating principle of APDCA. حسب Yangsong He (13909371)

Data Sheet 1_Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform.pdf حسب Jan Benedict Spannenkrebs (20595722)

"…As a target product the readily measurable green fluorescent reporter protein (GFP) is produced over multiple, consecutive iterations of testing. An evaluation of chosen (learning) algorithms for single and dual factor optimization was performed. …"

Data Sheet 2_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip حسب Yonghua Pang (20998022)

"…This study aimed to identify potential gene expression-based biomarkers for the diagnosis of ES.</p>Methods<p>We selected the GSE17679, GSE45544, and GSE68776 datasets from the Gene Expression Omnibus (GEO) database. …"

Data Sheet 1_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip حسب Yonghua Pang (20998022)

"…This study aimed to identify potential gene expression-based biomarkers for the diagnosis of ES.</p>Methods<p>We selected the GSE17679, GSE45544, and GSE68776 datasets from the Gene Expression Omnibus (GEO) database. …"

The possible correlations between the list genes. حسب Ting Yi (2855855)

"…</p><p>Results</p><p>Four AR signature genes (<i>MRPS30</i>, <i>CLPX</i>, <i>MRPL13</i>, and <i>MRPL53</i>) were selected by the MCC, EPC, BottleNeck, and Closeness algorithms. …"