Ten key risk factors for C-section delivery were identified by three feature selection techniques. by Shamsuz Zoha (22339469)

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Experimental task and basic behavior. by Gota Morishita (22806463)

“…(b) Partner Selection task. Participants chose between two potential partners they had observed in the Passive Observation task as the partner with whom they would perform the Observational Learning task. …”

Feature importance of risk factors based on SHAP values, (a) Mean absolute SHAP values (b) Local explanation summary. by Shamsuz Zoha (22339469)

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Performance metrics of machine learning models for predicting C-section delivery. by Shamsuz Zoha (22339469)

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Characteristics of the study participants. by Shamsuz Zoha (22339469)

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ROC curves of machine leaning models for predicting C-section delivery. by Shamsuz Zoha (22339469)

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An overview of the comprehensive workflow followed in this study. by Shamsuz Zoha (22339469)

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Class distribution of delivery modes full, before and after dataset partitioning. by Shamsuz Zoha (22339469)

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Code. by Shamsuz Zoha (22339469)

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pgph.0005494.t005 - by Shamsuz Zoha (22339469)

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Hyper parameters tuning of different classifiers using GridSearchCV. by Shamsuz Zoha (22339469)

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Proposed molecular algorithm following a complexity-increasing order of techniques for the assessment of molecular alterations in PTC in a public hospital from a developing country... by Sandra Lorena Colli (12334315)

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling by Kathryn Woods (4689739)

“…We introduce our 2^nd generation (gen) kinobead competition and correlation analysis (kiCCA) for kinome PPI mapping. 2^nd gen kiCCA utilizes kinome affinity purification with kinase inhibitor soluble competition, data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS), and a redesigned CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. …”

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling by Kathryn Woods (4689739)

“…We introduce our 2^nd generation (gen) kinobead competition and correlation analysis (kiCCA) for kinome PPI mapping. 2^nd gen kiCCA utilizes kinome affinity purification with kinase inhibitor soluble competition, data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS), and a redesigned CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. …”

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling by Kathryn Woods (4689739)

“…We introduce our 2^nd generation (gen) kinobead competition and correlation analysis (kiCCA) for kinome PPI mapping. 2^nd gen kiCCA utilizes kinome affinity purification with kinase inhibitor soluble competition, data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS), and a redesigned CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD038236. …”