Logical Fault Detection Approach for Mixed Control Flipping Faults in Reversible Circuits حسب Mousum Handique (21385838)

"…<p>Due to the rapid development of computing machines in terms of micro-architectural designs, millions of transistors are involved per chip, which leads to complex digital circuit design and meets the demands of more computational power. …"

DataSheet1_Multi-omic molecular characterization and diagnostic biomarkers for occult hepatitis B infection and HBsAg-positive hepatitis B infection.docx حسب Xinyi Jiang (1976353)

"…Prognostic biomarkers were identified using machine learning algorithms, and their validity was confirmed in a larger cohort using enzyme linked immunosorbent assay (ELISA).…"

Turkish_native_goat_genotypes حسب Yalçın YAMAN (20209833)

"…</p><p dir="ltr">Nine heterogeneous machine-learning models—including penalised linear methods, gradient-boosting algorithms, and interaction-sensitive tree ensembles—were applied to examine genotype–phenotype relationships. …"

Supporting data for Histone crotonylation is a novel epigenetic regulation and a therapeutic vulnerability for liver cancer treatment حسب Qidong Li (9069932)

"…</p><p><br></p><p dir="ltr">To delve deeper into the molecular mechanisms of histone Kcr in HCC, we employed CUT&Tag sequencing to generate genome-wide profiles of histone Kcr and 13 well-characterized epigenetic markers. Using the ChromHMM machine learning algorithm, we annotated chromatin states based on distinct combinations of these markers. …"

Integrative analysis of mitochondrial and immune pathways in diabetic kidney disease: identification of AASS and CASP3 as key predictors and therapeutic targets حسب Xinxin Yu (528120)

"…</p> <p>WGCNA revealed significant gene modules associated with immune responses and mitochondrial functions. Machine learning analysis highlighted two central biomarkers: aminoadipate-semialdehyde synthase (AASS) and caspase-3 (CASP3). …"

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. حسب Huiwen Li (17705280)

"…</p><p dir="ltr">SOC20 and SOC20-100 maps in the Qinling Mountains with a spatial resolution of 1 km × 1 km during the 1980s were extracted from our previous SOC datasets, which were generated by a machine learning algorithm (Li et al., 2022b). The spatial patterns of the two SOC maps are shown in Fig. 1b. …"

Image2_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif حسب Qingqing Long (845156)

"…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …"

Image1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif حسب Qingqing Long (845156)

"…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …"

DataSheet1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.docx حسب Qingqing Long (845156)

"…A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. …"

Supplementary file 2_Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.docx حسب Jing Mao (148640)

"…Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify co-expressed gene modules related to pemphigus. Machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to select key ferroptosis-related genes. …"

Supplementary file 1_Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.docx حسب Jing Mao (148640)

"…Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify co-expressed gene modules related to pemphigus. Machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to select key ferroptosis-related genes. …"

Supplementary file 3_Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.docx حسب Jing Mao (148640)

"…Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify co-expressed gene modules related to pemphigus. Machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to select key ferroptosis-related genes. …"

Table 1_Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experi... حسب Yaru Shi (2497831)

"…These core intersecting genes were subsequently refined via four complementary machine learning algorithms, KM survival analysis and LASSO Cox regression to establish a prognostic risk score model with predictive value. …"

Table 2_Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experi... حسب Yaru Shi (2497831)

"…These core intersecting genes were subsequently refined via four complementary machine learning algorithms, KM survival analysis and LASSO Cox regression to establish a prognostic risk score model with predictive value. …"

Data Sheet 1_Integrative multi-omics identifies MEIS3 as a diagnostic biomarker and immune modulator in hypertrophic cardiomyopathy.docx حسب Jinchen He (18929662)

"…</p>Methods<p>We performed bulk RNA sequencing on peripheral blood samples from clinically diagnosed HCM patients (n = 4) and matched healthy controls (n = 3), followed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms (LASSO and Random Forest) were used to identify key diagnostic genes. …"

BioSCape Processed Training Dataset حسب Alanna Rebelo (17834777)

"…The dataset was prepared according to the following pre-agreed criteria:</p><ul><li>As many points as possible were collected</li><li>The classes needed to be even (same number of training points) for the machine learning algorithms</li><li>Points didn’t need to be paired (i.e. paired invasive alien tree and fynbos points)</li><li>It was not necessary to collect training data in all sampling units, though a general effort to avoid bias and to sample across different sampling units was attempted</li></ul><p></p>…"

Non-Conjugate Variational Bayes for Pseudo-Likelihood Mixed Effect Models حسب Cristian Castiglione (21656583)

"…<p>We propose a unified, yet simple to code, non-conjugate variational Bayes algorithm for posterior approximation of generic Bayesian generalized mixed effect models. …"

Behavioral raw data recorded by HABITS autonomously حسب bowen yu (19822908)

"…Supported by the microcontroller-based general programming framework, we have not only replicated established paradigms in current neuroscience research but also developed several novel paradigms previously unexplored in mice, resulting in more than 300 mice tested in various cognition functions. Through a machine-teaching approach, HABITS can comprehensively optimize the presentation of stimuli and modalities for trials, leading to more efficient training and higher-quality behavioral outcomes. …"

P values for gene set mRNA enrichment analysis. حسب Wenliang Yuan (7842908)

"…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…"

The result of differential expression analysis. حسب Wenliang Yuan (7842908)

"…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…"