Single-cell RNA sequencing analysis reveals the expression patterns of the prognostic signature across cellular subpopulations. by Xiangxin Wu (22320058)

Growth rate comparison between models and experimental results in mono-culture and co-culture. by Elias Vera-Siguenza (16993830)

Evidence that WRMF and other matching covariates are proxies for true, underlying preferences. by David Easley (8415075)

Validation of SS-ANN. by Zeke Barger (8124335)

Data_Sheet_1_The Comparison of Three Statistical Models for Syndromic Surveillance in Cattle Using Milk Production Data.PDF by Anouk M. B. Veldhuis (8539296)

Archetype connectivity. by Giulio Ruffini (383994)

Individual shift of Ising temperature (top), LZW (middle), and BDM (bottom) complexity induced by LSD (empirical data). by Giulio Ruffini (383994)

Individual shift of LZW (top row) and BDM (bottom) complexity in LSD and placebo conditions estimated from synthetic data generated by the archetype model. by Giulio Ruffini (383994)

Table 4_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…</p>Results<p>Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”

Table 1_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…</p>Results<p>Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”

Image 1_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.pdf by Wenwu Tang (5493086)

“…</p>Results<p>Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”

Table 2_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…</p>Results<p>Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”

Table 3_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…</p>Results<p>Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”

Table 5_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…</p>Results<p>Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”

The information of datasets used in this study. by Kaiyi Zhou (2553352)

“…</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). …”

The workflow of the present study. by Kaiyi Zhou (2553352)

“…</p><p>Methods</p><p>We utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). …”

Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data - Fig 7 by Qingke Gao (6645740)

Table1_Aortic systolic and pulse pressure invasively and non-invasively obtained: Comparative analysis of recording techniques, arterial sites of measurement, waveform analysis alg... by Daniel Bia (8157270)

“…</p><p>Aim: To evaluate the agreement between aoSBP and aoPP values invasively and non-invasively obtained using different: 1) recording techniques (tonometry, oscilometry/plethysmography, ultrasound), 2) recording sites [radial, brachial (BA) and carotid artery (CCA)], 3) waveform analysis algorithms (e.g., direct analysis of the CCA pulse waveform vs. peripheral waveform analysis using general transfer functions, N-point moving average filters, etc.), 4) calibration schemes (systolic-diastolic calibration vs. methods using BA diastolic and mean blood pressure (bMBP); the latter calculated using different equations vs. measured directly by oscillometry, and 5) different equations to estimate bMBP (i.e., using a form factor of 33% (“033”), 41.2% (“0412”) or 33% corrected for heart rate (“033HR”).…”

Fig 3 - by Valery Kirzhner (331109)

S1 Dataset - by Alexander Stäuber (10353580)

“…Here, we aimed to compare the TTE and oscillometric PWA algorithm Antares for a non-invasive estimation of CO.…”