Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics حسب Paolo Cifani (1575613)

"…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …"

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics حسب Paolo Cifani (1575613)

"…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …"

3D visualization of the Rastrigin function from the CEC 2019 benchmark test functions. حسب Mahmood A. Jumaah (22272371)

Comparison of CRLPO and nine selected algorithms on 30 test functions with 30 dimensions from IEEE CEC 2014. حسب Aijun Zhu (822068)

"…<p>Comparison of CRLPO and nine selected algorithms on 30 test functions with 30 dimensions from IEEE CEC 2014.…"

Spreadsheets containing the graph parameters, brain areas and network values. حسب Jafar Zamani (12902504)

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. حسب Kevin Sawade (16726527)

"…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…"

Table_1_Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.docx حسب Baoyu Yan (8628765)

"…However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. …"

Phase diagram for global archetype model as a function of temperature. حسب Giulio Ruffini (383994)

الموضوعات:

Spatial characterization and cell-type-specific genes in mouse brain tissue. حسب Huamei Li (8815955)

Studies with ML classification algorithms for distinguishing ASD and ADHD groups described in subsection 1.2. حسب Caroline L. Alves (14271413)

Vulnerability algorithm from occurrences حسب Arnaud Auber (9084260)

Modeling with uniform <i>σ</i> values. حسب Giacomo Janson (8138517)

Efficient algorithms to discover alterations with complementary functional association in cancer حسب Rebecca Sarto Basso (6728921)

"…In addition, our algorithms are much faster than the state-of-the-art, allowing the analysis of large datasets of thousands of target profiles from cancer cell lines. …"

Binary Networks (Threshold = 0.4) حسب Majid Saberi (11796824)

الموضوعات:

Data_Sheet_1_Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.docx حسب Baoyu Yan (8628765)

"…However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. …"

Model parameter estimates and standard errors for pooled-ERGM analysis of brain functional connectivity networks. حسب Fan Yin (170408)

MAP estimates and posterior standard deviations, conjugate Bayesian analysis of brain functional connectivity networks. حسب Fan Yin (170408)

DataSheet_1_Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.pdf حسب Tingting Zhang (264633)

"…In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01–0.08 Hz; slow-4: 0.027–0.08 Hz; slow-5: 0.01–0.027 Hz) at Rest. …"

DataSheet_1_Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.pdf حسب Tingting Zhang (264633)

"…In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01–0.08 Hz; slow-4: 0.027–0.08 Hz; slow-5: 0.01–0.027 Hz) at Rest. …"

BNA Neuromarker scores as a function of LEDD. حسب Sharon Hassin-Baer (420354)

الموضوعات: