Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…No significant difference was found across the 3 methods. (B) However, simulated annealing was the only approach to provide TFs consistent with the nature of biological data (TF with no more than 1 non-derivable point), while both the other deterministic methods run into inconsistent TFs in roughly 60% of the cases.…”

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

Linear relationship between ln(methadone concentration) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Linear relationship between ln(methadone metabolism) and ln(methadone dose) by enantiomer and <i>CYP2B6</i> genotype function. by Andrew H. Talal (8727396)

Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

Automatic cropping algorithm. by Thomas W Georgi (12427189)

Demonstration of the combinatorial Hill functions (A and B), and the regulation function (C and D) under the regulation of an activator and a repressor. by Ruihao Li (2908991)

Equilibrium membrane displacement as a function of active force. by Kai Liu (159026)

The Precision-Recall curve of both the models. by Aastha Vatsyayan (9981916)

Subjects:

Pseudocode of the RPath algorithm. by Daniel Domingo-Fernández (5432273)

“…The function then prioritizes the drug if at least one path is concordant with the observed drug-perturbed transcriptomic signatures (evaluated via <b>Function 1</b>, <i>is_concordant</i>) and the same path is anti-correlated with the observed disease-specific transcriptomic signatures (evaluated via <b>Function 2</b>, <i>is_anti_correlated</i>). …”

Approximate energy consumed per particles in <i>nano Joules</i> (<i>nJ</i>) for various computation platforms and algorithms (A: first algorithm, B: second algorithm, C: third algo... by Abedalmuhdi Almomany (18547669)

Spatial properties of RFs generated by thresholding algorithms and in macaques’ V1 and V2. by Ilias Rentzeperis (10215602)

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. by Arian Ashourvan (6685232)

Reconstruction performance across algorithms, dynamics, cell types, and recording length. by Christian Donner (3663007)

“…<b>F</b> APS and MCC for each type of connectivity, i.e., either excitatory (E, in red), or inhibitory (I, in blue), or a combination of both types (E+I, in black). …”