Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

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Performance comparison of feature selection strategies for <i>S</i>. <i>cerevisiae</i> S phosphorylation site. by Chao Wang (146527)

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The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions. by Yang Cao (53545)

“…<p>Wilcoxon’s test results for EBJADE algorithms and other state-of-the-art CEA-ES algorithms using CEC2014 functions.…”

Feature intersection and sequence patterns for <i>S</i>. <i>cerevisiae</i> S site of five fungal species. by Chao Wang (146527)

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Comparison results of four feature selection strategies for <i>S</i>. <i>cerevisiae</i> S phosphorylation site. by Chao Wang (146527)

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Benchmark functions. by Wei Liu (20030)

“…The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.…”

Data_Sheet_1_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.PDF by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Data_Sheet_1_FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.PDF by Hong Gu (68558)

“…Then the estimation of the background distribution and the identification of driver genes were conducted in each cluster obtained by the hierarchical clustering algorithm. We applied FI-net and other 22 state-of-the-art methods to 31 datasets from The Cancer Genome Atlas project. …”

Algorithm description and the effects of replay and forgetting on model performance. by Georgy Antonov (11938961)

“…Right: with MB forgetting (controlled by MB forgetting rate, <i>ϕ</i>^<i>MB</i>), the algorithm’s estimate of reward becomes an expectation of the reward function under its state-transition model. …”

Functional Connectivity for Experiment and Simulated Anesthesia and Reversal. by Bolaji P. Eniwaye (12915339)

Pharmacokinetics model: Schematic of the PINNs algorithm for predicting the unknown term <i>h</i>(<i>t</i>; <i>θ</i>₂) and the values of parameters simultaneously. by Nazanin Ahmadi Daryakenari (18143871)

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Enzymatic reaction networks specified by divisions and distances between the divisions. by Takashi Naka (272391)

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Complex enzymatic reaction networks consisting of post-translational modification reactions. by Takashi Naka (272391)

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Bistable enzymatic reaction networks found in the search. by Takashi Naka (272391)

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The total concentration of enzymes in each enzymatic reaction network found. by Takashi Naka (272391)

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Relationship between bistability and resettability. by Takashi Naka (272391)

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