Identification and enrichment analysis of the TMGs. by Ravinder Sharma (13154079)

Incremental Inverse Design of Desired Soybean Phenotypes by Joseph Zavorskas (19761296)

“…The limitations of inverse design in genotype-to-bulk phenotype (G-BP) mapping can be addressed via an established design paradigm: “design, build, test, learn” (DBTL), where computational inverse design automates both the design and learn phases. In any context, inverse design is limited by the fundamental “one-to-many” nature of the inverse function. …”

Effect of starting random seed on RfaH simulation results. by Lu Hong (399882)

Changes in anesthesia level lead to transitions from high frequency asynchronous to low frequency synchronous spiking patterns. by Bolaji P. Eniwaye (12915339)

Hierarchical regression results. by Muna Saleh (10775575)

“…While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. …”

SHAP values (impact on model output). by Muna Saleh (10775575)

“…While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. …”

Variable importance for the fitted XGBoost model. by Muna Saleh (10775575)

“…While our linear model did not reveal a statistically significant association between child mental health and family functioning, results from XGBOOST highlight the substantial importance of family functioning in contributing to child depressive symptoms. …”

Integration of multiple models through evolutionary multiobjective optimization improves RfaH sequence design outcomes. by Lu Hong (399882)

“…<p>Each column represents one genetic algorithm setup, indicated by the legend above each column that describes how, at each iteration, the subset of designable positions is selected, how the mutations are proposed, and which objective functions are used to score the designs; here, RND stands for random, pMPNN stands for ProteinMPNN, and ESM refers to ESM-1v. …”

Integration of multiple models into RfaH sequence design leads to reduction of sequence bias and variance. by Lu Hong (399882)

“…The fourth panel on the right shows the empirical cumulative distribution functions (eCDF) for the per-position sequence entropy (base <i>e</i>) of these populations of designed sequences. …”

Data_Sheet_1_Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence.pdf by Mónica López-Vicente (3980462)

“…In contrast, dynamic approaches offer a more comprehensive representation of functional connectivity by allowing for different connectivity configurations (time varying connectivity) throughout the scanning session. …”

UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

Parameter optimization for simulated anesthetic concentrations when performed on 10 different network realizations. by Bolaji P. Eniwaye (12915339)

Parameter values for best fit of simulated anesthesia and cholinergic reversal. by Bolaji P. Eniwaye (12915339)

Structural models of CaM. by Lu Hong (399882)

“…<div><p>With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. …”

PapD sequences redesigned by pMPNN-AD are enriched in basic residues at L107. by Lu Hong (399882)

Network propagation improves the correlation between mRNA and protein levels of ageing tissues (brain and liver) and PCa samples. by Konstantina Charmpi (9901053)

Stipulating positional constraints to design bacterial promoters. by Virgile Andreani (17087764)

Supervised Predictive Modeling of High-dimensional Data with Group l0-norm Constrained Neural Networks by Zhihuang Yang (22530437)

“…Convergence guarantees and sparsity recovery capabilities of these algorithms are rigorously examined. Empirical validation through a series of comprehensive experiments conducted on both real-world and synthetic datasets underscores the superior performance of our proposed estimator. …”

General architecture of Model2 and Model3. by Robin Dirk (17296179)

Model 2.2D and 2.3D outperform human expert predictions. by Robin Dirk (17296179)