Friedman average rank sum test results. by Chenyi Zhu (9383370)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

Raw Data for the Thesis: "<i>Enhancing RNAi-Based Pest Control through Effective Target Gene Selection and Optimal dsRNA Design</i>" by Doga CEDDEN (12675286)

“…</p><p><br></p><p dir="ltr">Chapter 4 introduces the dsRIP web platform (<a href="https://dsrip.uni-goettingen.de/" target="_blank">https://dsrip.uni-goettingen.de/</a>) for designing sequence-optimized dsRNA for RNAi-based pest control. In the experimental part, small interfering RNA (siRNA) features that were associated with RNAi efficacy in human cells were tested in <i>T. castaneum </i>by targeting an essential gene and measuring insecticidal efficacy. …”

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i>_<i>δ</i>) based on this metric. …”

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

“…Most importantly, this knowledge can be used to discover drugs’ mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. …”

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

“…Most importantly, this knowledge can be used to discover drugs’ mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. …”

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

“…Most importantly, this knowledge can be used to discover drugs’ mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. …”

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

“…P <0.05 was considered statistically significant. (B). The MCDM algorithm-Stage 2. Feature Ranking, this stage is the process of using the TOPSIS method to rank features. …”

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. by Linus Woitke (22783534)

DataSheet_1_Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.pdf by Ashleigh van Heerden (11041338)

“…<p>The rapid development of antimalarial resistance motivates the continued search for novel compounds with a mode of action (MoA) different to current antimalarials. Phenotypic screening has delivered thousands of promising hit compounds without prior knowledge of the compounds’ exact target or MoA. …”

DataSheet1_Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning.zip by Xingkai Wang (13861133)

“…Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. …”

Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

“…In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. …”

Data_Sheet_1_Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint.docx by Xue Li (285380)

Table 1_Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma.docx by Fangmin Zhong (17415318)

“…Prognostic models were developed and optimized via 10 machine learning algorithms with 10-fold cross-validation. …”