Table_1_Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.docx by Lizhao Yan (11774354)

“…Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. …”

Image1_Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.eps by Ryszard Kubinski (12105983)

“…In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome’s composition are currently being explored. …”

Dependence of user identification time on the size of model library. by Mukesh Karunanethy (18422857)

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

“…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…”

Fairness in Machine Learning: A Review for Statisticians by Xianwen He (22529252)

“…<p>With the widespread application of machine learning algorithms in daily life, it is crucial to mitigate the risk of these algorithms producing socially undesirable outcomes that may disproportionately disadvantage certain groups or individuals based on demographic characteristics such as gender, race, or disabilities. …”

Parameters of the experiments. by Enrico Zardini (17382523)

“…Regarding the quantum pipeline, it has been developed using Qiskit, and it consists of a quantum <i>k</i>-NN and a quantum binary classifier, both already available in the literature. …”

Quantum pipeline workflow overview. by Enrico Zardini (17382523)

“…Regarding the quantum pipeline, it has been developed using Qiskit, and it consists of a quantum <i>k</i>-NN and a quantum binary classifier, both already available in the literature. …”

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water by Kuan Huang (1504921)

“…Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. …”

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water by Kuan Huang (1504921)

“…Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. …”

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water by Kuan Huang (1504921)

“…Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. …”

Pan-cancer machine learning predictions of MEKi response. by John P. Lloyd (10196288)

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”

AUPRC of the ML models. by Adhiraj Nath (17118269)

“…Currently, there are no existing tool that is exclusively designed for insect pre-microRNA detection.</p><p>Aim</p><p>Application of machine learning algorithms to develop an open source tool for prediction of novel precursor microRNA in insects and search for their miRNA targets in the model insect organism, <i>Drosophila melanogaster</i>.…”

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction by Raul A. Flores (2910539)

“…Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. …”

Image1_Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.TIF by Álmos Orosz (12828104)

Data_Sheet_1_Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach.pdf by Md. Zahangir Alam (12056864)

“…To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%).…”

DataSheet1_Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.docx by Álmos Orosz (12828104)

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)

Supplemental Tables S1 and S2 for Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks by Diana Murray (16859046)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. The dataset was stratified into training (80%) and testing (20%) sets. …”