Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Comparing the performance of One-class classifiers with their respective binary classifier. by Efrat Cohen-Davidi (19485986)

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

Prediction AUC of Strokeformer and the competitor methods on the ten large-scale binary classification datasets. by Maocheng Cao (22127378)

Prediction AUC of Strokeformer and the competitor methods on the nine small-scale binary classification datasets. by Maocheng Cao (22127378)

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

Fig 6 - by Yuqing Lei (11600190)

Fig 2 - by Yuqing Lei (11600190)

Fig 1 - by Yuqing Lei (11600190)

Fig 7 - by Yuqing Lei (11600190)

Fig 5 - by Yuqing Lei (11600190)

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. by Meghana Venkata Palukuri (11890672)

List of network properties used as features. by Anubha Dey (16788919)

“…We developed MAGICAL (<i>Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning</i>), a multi-class random forest based machine learning model for genetic interaction prediction. …”

Results and comparisons for a real biological setting. by Michela C. Massi (14599915)

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)

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

“…These features were selected based on a comprehensive review of toxicological mechanisms associated with metal oxide nanoparticles and their interactions with biological systems.…”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…If you are interested in trying machine learning, the files Features.hdf5 and Target.hdf5 contain the data munged in a convenient form for standard supervised machine learning algorithms.…”

Trained Model by TAPAS CHAKRABORTY (10923396)

“…Most existing computational algorithms model protein interactions as binary relationships, often overlooking the evolutionary regions of protein function and interactions. …”

Data_Sheet_3_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.xlsx by Kirsty E. Waddington (5754545)

“…Furthermore, patients who become ADA+ had a distinct metabolic response to IFNβ in the first 3 months, with 29 differentially regulated metabolites. Machine learning algorithms could also predict ADA status based on metabolite concentrations at 3 months. …”

Image_1_Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ.JPEG by Kirsty E. Waddington (5754545)

“…Furthermore, patients who become ADA+ had a distinct metabolic response to IFNβ in the first 3 months, with 29 differentially regulated metabolites. Machine learning algorithms could also predict ADA status based on metabolite concentrations at 3 months. …”