DataSheet2_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF by András Micsonai (801004)

“…<p>Intrinsically disordered proteins lack a stable tertiary structure and form dynamic conformational ensembles due to their characteristic physicochemical properties and amino acid composition. …”

DataSheet3_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.xlsx by András Micsonai (801004)

“…<p>Intrinsically disordered proteins lack a stable tertiary structure and form dynamic conformational ensembles due to their characteristic physicochemical properties and amino acid composition. …”

DataSheet1_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF by András Micsonai (801004)

“…<p>Intrinsically disordered proteins lack a stable tertiary structure and form dynamic conformational ensembles due to their characteristic physicochemical properties and amino acid composition. …”

Presentation_1_Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms.pdf by Dougho Park (9566450)

Model performance and benchmarking for multiclass MOA and 30S/50S protein-synthesis classifiers. by Josh L. Espinoza (10492448)

Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas by Natalie Weed (7345124)

“…To screen for genes that change expression at area borders, we employed a random forest algorithm and binary region classification. …”

DataSheet1_Effective Search of Triterpenes with Anti-HSV-1 Activity Using a Classification Model by Logistic Regression.pdf by Keiko Ogawa (1796347)

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

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

“…<p><b>(A)</b> The SINATRA Pro algorithm requires the following inputs: <i>(i)</i> (<i>x</i>, <i>y</i>, <i>z</i>)-coordinates corresponding to the structural position of each atom in every protein; <i>(ii)</i> <b>y</b>, a binary vector denoting protein class or phenotype (e.g., mutant versus wild-type); <i>(iii)</i> <i>r</i>, the cutoff distance for simplicial construction (i.e., constructing the mesh representation for every protein); <i>(iv)</i> <i>c</i>, the number of cones of directions; <i>(v)</i> <i>d</i>, the number of directions within each cone; <i>(vi)</i> <i>θ</i>, the cap radius used to generate directions in a cone; and <i>(vii)</i> <i>l</i>, the number of sublevel sets (i.e., filtration steps) used to compute the differential Euler characteristic (DEC) curve along a given direction. …”

Data_Sheet_1_Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal.pdf by Kotaro Yamashiro (10502753)

“…Furthermore, we confirmed that our deep learning-based method surpassed classic machine-learning methods in terms of binary classification performance. Combined with the visualization of the hidden layer of our deep learning algorithm, our model provides a new platform for identifying unbiased criteria for cell-type classification.…”

Accessibility of translation initiation sites is the strongest predictor of heterologous protein expression in <i>E. coli</i>. by Bikash K. Bhandari (11524776)

Table1_Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.XLSX by Zhen Shen (393133)

“…</p><p>Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).…”

DataSheet1_Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.PDF by Zhen Shen (393133)

“…</p><p>Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).…”

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

Image_1_A predictive model based on random forest for shoulder-hand syndrome.JPEG by Suli Yu (14947807)

“…</p>Results<p>A binary classification model was trained based on 25 handpicked features. …”

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

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

Structure-based antibody paratope prediction with 3D Zernike descriptors and SVM by Sebastian Daberdaku (4391767)

“…Roto-translationally invariant descriptors are computed from circular patches of the antibody surface enriched with a chosen subset of physicochemical properties from the AAindex1 amino acid index set, and are used as samples for a binary classification problem. …”