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

“…Even without providing high-resolution information, it is especially useful when NMR, X-ray, or other techniques are problematic or one simply needs a fast technique to verify the structure of proteins. Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. …”

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

“…Even without providing high-resolution information, it is especially useful when NMR, X-ray, or other techniques are problematic or one simply needs a fast technique to verify the structure of proteins. Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. …”

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

“…Even without providing high-resolution information, it is especially useful when NMR, X-ray, or other techniques are problematic or one simply needs a fast technique to verify the structure of proteins. Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. …”

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

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

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

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

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

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

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

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

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)

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. …”

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

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. …”