Normalization of calibration data does not significantly affect model calibration results. by Janice Yang (5747246)

Preliminary model calibrations to in vitro data support findings from in silico calibrations. by Janice Yang (5747246)

Ensemble averages of normalized fluorescence intensities of FluoVolt and Cal520 traces from engineered heart tissue samples with different calcium concentrations. by Janice Yang (5747246)

Calibrating parameters to data from the mixed condition protocol improves model predictions of IKr block response. by Janice Yang (5747246)

Pulsed Blue Laser Diode Thermal Desorption Microplasma Imaging Mass Spectrometry by Alexander Knodel (8538882)

Calibrating to both AP and CaT simultaneously improves model predictions of I_Kr block response. by Janice Yang (5747246)

Varying the types and number of experimental conditions has minimal effect on overall calibration error and spread. by Janice Yang (5747246)

Varying the number of experimental conditions affects model predictions, but not calibration error or spread. by Janice Yang (5747246)

MOA classification performance and model benchmarking. by Josh L. Espinoza (10492448)

Unsupervised clustering performance and error profiles of transcriptomes and CoHEC model probability vectors. by Josh L. Espinoza (10492448)

Growth rate comparison between models and experimental results in mono-culture and co-culture. by Elias Vera-Siguenza (16993830)

Posture identification allows the tracking of other cells during late-stage embryogenesis. by Andrew Lauziere (14192836)

Schematic of <i>P. chabaudi</i> within-host infection dynamics and fitness optimization. by Avril Wang (22404300)

“…For local optimization, an arbitrary starting spline is picked (left panel) and an optimization algorithm is used to adjust the relative weights of the basis functions until a fitness maximum is achieved (going from left to right). …”

Cancer cell state map built with XDec-SM deconvolution. by Oscar D. Murillo (11679112)

“…XDec-SM uses an iterative algorithm for constrained matrix factorization using quadratic programming. …”

Data_Sheet_1_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.PDF by Seema Khadirnaikar (9227318)

“…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”

Data_Sheet_2_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.xlsx by Seema Khadirnaikar (9227318)

“…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …”

Indels characterization algorithm development and benchmark with simulated data and real data. by Marta Sanvicente-García (12306659)

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives by Carlos Santiago (13227143)

“…A computational high-throughput screening (cHTS) study of 2-acylpyrroles <b>5a</b>,<b>b</b> has been performed calculating >20,700 activity scores <i>vs</i> a large space of 647 assays involving multiple <i>Leishmania</i> species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. …”

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives by Carlos Santiago (13227143)

“…A computational high-throughput screening (cHTS) study of 2-acylpyrroles <b>5a</b>,<b>b</b> has been performed calculating >20,700 activity scores <i>vs</i> a large space of 647 assays involving multiple <i>Leishmania</i> species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. …”

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives by Carlos Santiago (13227143)

“…A computational high-throughput screening (cHTS) study of 2-acylpyrroles <b>5a</b>,<b>b</b> has been performed calculating >20,700 activity scores <i>vs</i> a large space of 647 assays involving multiple <i>Leishmania</i> species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. …”