23 papers · ranked by Valyu relevance
Ari Frenkel, Alicia Rendon, Carlos Chavez-Lencinas, Juan Carlos Gomez De la Torre + 12 more
'Juan Carlos Gomez De la Torre' 'Jen MacDermott' 'Collen Gross' 'Stephanie Allman' 'Sheri Lundblad' 'Ivanna Zavala' 'Dave Gross' 'Jessica Siegel' 'Soojung Choi' 'Miguel Hueda-Zavaleta' 'Milan Kolář' 'Ju-Seop Kang' 'Yudong Cai'] Background: Antimicrobial stewardship programs (ASPs) are essential in combating…
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them. In such a setting, one faces the problem of whether the fixed budget for obtaining training examples should rather be used for obtaining all…
Naghmeh Pakgohar, Attila Lengyel, Zoltán Botta-Dukát
Different clustering methods often classify the same dataset differently. Selecting the ‘best’ clustering solution out of a multitude of alternatives is possible with cluster validation indices. The behavior of validity indices changes with the structure of the sample and the properties of the clustering algorithm.…
Zoltán Botta‐Dukát
A vast number of different methods are available for unsupervised classification. Since no algorithm and parameter setting performs best in all types of data, there is a need for cluster validation to select the actually best-performing algorithm. Several indices were proposed for this purpose without using any…
Mohsen Sadatsafavi, Paul Gustafson, Solmaz Setayeshgar, Laure Wynants + 1 more
Contemporary sample size calculations for external validation of risk prediction models require users to specify fixed values of assumed model performance metrics alongside target precision levels (e.g., 95% CI widths). However, due to the finite samples of previous studies, our knowledge of true model performance in…
Shuyue Guan, Murray H. Loew
To evaluate clustering results is a significant part of cluster analysis. Since there are no true class labels for clustering in typical unsupervised learning, many internal cluster validity indices (CVIs), which use predicted labels and data, have been created. Without true labels, to design an effective CVI is as…
Giuseppe Gallitto, Robert Englert, Balint Kincses, Raviteja Kotikalapudi + 4 more
Multivariate predictive models integrate information across multiple variables to construct predictions of a specific outcome and hold promise for delivering more accurate estimates than traditional univariate methods . For instance, when predicting individual behavioral and psychometric characteristics from brain…
Giuseppe Gallitto, Robert Englert, Balint Kincses, Raviteja Kotikalapudi + 4 more
Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can…
Guy Tennenholtz, Tom Zahavy, Shie Mannor
Model selection on validation data is an essential step in machine learning. While the mixing of data between training and validation is considered taboo, practitioners often violate it to increase performance. Here, we offer a simple, practical method for using the validation set for training, which allows for a…
John P. Efromson, Shuai Li, Michael D. Lynch
Autosampling from bioreactors reduces error, increases reproducibility and offers improved aseptic handling when compared to manual sampling. Additionally, autosampling greatly decreases the hands-on time required for a bioreactor experiment and enables sampling 24 hrs a day. We have designed, built and tested a low…
Yongtian Cheng, Pablo A. Pérez-Díaz, K. V. Petrides, Johnson Li
Monte Carlo simulation is a common method of providing empirical evidence to verify statistics used in psychological studies. A representative set of conditions should be included in simulation studies. However, several recently published Monte Carlo simulation studies have not included the conditions of the null…
Vincenzo Caretti, Eleonora Topino, Andrea Fontana, Gianluigi Di Cesare + 4 more
The internal saboteur may be understood as a multidimensional configuration of maladaptive inner processes involving recurrent negative self-evaluation, distressing relational expectations, repetitive negative thinking, and self-undermining inner experiences. Within this framework, the present study aimed to develop…
Authors not listed
We present graphRC, a graph-based method for rapid transition state (TS) mode analysis that provides chemical insight along normal mode displacements and reaction coordinate trajectories by translating Cartesian displacements into meaningful internal coordinate changes. Internal coordinates are constructed using…
Claude Lambert, Gulderen Yanikkaya Demirel, Thomas Keller, Frank Preijers + 4 more
'Frank Preijers' 'Katherina Psarra' 'Matthias Schiemann' 'Mustafa Özçürümez' 'Ulrich Sack'] Many anticancer therapies such as antibody-based therapies, cellular therapeutics (e.g., genetically modified cells, regulators of cytokine signaling, and signal transduction), and other biologically tailored interventions…
De Lin, Lesley-Anne Pearson, Shamshad Ahmad, Sandra O’Neill + 4 more
False-positives plague High Throughput Screening in general and are costly as they consume resource and time to resolve. Methods that can rapidly identify such compounds at the initial screen are therefore of great value. Advances in mass spectrometry have led to the ability to screen inhibitors in drug discovery…
Mostapha Benhenda
Generating diverse molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. To facilitate evaluation of generative models, this paper introduces a metric of internal chemical diversity, and raises the following challenge: can a…
David Higgins, Christian Johner
The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in-vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to…
Keiko Masuda, Keiko Kasahara, Ryohei Narumi, Masaru Shimojo + 1 more
Preparation of stable isotope-labeled internal standard peptides is crucial for mass spectrometry (MS)-based targeted proteomics. Herein, we developed versatile and multiplexed absolute protein quantification method using MS. A previously developed method based on the cell-free peptide synthesis system, termed MS-based…
Authors not listed
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an essential analytical technique in the pharmaceutical industry, used particularly for elucidating the structure of unknown impurities in the synthesis of active pharmaceutical ingredients. However, the interpretation of mass spectra is challenging and…
Lucy Ellen Lwakatare, Ellinor Rånge, Ivica Crnković, Jan Bosch
—Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training ML models using bad data, research and industrial practice suggest incorporating a data…
Yunsie Chung, William H. Green
Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond + 2 more
Recent applications of Recurrent Neural Networks enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1 % of the…