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Tidal: topology-inferred drug addiction learning
Z Zhu, B Dou, Y Cao, J Jiang, Y Zhu… - Journal of chemical …, 2023 - ACS Publications
Drug addiction is a global public health crisis, and the design of antiaddiction drugs remains
a major challenge due to intricate mechanisms. Since experimental drug screening and …
a major challenge due to intricate mechanisms. Since experimental drug screening and …
Pharmacoprint: A combination of a pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design
D Warszycki, Ł Struski, M Smieja, R Kafel… - Journal of chemical …, 2021 - ACS Publications
Structural fingerprints and pharmacophore modeling are methodologies that have been
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …
Development of predictive models for identifying potential S100A9 inhibitors based on machine learning methods
S100A9 is a potential therapeutic target for various disease including prostate cancer,
colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such …
colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such …
A two-stage feature selection method for power system transient stability status prediction
Z Chen, X Han, C Fan, T Zheng, S Mei - Energies, 2019 - mdpi.com
Transient stability status prediction (TSSP) plays an important role in situational awareness
of power system stability. One of the main challenges of TSSP is the high-dimensional input …
of power system stability. One of the main challenges of TSSP is the high-dimensional input …
Constrained clustering with a complex cluster structure
M Śmieja, M Wiercioch - Advances in Data Analysis and Classification, 2017 - Springer
In this contribution we present a novel constrained clustering method, Constrained
clustering with a complex cluster structure (C4s), which incorporates equivalence …
clustering with a complex cluster structure (C4s), which incorporates equivalence …
Semi-supervised cross-entropy clustering with information bottleneck constraint
In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data
with a set of Gaussian distributions and that retrieves clusters based on a partial labeling …
with a set of Gaussian distributions and that retrieves clusters based on a partial labeling …
MOTiFS: Monte carlo tree search based feature selection
MU Chaudhry, JH Lee - Entropy, 2018 - mdpi.com
Given the increasing size and complexity of datasets needed to train machine learning
algorithms, it is necessary to reduce the number of features required to achieve high …
algorithms, it is necessary to reduce the number of features required to achieve high …
SVM with a neutral class
In many real binary classification problems, in addition to the presence of positive and
negative classes, we are also given the examples of third neutral class, ie, the examples …
negative classes, we are also given the examples of third neutral class, ie, the examples …
Semi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized data sets. We propose a
semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural …
semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural …
Semi-supervised projected model-based clustering
L Guerra, C Bielza, V Robles, P Larrañaga - Data mining and knowledge …, 2014 - Springer
We present an adaptation of model-based clustering for partially labeled data, that is
capable of finding hidden cluster labels. All the originally known and discoverable clusters …
capable of finding hidden cluster labels. All the originally known and discoverable clusters …