22 papers · ranked by Valyu relevance
Authors not listed
Deciphering the correct mechanism governing certain phenomenon in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic flow (in the…
Maxat Tezekbayev, Arman Bolatov, Zhenisbek Assylbekov
We revisit Deep Linear Discriminant Analysis (Deep LDA) from a likelihood-based perspective. While classical LDA is a simple Gaussian model with linear decision boundaries, attaching an LDA head to a neural encoder raises the question of how to train the resulting deep classifier by maximum likelihood estimation (MLE).…
Osama Khalifa, David Corne, Mike J. Chantler
Hyper-parameters play a major role in the learning and inference process of latent Dirichlet allocation (LDA). In order to begin the LDA latent variables learning process, these hyperparameters values need to be pre-determined. We propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs Newton'…
Yuri Ahuja, Doudou Zhou, Zeling He, Jiehuan Sun + 5 more
A major bottleneck hindering utilization of electronic health record (EHR) data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to…
Meelad Amouzgar, David R. Glass, Reema Baskar, Inna Averbukh + 4 more
Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction enables visualization of data by representing cells in two-dimensional plots that capture the structure and heterogeneity of the original dataset. Visualizations contribute to human…
Liang Tang, Silong Peng, Yiming Bi, Peng Shan + 2 more
'Lars Kaderali'] Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS)…
P. Celard, A. Seara Vieira, E. L. Iglesias, L. Borrajo + 1 more
'Marco Bonizzoni'] This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over…
Fangyuan Zhao, Xuebin Ren, Shusen Yang, Qing Han + 2 more
'Xinyu Yang'] Abstract—Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various applications. However, the LDA model as well as the training process of LDA may expose the text information in the…
DM Blei, K Franks, MI Jordan, IS Mian
Center Bibliography for genes related to life span Authors: ['DM Blei' 'K Franks' 'MI Jordan' 'IS Mian'] Background The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made from analysis of molecular sequence and profiling data.…
Meelad Amouzgar, David R. Glass, Reema Baskar, Inna Averbukh + 4 more
'Samuel C. Kimmey' 'Albert G. Tsai' 'Felix J. Hartmann' 'Sean C. Bendall'] Title: Summary Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional…
Tomasz Krzeszowski, Krzysztof Wiktorowicz
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to…
Desheng Huang, Yu Quan, Miao He, Baosen Zhou
Background More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA) and its modification methods for…
Tamim Abdelaal, Vincent van Unen, Thomas Höllt, Frits Koning + 2 more
Mass cytometry (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, however, they are time consuming, often involve a manual step, and lack…
Fangyuan Zhao, Xuebin Ren, Shusen Yang, Xinyu Yang
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive…
Yiwei Zhang, Jiawei Han, Tengjun Liu, Zelan Yang + 2 more
Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their…
Authors not listed
The equilibrium binding affinity has traditionally guided the drug discovery. Despite offering insight into the extent of binding, it is occasionally inaccurate in predicting biological response. Emerging evidence suggests that the lifetime of binary complex, known as residence time (RT), is more directly correlated…
Navid Ziaei, Behzad Nazari, Uri T. Eden, Alik S. Widge + 1 more
Decoder (LDGD) Model for High-Dimensional Data Authors: ['Navid Ziaei' 'Behzad Nazari' 'Uri T. Eden' 'Alik S. Widge' 'Ali Yousefi'] Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different…
Gabriela Alexe, Sorin Alexe, David E Axelrod, Tibérius O Bonates + 3 more
'Irina I Lozina' 'Michael Reiss' 'Peter L Hammer'] Introduction The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data…
Abhijit Mahalunkar, John D. Kelleher
In order to build efficient deep recurrent neural architectures, it is essential to analyze the complexity of long distance dependencies (LDDs) of the dataset being modeled. In this context, in this paper, we present detailed analysis of the complexity and the degree of LDDs (or LDD characteristics) exhibited by…
Authors not listed
Drug Discovery is a very lengthy and resource-consuming process. However, a variety of advanced Artificial Intelligence (AI) and Deep Learning (DL) techniques are being utilized to accelerate and advance DD, such as Large Language Models (LLMs). This survey is in aim of discovering and comparing the currently available…
Authors not listed
The rapid advancements in computational methods have revolutionized drug discovery and development. These methods, ranging from molecular modelling to machine learning algorithms, have drastically increased in number and sophistication. However, a comprehensive understanding of these diverse approaches is essential for…
Martin Sicho, Sohvi Luukkonen, Helle van den Maagdenberg, Linde Schoenmaker + 2 more
The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of de novo drug design tools. However, few resources exist that are both user-friendly as well as easily customisable. In this application…