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Skew-Probabilistic Neural Networks for Learning from Imbalanced data

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arXiv 2023年

作者： Naik, Shraddha M. Chakraborty, Tanujit Panja, Madhurima Hadid, Abdenour Chakraborty, Bibhas Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Sorbonne University Abu Dhabi SAFIR Abu Dhabi United Arab Emirates Duke-NUS Medical School National University of Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University United States

Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for the minority class. This paper introduces an imbalanced data-oriented classifier using probabilistic neural networks (PNN) with a skew-normal kernel function to address this major challenge. PNN is known for providing probabilistic outputs, enabling quantification of prediction confidence, interpretability, and the ability to handle limited data. By leveraging the skew-normal distribution, which offers increased flexibility, particularly for imbalanced and non-symmetric data, our proposed Skew-Probabilistic Neural Networks (SkewPNN) can better represent underlying class densities. Hyperparameter fine-tuning is imperative to optimize the performance of the proposed approach on imbalanced datasets. To this end, we employ a population-based heuristic algorithm, the Bat optimization algorithm, to explore the hyperparameter space effectively. We also prove the statistical consistency of the density estimates, suggesting that the true distribution will be approached smoothly as the sample size increases. Theoretical analysis of the computational complexity of the proposed SkewPNN and BA-SkewPNN is also provided. Numerical simulations have been conducted on different synthetic datasets, comparing various benchmark-imbalanced learners. Real-data analysis on several datasets shows that SkewPNN and BA-SkewPNN substantially outperform most state-of-the-art machine-learning methods for both balanced and imbalanced datasets (binary and multi-class categories) in most experimental settings. © 2023, CC BY.

关键词： Adversarial machine learning

NUMERICAL METHODS FOR COMPUTING THE DISCRETE AND CONTINUOUS LAPLACE TRANSFORMS

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arXiv 2023年

作者： Zhang, Yupeng Shen, Yueyang Zhang, Rongqian Liu, Yuyao Guo, Yunjie Deng, Daxuan Dinov, Ivo D. Statistics Online Computational Resource University of Michigan Ann ArborMI48109 United States Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States Precision Health and Michigan Institute for Data Science University of Michigan Ann ArborMI48109 United States SOCR University of Michigan United States

Overview: Many mathematical models and statistical computing techniques can be significantly simplified by mapping the original problem from its native state space representation into another space via specialized integral transformations. Examples of these include the Laplace transform (LT) and its inverse (ILT), which play vital roles in contemporary data science, probability modeling, and statistical inference. However, there are challenges with computing LT and ILT for some complex functions, noisy data, or incomplete observations. This article reports on a new numerical algorithm for computing the forward and inverse Laplace transforms. The technique is applicable to both continuous functions and discrete signals, and yields computationally efficient and analytically robust results. Methods: We propose a numerical method to spline-interpolate discrete signals and then apply the integral transforms to the corresponding analytical spline functions. This represents a robust and computationally efficient technique for estimating the Laplace transform for noisy data. We revisited a Meijer-G symbolic approach to compute the Laplace transform and alternative approaches to extend canonical observed time-series. A discrete quantization scheme provides the foundation for rapid and reliable estimation of the inverse Laplace transform. We derive theoretic estimates for the inverse Laplace transform of analytic functions and demonstrate empirical results validating the algorithmic performance using observed and simulated data. We also introduce a generalization of the Laplace transform in higher dimensional space-time. Results: We tested the discrete LT algorithm on data sampled from analytic functions with known exact Laplace transforms. The validation of the discrete ILT involves using complex functions with known analytic ILTs. For discrete signals, we also report empirical evidence of the asymptotic behavior of the expectation of the smallest singular value of the LT ma

关键词： Laplace transforms

Synthetic Control Removes Spurious Discoveries from Double Dipping in Single-Cell and Spatial Transcriptomics data Analyses 29th

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Synthetic Control Removes Spurious Discoveries from Double ...

29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Song, Dongyuan Chen, Siqi Lee, Christy Li, Kexin Ge, Xinzhou Li, Jingyi Jessica Department of Genetics and Genome Sciences University of Connecticut Health Center FarmingtonCT06032 United States Interdepartmental Program of Bioinformatics University of California Los Angeles Los AngelesCA90095-7246 United States Department of Statistics and Data Science University of California Los Angeles Los AngelesCA90095-1554 United States Department of Statistics Oregon State University CorvallisOR97331-4606 United States

ISBN: (纸本)9783031902512

Double dipping is a common analytical pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems

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Implementation of Face Patterns and Smile Recognition for In...

International Conference of Computer and Informatics Engineering (IC2IE)

作者： Miftakhurrokhmat Teddy Suparyanto Gregorius Natanael Elwirehardja Shofiyati Nur Karimah Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Informatics Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Students “attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students” attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification.

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Efficient Algorithms for Co-folding of Multiple RNAs 13th

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Efficient Algorithms for Co-folding of Multiple RNAs

13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

作者： Lorenz, Ronny Flamm, Christoph Hofacker, Ivo L. Stadler, Peter F. Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien1090 Austria Bioinformatics and Computational Biology Faculty of Computer Science University of Vienna Währingerstraße 29 Wien1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics and Competence Center for Scalable Data Services and Solutions Dresden/Leipzig Universität Leipzig Härtelstraße 16-18 Leipzig04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

ISBN: (纸本)9783030723781

The simplest class of structures formed by N≥ 2 interacting RNAs consists of all crossing-free base pairs formed over linear arrangements of the constituent RNA sequences. For each permutation of the N strands the structure prediction problem is algorithmically very similar – but not identical – to folding of a single, contiguous RNA. The differences arise from two sources: First, "nicks", i.e., the transitions from one to the next piece of RNA, need to be treated with special care. Second, the connectedness of the structures needs to guaranteed. For the forward recursions, i.e., the computation of folding energies or partition functions, these modifications are rather straightforward and retain the cubic time complexity of the well-known folding algorithms. This is not the case for a straightforward implementation of the corresponding outside recursion, which becomes quartic. Cubic running times, however, can be restored by introducing linear-size auxiliary arrays. Asymptotically, the extra effort over the corresponding algorithms for a single RNA sequence of the same length is negligible in both time and space. An implementation within the framework of the ViennaRNA package conforms to the theoretical performance bounds and provides access to several algorithmic variants, include the handling of user-defined hard and soft constraints. © 2021, Springer Nature Switzerland AG.

关键词： RNA

Decoding the plant genome: From epigenome to 3D organization

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Journal of Genetics and Genomics 2020年第8期47卷 425-434页

作者： Weizhi Ouyang Zhilin Cao Dan Xiong Guoliang Li Xingwang Li National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan430070China Department of Resources and Environment Henan University of EngineeringZhengzhou451191China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data Huazhong Agricultural UniversityWuhan430070China

The linear genome of eukaryotes is partitioned into diverse chromatin states and packaged into a threedimensional(3D)structure,which has functional implications in DNA replication,DNA repair,and transcriptional *** the past decades,research on plant functional genomics and epigenomics has made great progress,with thousands of genes cloned and molecular mechanisms of diverse biological processes ***,3D genome research has gradually attracted great attention of many plant ***,we briefly review the progress in genomic and epigenomic research in plants,with a focus on Arabidopsis and rice,and summarize the currently used technologies and advances in plant 3D genome organization *** also discuss the relationships between onedimensional linear genome sequences,epigenomic states,and the 3D chromatin *** review provides basis for future research on plant 3D genomics.

关键词： Plant genomics Epigenetic modification Chromatin three-dimensional structures Genomic methods

Minimum Φ-distance estimators for finite mixing measures

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arXiv 2023年

作者： Wei, Yun Mukherjee, Sayan Nguyen, XuanLong Department of Statistical Science Duke University NC United States Departments of Mathematics Computer Science Biostatistics & Bioinformatics Duke University NC United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany Department of Statistics University of Michigan Ann ArborMI United States

Finite mixture models have long been used across a variety of fields in engineering and sciences. Recently there has been a great deal of interest in quantifying the convergence behavior of the mixing measure, a fundamental object that encapsulates all unknown parameters in a mixture distribution. In this paper we propose a general framework for estimating the mixing measure arising in finite mixture models, which we term minimum Φ-distance estimators. We establish a general theory for the minimum Φ-distance estimator, where sharp probability bounds are obtained on the estimation error for the mixing measures in terms of the suprema of the associated empirical processes for a suitably chosen function class Φ. Our framework includes several existing and seemingly distinct estimation methods as special cases but also motivates new estimators. For instance, it extends the minimum Kolmogorov-Smirnov distance estimator to the multivariate setting, and it extends the method of moments to cover a broader family of probability kernels beyond the Gaussian. Moreover, it also includes methods that are applicable to complex (e.g., non-Euclidean) observation domains, using tools from reproducing kernel Hilbert spaces. It will be shown that under general conditions the methods achieve optimal rates of estimation under Wasserstein metrics in either minimax or pointwise sense of convergence;the latter case can be achieved when no upper bound on the finite number of components is *** Codes Primary 62H30, 62C20, Secondary 62F12 © 2023, CC BY-NC-ND.

关键词： Method of moments

A Compounded Burr Probability Distribution for Fitting Heavy-Tailed data with Applications to Biological Networks

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arXiv 2024年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Naik, Shraddha M. Hens, Chittaranjan SAFIR Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Paris France Department of Computer Science & Engineering XIM University Bhubaneswar India Department of Data Science SP Jain School of Global Management Mumbai India Department of Computer Science Khalifa University United Arab Emirates Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad India

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein–protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power-law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four-parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks. © 2024, CC BY.

关键词： Maximum likelihood estimation

003 Interleukin (IL)-26 drives pathogenic IL-17A responses through a TH17-keratinocyte crosstalk

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Journal of Investigative Dermatology 2022年第12期142卷 S181-S181页

作者： A. Fries F. Saidoune F. Kuonen C. Conrad I. Dupanloup A. Guerra de Souza N. Fournier R. Gottardo J. Di Domizio M. Gilliet Dermatology University Hospital Lausanne CHUV Lausanne Switzerland Translational Data Science Swiss Institute of Bioinformatics Lausanne Switzerland Biomedical Data Sciences Center CHUV UNIL and Swiss Institute of Bioinformatics Lausanne Switzerland