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Docking of Human Band 3 Anion Transporter Proteins with Their Plasmodium falciparum Interactors Based on Short Linear Motifs

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American Journal of Molecular Biology 2024年第4期14卷 187-200页

作者： Fatoumata Gniné Fofana Ayoub Ksouri Cheickna Cisse Oussema Souiai Alia Benkahla Hedmon Okella Mamadou Sangare Jeffrey G. Shaffer Seydou Doumbia Mamadou Wele African Center of Excellence in Bioinformatics University of Sciences Techniques and Technologies of Bamako (USTTB) Bamako Mali Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS) Institut Pasteur de Tunis University of Tunis El Manar Tunis Tunisia School of Veterinary Medicine University of California Davis Tulare USA Department of Biostatistics and Data Science Tulane University School of Public Health and Tropical Medicine New Orleans USA University Clinical Research Centre (UCRC) USTTB Bamako Mali

Plasmodium (P.) falciparum is a pathogen that causes severe forms of malaria. Protein interactions have been shown to occur between P. falciparum and human erythrocytes in human blood. The Band 3 Anion Transporter (B3AT) protein is considered the main invasive pathway for the parasite in erythrocytes that causes clinical symptoms for malaria in humans. The interactions between P. falciparum parasites and erythrocytes along this receptor have previously been explored. Short linear motifs (SLIMs) are short linear mediator sequences that involve several biological processes, acting as mediators of protein interactions identifiable by computational tools such as SLiMFinder. For a given protein, the identification of SLIMs allows predicting its interactors. Using the SLIMs approach, protein-protein interaction network analyses between P. falciparum and its human host, were used to identify a tryptophan-rich protein, A5K5E5_PLAVS as an essential interactor of B3AT. To better understand the interaction mechanism, a guided protein-protein docking approach based on SLIM motifs was performed for human B3AT and A5K5E5_PLAVS. The highlights of this important interaction between P. falciparum and its human host have the potential to pave the way to identify new therapeutic candidates.

关键词： Malaria Protein-Protein Docking Protein-Protein Interaction Short Linear Motifs

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Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes with Ordinal Outcomes

arXiv

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arXiv 2025年

作者： Wang, Xinru Chakraborty, Tanujit Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore School of Data Science and Engineering Sorbonne University Abu Dhabi Abu Dhabi United Arab Emirates Sorbonne Center For Artificial Intelligence Sorbonne University Paris France Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University Durham United States

Dynamic treatment regimes (DTRs) are sequences of decision rules designed to tailor treatment based on patients’ treatment history and evolving disease status. Ordinal outcomes frequently serve as primary endpoints in clinical trials and observational studies. However, constructing optimal DTRs for ordinal outcomes has been underexplored. This paper introduces a Bayesian machine learning (BML) framework to handle ordinal outcomes in the DTR setting. To deal with potential nonlinear associations between outcomes and predictors, we first introduce ordinal Bayesian additive regression trees (OBART), a new model that integrates the latent variable framework within the traditional Bayesian additive regression trees (BART). We then incorporate OBART into the BML to estimate optimal DTRs based on ordinal data and quantify the associated uncertainties. Extensive simulation studies are conducted to evaluate the performance of the proposed approach against existing methods. We demonstrate the application of the proposed approach using data from a smoking cessation trial and provide the OBART R package along with R code for implementation. © 2025, CC BY.

关键词： Adversarial machine learning

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression

Systematic Literature Review of Transformer Model Implementa...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Kenjovan Nanggala Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities.

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scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data (vol 15, 1629, 2024)

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NATURE COMMUNICATIONS 2024年第1期15卷 1-16页

作者： Tang, Songming Cui, Xuejian Wang, Rongxiang Li, Sijie Li, Siyu Huang, Xin Chen, Shengquan School of Mathematical Sciences and LPMC Nankai University Tianjin 300071 China MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST Department of Automation Tsinghua University 100084 Beijing China Department of Computer Science University of Virginia Charlottesville VA 22903 USA School of Statistics and Data Science Nankai University Tianjin 300071 China Beijing Key Laboratory for Radiobiology Department of Radiation Biology Beijing Institute of Radiation Medicine 100850 Beijing China

Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement performance.

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review

Pap Smear Medical Image Classification Using Deep Learning: ...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Elvan Selvano Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability.

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging

A Systematic Literature Review of Deep Learning Application ...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Philips Bens Pardamean Gregorius Natanael Elwirehardja Mahmud Isnan Computer Science Department BINUS Graduate Program - Master of Compuer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance.

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review

Exploring Recurrent Neural Network Models for Depression Det...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Brilyan Nathanael Rumahorbo Bens Pardamean Gregorius Natanael Elwirehardja Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD.

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Asymptotics of Bayesian uncertainty estimation in random features regression 23

Asymptotics of Bayesian uncertainty estimation in random fea...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Youngsoo Baek Samuel I. Berchuck Sayan Mukherjee Department of Statistical Science Duke University Durham NC Department of Biostatistics & Bioinformatics Duke University Durham NC Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig

In this paper, we compare and contrast the behavior of the posterior predictive distribution to the risk of the maximum a posteriori (MAP) estimator for the random features regression model in the overparameterized regime. We will focus on the variance of the posterior predictive distribution (Bayesian model average) and compare its asymptotics to that of the risk of the MAP estimator. In the regime where the model dimensions grow faster than any constant multiple of the number of samples, asymptotic agreement between these two quantities is governed by the phase transition in the signal-to-noise ratio. They also asymptotically agree with each other when the number of samples grows faster than any constant multiple of model dimensions. Numerical simulations illustrate finer distributional properties of the two quantities for finite dimensions. We conjecture they have Gaussian fluctuations and exhibit similar properties as found by previous authors in a Gaussian sequence model, which is of independent theoretical interest.

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TENSOR-ON-TENSOR REGRESSION: RIEMANNIAN OPTIMIZATION, OVER-PARAMETERIZATION, STATISTICAL-COMPUTATIONAL GAP, AND THEIR INTERPLAY

arXiv

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arXiv 2022年

作者： Luo, Yuetian Zhang, Anru R. Data Science Institute University of Chicago United States Department of Biostatistics & Bioinformatics Department of Computer Science Duke University United States

We study the tensor-on-tensor regression, where the goal is to connect tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without prior knowledge of its intrinsic rank. We propose the Riemannian gradient descent (RGD) and Riemannian Gauss-Newton (RGN) methods and cope with the challenge of unknown rank by studying the effect of rank over-parameterization. We provide the first convergence guarantee for the general tensor-on-tensor regression by showing that RGD and RGN respectively converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized settings. Our theory reveals an intriguing phenomenon: Riemannian optimization methods naturally adapt to over-parameterization without modifications to their implementation. We also prove the statistical-computational gap in scalar-on-tensor regression by a direct low-degree polynomial argument. Our theory demonstrates a "blessing of statistical-computational gap" phenomenon: in a wide range of scenarios in tensor-on-tensor regression for tensors of order three or higher, the computationally required sample size matches what is needed by moderate rank over-parameterization when considering computationally feasible estimators, while there are no such benefits in the matrix settings. This shows moderate rank over-parameterization is essentially "cost-free" in terms of sample size in tensor-on-tensor regression of order three or higher. Finally, we conduct simulation studies to show the advantages of our proposed methods and to corroborate our theoretical findings. © 2022, CC BY.

关键词： Tensors

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method

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Procedia Computer Science 2023年 216卷 136-143页

作者： Muhammad Titan Hafizal Dian Pratama Putra Herry Wirianata Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Faculty of Forestry STIPER Institute of Agriculture Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants’ condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-to-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants.

关键词： agriculture cocoa expert system forward chaining recommendation system

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