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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells 1

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丛书名： Springer Theses

1000年

作者： Iman Tavassoly

ISBN: (数字)9783319149622

ISBN: (纸本)9783319149615;9783319365121

This interdisciplinary thesis introduces a systems biology approach to study the cell fate decision mediated by autophagy. A mathematical model of interaction between Autophagy and Apoptosis in mammalian cells is proposed. In this dynamic model autophagy acts as a gradual response to stress (Rheostat) that delays the initiation of bistable switch of apoptosis to give the cells an opportunity to survive. The author shows that his dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells, he has provided a systems biology experimental framework to study quantitative and dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative fluorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells.

关键词： Biomaterials Computational Biology/bioinformatics Cancer Research Cell Biology Biological and Medical Physics, Biophysics

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MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

arXiv

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arXiv 2025年

作者： Nielsen, Aleksandra Weronika Manoochehri, Hafez Eslami Zhong, Hua Panwar, Vandana Jarmale, Vipul Jasti, Jay Nourani, Mehrdad Rakheja, Dinesh Brugarolas, James Kapur, Payal Rajaram, Satwik Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center DallasTX United States Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX United States Department of Pathology University of Texas Southwestern Medical Center DallasTX United States Kidney Cancer Program Simmons Comprehensive Cancer Center University of Texas Southwestern Medical Center DallasTX United States University of Texas Southwestern Medical Center DallasTX United States Department of Urology University of Texas Southwestern Medical Center at Dallas DallasTX United States

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology. © 2025, CC BY-NC-ND.

关键词： Tumors

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Mapping short sequencing reads to distant relatives 11

Mapping short sequencing reads to distant relatives

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2011 ACM Conference on bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011

作者： Reynoso, Vinicio Putonti, Catherine Department of Biology Bioinformatics Program Loyola University Chicago 1032 W Sheridan Rd. Chicago IL 60660 United States Department of Computer Science Bioinformatics Program Loyola University Chicago 1032 W Sheridan Rd. Chicago IL 60660 United States

ISBN: (纸本)9781450307963

Numerous different algorithmic approaches have been developed to map the short-reads produced by next-generation sequencing technologies onto reference genome sequences. When sufficiently close reference genomes do not exist, less rigorous approaches must be taken, as is the case for analysis of diverse environmental samples. We have developed a new suite of data structures and algorithms specifically for the mapping of reads from environmental sequencing projects. A pipeline was developed which can rigorously map reads to genomes with many mismatches between the two. Using 50+ million reads generated from soil samples, we present the results of our performance analysis of our approach. Copyright © 2011 ACM.

关键词： Mapping

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Acute and chronic post-COVID-19 conditions: A study of genetic integrity and clinical markers

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Mutation Research - Genetic Toxicology and Environmental Mutagenesis 2025年 904卷

作者： Martins, Bruna Alves Alonso Garcia, Ana Leticia Hilario Borges, Malu Siqueira Nobles, Daiane Dias Ribeiro Hansen, Alana Witt Spilki, Fernando Rosado Schuler-Faccini, Lavínia Rampelotto, Pabulo Henrique da Silva, Juliana Graduate Program in Genetics and Molecular Biology Federal University of Rio Grande do Sul (UFRGS) Porto Alegre 91501-970 Brazil Laboratory of Genetic Toxicology La Salle University (UniLaSalle) and Lutheran University of Brazil (ULBRA) Canoas 92010-000 Brazil Bioinformatics and Biostatistics Core Facility Institute of Basic Health Sciences Federal University of Rio Grande do Sul (UFRGS) Porto Alegre 91501-970 Brazil University Feevale Institute Ciencias Saude Molecular Microbiology Lab Novo Hamburgo 93525-075 Brazil

The long-term effects of COVID-19 infection on genomic integrity, along with hematological, biochemical, and inflammatory, remain poorly understood. Viral infections, including SARS-CoV-2, are known to induce genomic instability, potentially contributing to the persistence of post-COVID-19 symptoms. This study aimed to assess genomic instability in individuals with acute and chronic post-COVID-19 conditions, alongside hematological profiles, metabolic parameters, and inflammatory markers, compared to a SARS-CoV-2-negative control group. Participants (n = 231) from southern Brazil were stratified into acute post-COVID (n = 78), chronic post-COVID (n = 79), and control groups (n = 74). DNA damage was assessed using alkaline and enzyme-modified comet assays. Oxidative lesions were detected across all groups, but no significant differences were observed among them. Correlations with biochemical markers suggest inflammation and oxidative stress as central mechanisms in post-COVID-19 pathophysiology. Hematological and biochemical analyses revealed persistent inflammation, lipid metabolism disruptions, and gender-specific alterations, such as higher levels of inflammatory markers (C-reactive protein and ferritin) and lipid abnormalities in men, whereas women exhibited distinct hematological patterns. Age-related influences on metabolic and inflammatory markers further illustrate the systemic complexity of post-COVID-19 effects. The chronic group exhibited ongoing but attenuated markers of inflammation and oxidative stress compared to the acute group. These findings suggest that genetic instability alone may not fully explain the observed clinical manifestations, emphasizing the role of persistent inflammation and metabolic dysregulation. This study provides a comprehensive view of the interplay between genomic instability, inflammation, oxidative damage, and systemic alterations in post-COVID-19 condition. It underscores the importance of a multifaceted approach to underst

关键词： Biochemical analyses DNA damage Long COVID Post-COVID SARS-CoV-2 Symptoms

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Evolutionary computation and swarm intelligence for the inference of gene regulatory networks

Evolutionary computation and swarm intelligence for the infe...

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作者： Hattori, Leandro Takeshi Lopes, Heitor Silvério Lopes, Fabrício Martins Graduate Program in Electrical and Computer Engineering Federal University of Technology ParanáCuritiba Brazil Graduate Program in Bioinformatics Federal University of Technology ParanáCornélio Procópio Brazil

The inference of gene regulatory networks (GRNs) from expression profiles is still an important challenge in bioinformatics research. The main difficulty of this problem is associated to the huge number of genes and the small number of samples available, as well as the intrinsic noise in the data acquisition process. In this context, this paper presents a feature selection approach to the identification of GRNs using optimisation strategies from evolutionary computation and swarm intelligence. As a case-study we used an artificial gene network (AGN) based on the scale-free topology. This AGN has 1,000 genes and was simulated with 500 temporal expression samples. The methods compared were: differential evolution (DE), bat algorithm (BAT) and artificial bee colony (ABC) algorithms. All algorithms used their standard control parameters and the same criterion function: the mean conditional entropy (MCE). This is an information theory measure, commonly adopted for various feature selection problems in the pattern recognition research field. The results showed that DE algorithm leaded to the best results than BAT and ABC in all comparisons, and the inferred network was more similar to the original network. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Evolutionary algorithms

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Regulation of the SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE genes/microRNA 156 Module by the Homeodomain Proteins PENNYWISE and POUND-FOOLISH in Arabidopsis

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Molecular Plant 2011年第6期4卷 1123-1132页

作者： Shruti Lal Leo Bryan Pacis Harley M.S. Smith Genetics Genomics and Bioinformatics Graduate Program University of California Riverside CA 92521 USA Center for Plant Cell Biology Institute of Integrative Genome Biology Department of Botany and Plant Sciences University of California Riverside CA 92521 USA

The morphology of inflorescences is regulated in part by the temporal and spatial events that regulate flower specification. In Arabidopsis, an endogenous flowering time pathway mediated by a subset of SQUAMOSA PROMOTER- BINDING PROTEIN-LIKE （SPL） transcription factors, including SPL3, SPL4, and SPL5, function to specify flowers by activating floral meristem identity genes. During shoot development, SPL3, SPL4, and SPL5 are post-transcriptionally regulated by microRNA156 （miR156）. The photoperiod regulated florigenic signal, FLOWERING LOCUS T （FT）, promotes floral induction, in part by activating SPL3, SPL4, and SPL5. In turn, these SPLs function in parallel with FT to specify flower meristems. Two related BELLl-like homeobox genes PENNYWISE （PNY） and POUND-FOOLISH （PNF） expressed in the shoot apical meristem are absolutely required for the specification of floral meristems. Genetic studies show that the floral specification function of FT depends upon PNYand PNF; however, the interplay between these homeodomain proteins and SPLs is not known. In this manuscript, we show that the photoperiodic floral induction of SPL3, SPL4, and SPL5 is dependent upon PNY and PNE Further, PNY and PNF also control SPL3, SPL4, and SPL5 expression by negatively regulating miR156. Lastly, ectopic expres- sion of SPL4 partially rescues the pny pnf non-flower-producing phenotype, while overexpression of SPL3 or SPL5 in pny pnf plants was unable to restore flower specification. These results suggest that：（1） SPL3, SPL4, and SPL5 function is dependent upon PNY and PNF, or （2） expression of multiple SPLs is required for floral specification in pny pnf plants.

关键词： Development meristem homeodomain flowering microRNA.

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Variable importance-weighted Random Forests

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Frontiers of Electrical and Electronic Engineering in China 2017年第4期5卷 338-351页

作者： Yiyi Liu Hongyu Zhao Department of Biostatistics School of Public Health Yale University New Haven CT 06511 USA Program of Computational Biology and Bioinformatics Yale University New Haven CT 06511 USA

Background： Random Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest. Methods： We propose variable importance-weighted Random Forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features. Results： We evaluate the performance of our method through comprehensive simulation and real data analyses, for both regression and classification. Compared to the standard Random Forests and the feature elimination Random Forests methods, our proposed method has improved performance in most cases. Conclusions： By incorporating the variable importance scores into the random feature selection step, our method can better utilize more informative features without completely ignoring less informative ones, hence has improved prediction accuracy in the presence of weak signals and large noises. We have implemented an R package ＂viRandomForests＂ based on the original R package ＂randomForest＂ and it can be freely downloaded from http：// ***/software.

关键词： Random Forests variable importance score classification regression

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Uncertainty Quantification and Statistical Inference for Biologically Informed Neural Networks

Uncertainty Quantification and Statistical Inference for Bio...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

In precision medicine, deep learning models play an important role in identifying relevant biomarkers and making predictions for the early diagnosis and prognosis of diseases. While successful in developing accurate prediction models for prognosis and diagnosis, the 'black box' nature of deep learning poses significant challenges, which makes it difficult to have a meaningful biological interpretation. Recently, several biologically informed neural networks (BINNs), such as the Visible Neural Network and P-NET, have been proposed to incorporate biological knowledge for improved interpretation. These models assess node importance using quantitative measures such as SHAP and DeepLIFT. However, these approaches have limitations because they typically provide a single estimate of node importance without any information on the uncertainty of these estimates. This study aims to assess uncertainty of node importance through a novel statistical framework. Bootstrapping is used to calculate confidence interval of node importance. Our findings validate that the proposed statistical framework quantifies the uncertainty of nodes in BINNs. By providing robust confidence intervals of pathways and genes, this framework enhances the interpretability and reliability of BINNs. © 2024 IEEE.

关键词： Neural network models

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