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ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Tangherloni, Andrea Riva, Simone G. Myers, Brynelle Cazzaniga, Paolo Univ Bergamo Dept Human & Social Sci Bergamo Italy Weatherall Inst Mol Med Radcliffe Dept Med Oxford England Univ Oxford Wellcome Ctr Human Genet Oxford England Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Milan Italy

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

The computational analyses of single-cell data, aimed at elucidating and characterizing the functional roles of known and putative novel cell types, are enabling a thorough understanding of the processes driving cell development and pathology progression. The isolation of specific cell types is a crucial step to perform detailed analyses but requires the identification of succinct marker panels, which include genes that refer to cell surface proteins and clusters of differentiation molecules. This still represents a challenging NP-hard computational problem, which can be tackled through global optimization techniques. In this work, we formulate the marker panel identification problem as a bi-objective optimization problem, where the first objective regards the capability of the marker panels to accurately discriminate different cell types, while the second objective is related to the number of genes to include in the panel. In particular, we compared the performance of two multi-objective optimization algorithms, as well as of Genetic Algorithms (GAs) when considering only the first objective, employing two different representations for the candidate solutions. Our results show that the multi-objective optimization algorithms are better than GAs, considering both the quality and the consistency of the obtained marker panels;moreover, the collected results point out that different representations of the candidate solutions have a relevant impact on the performance of the optimization algorithms.

关键词： Candidate Solution Representation Multiobjective Optimization Single-Cell Transcriptomic Data computational biology bioinformatics

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Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第1期20卷 119-135页

作者： Bohnsack, Katrin Sophie Kaden, Marika Abel, Julia Villmann, Thomas Univ Appl Sci Mittweida Saxon Inst Computat Intelligence & Machine Learni D-09648 Mittweida Germany

The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of: 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.

关键词： Alignment-free sequence comparison machine learning systematization proximity measures data transformation feature generation

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Improving Clinical Decision Making With a Two-Stage Recommender System

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第5期21卷 1180-1190页

作者： Raza, Shaina Ding, Chen Vector Inst Artificial Intelligence Toronto ON M5G IM1 Canada Toronto Metropolitan Univ Toronto ON M5B 2K3 Canada

Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses challenges due to the multifaceted nature of clinical data and the demand for tailored recommendations. In this article, we introduce a 2-Stage Recommendation framework for clinical decision-making, which leverages a publicly accessible dataset of electronic health records. In the first stage, a deep neural network-based model is employed to extract a set of candidate items, such as diagnoses, medications, and prescriptions, from a patient's electronic health records. Subsequently, the second stage utilizes a deep learning model to rank and pinpoint the most relevant items for healthcare providers. Both retriever and ranker are based on pre-trained transformer models that are stacked together as a pipeline. To validate our model, we compared its performance against several baseline models using different evaluation metrics. The results reveal that our proposed model attains a performance gain of approximately 12.3% macro-average F1 compared to the second best performing baseline. Qualitative analysis across various dimensions also confirms the model's high performance. Furthermore, we discuss challenges like data availability, privacy concerns, and shed light on future exploration in this domain.

关键词： Healthcare language models recommender systems neural networks MIMIC dataset

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ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-Based Nucleotide Level Profiles

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第1期21卷 155-168页

作者： Sinha, Rituparna Pal, Rajat Kumar De, Rajat K. Heritage Inst Technol Dept Informat Technol Kolkata 700150 India Univ Calcutta Dept Comp Sci & Engn Kolkata 700073 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H. sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer.

关键词： Biomarkers copy number variation (CNV) cancer genes HBase hadoop NGS WGS

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Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules With Desirable Properties

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 2050-2063页

作者： Guo, Siyuan Guan, Jihong Zhou, Shuigeng Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

In the past decade, Artificial intelligence (AI) driven drug design and discovery has been a hot research topic in the AI area, where an important branch is molecule generation by generative models, from GAN-based models and VAE-based models to the latest diffusion-based models. However, most existing models pursue mainly the basic properties like validity and uniqueness of the generated molecules, a few go further to explicitly optimize one single important molecular property (e.g. QED or PlogP), which makes most generated molecules little usefulness in practice. In this paper, we present a novel approach to generating molecules with desirable properties, which expands the diffusion model framework with multiple innovative designs. The novelty is two-fold. On the one hand, considering that the structures of molecules are complex and diverse, and molecular properties are usually determined by some substructures (e.g. pharmacophores), we propose to perform diffusion on two structural levels: molecules and molecular fragments respectively, with which a mixed Gaussian distribution is obtained for the reverse diffusion process. To get desirable molecular fragments, we develop a novel electronic effect based fragmentation method. On the other hand, we introduce two ways to explicitly optimize multiple molecular properties under the diffusion model framework. First, as potential drug molecules must be chemically valid, we optimize molecular validity by an energy-guidance function. Second, since potential drug molecules should be desirable in various properties, we employ a multi-objective mechanism to optimize multiple molecular properties simultaneously. Extensive experiments with two benchmark datasets QM9 and ZINC250 k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr & eacute;chet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.

关键词： Training Drugs Data models computational modeling Gaussian distribution Diffusion models Optimization Diffusion model molecule generation multi-objective optimization

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Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 2531-2542页

作者： Ning, Qiao Zhao, Yaomiao Gao, Jun Chen, Chen Yin, Minghao Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214126 Peoples R China Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Jiangnan Univ Sch Fiber Engn & Equipment Technol Wuxi 214122 Peoples R China Northeast Normal Univ Informat Sci & Technol Changchun 130024 Peoples R China

MicroRNAs (miRNAs) play a significant role in cell differentiation, biological development as well as the occurrence and growth of diseases. Although many computational methods contribute to predicting the association between miRNAs and diseases, they do not fully explore the attribute information contained in associated edges between miRNAs and diseases. In this study, we propose a new method, Hierarchical Hypergraph learning in Association-Weighted heterogeneous network for MiRNA-Disease association identification (HHAWMD). HHAWMD first adaptively fuses multi-view similarities based on channel attention and distinguishes the relevance of different associated relationships according to changes in expression levels of disease-related miRNAs, miRNA similarity information, and disease similarity information. Then, HHAWMD assigns edge weights and attribute features according to the association level to construct an association-weighted heterogeneous graph. Next, HHAWMD extracts the subgraph of the miRNA-disease node pair from the heterogeneous graph and builds the hyperedge (a kind of virtual edge) between the node pair to generate the hypergraph. Finally, HHAWMD proposes a hierarchical hypergraph learning approach, including node-aware attention and hyperedge-aware attention, which aggregates the abundant semantic information contained in deep and shallow neighborhoods to the hyperedge in the hypergraph. Our experiment results suggest that HHAWMD has better performance and can be used as a powerful tool for miRNA-disease association identification.

关键词： Diseases Feature extraction Semantics Vectors Heterogeneous networks Data mining Correlation Biomedical measurement Aggregates Velocity measurement miRNA-disease association hierarchical hypergraph learning association-weighted heterogeneous network

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Classification of Parkinson's Disease Patients and Effectiveness of Medication for Freezing of Gait

Classification of Parkinson's Disease Patients and Effective...

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ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Beigi, Omid Mohamad Nobrega, Ligia Reis Houghten, Sheridan Andrade, Adriano de Oliveira Pereira, Adriano Alves Brock Univ Dept Comp Sci St Catharines ON Canada Univ Fed Uberlandia Fac Elect Engn Uberlandia MG Brazil

ISBN: (纸本)9781665484626

Parkinson's disease (PD), a neurodegenerative disease with symptoms hard to distinguish from other disorders, affects millions of people globally. Among the symptoms of PD, changes in gait have been used as a primary diagnosis factor. Symptoms of PD include bradykinesia, tremors, depression, hallucinations, cognitive decline, and falls. This study presents a dataset that records data on PD patients who experience freezing of gait, including data for medication in the "on" and "off" states. Classification is applied to two problems relating to this PD data: first, to distinguish PD patients from healthy individuals, and second, to determine effectiveness of medication for freezing of gait. Among the classifiers considered, Multilayer Perceptron, K-Nearest Neighbors, Random Forest, and Support Vector Machine obtain the best results when applied to these problems.

关键词： Support vector machines Parkinson's disease Multilayer perceptrons Depression bioinformatics Medical diagnostic imaging computational intelligence

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A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data

A Deep Learning Pipeline for the Automatic cell type Assignm...

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ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Riva, Simone G. Myers, Brynelle Cazzaniga, Paolo Tangherloni, Andrea Univ Oxford Weatherall Inst Mol Med Radcliffe Dept Med Oxford England Univ Oxford Wellcome Ctr Human Genet Oxford England Univ Bergamo Dept Human & Social Sci Bergamo Italy

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

The increasing number of single-cell transcriptomics and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism, as well as the onset of pathologies. In this context, cell type annotation represents a crucial step for the analysis of single-cell RNA sequencing data, which is usually performed by means of time-consuming and possibly biased manual processes, carried out by expert biologists. Recently, alternative computational tools have been proposed to realize an automatic cell identification either based on supervised or unsupervised Machine Learning approaches. These methods typically exploit gene expression data of curated marker gene databases to associate gene expression profiles of single cells with a cell type. In this paper, we propose a novel fully-automatic computational pipeline, named single-cell Automatic Labeling of cell POpulations (scALPO), which leverages a Long Short-Term Memory Neural Network to assign the cell types. Specifically, scALPO can label the provided clusters by simply relying on marker genes rather than gene expressions. Our results, obtained by considering two different datasets, show that scALPO outperforms the most promising state-of-the-art approaches (i.e., SCSA and scType), achieving a cell type annotation more similar to the manually-created ground truth.

关键词： Artificial intelligence Deep Learning Automatic Inference bioinformatics scRNA-seq

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CNNGRN: A Convolutional Neural Network-Based Method for Gene Regulatory Network Inference From Bulk Time-Series Expression Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 2853-2861页

作者： Gao, Zhen Tang, Jin Xia, Junfeng Zheng, Chun-Hou Wei, Pi-Jing Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230093 Anhui Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230093 Anhui Peoples R China Anhui Univ Coll Artificial Intelligence Hefei 230093 Anhui Peoples R China

Gene regulatory networks (GRNs) participate in many biological processes, and reconstructing them plays an important role in systems biology. Although many advanced methods have been proposed for GRN reconstruction, their predictive performance is far from the ideal standard, so it is urgent to design a more effective method to reconstruct GRN. Moreover, most methods only consider the gene expression data, ignoring the network structure information contained in GRN. In this study, we propose a supervised model named CNNGRN, which infers GRN from bulk time-series expression data via convolutional neural network (CNN) model, with a more informative feature. Bulk time series gene expression data imply the intricate regulatory associations between genes, and the network structure feature of ground-truth GRN contains rich neighbor information. Hence, CNNGRN integrates the above two features as model inputs. In addition, CNN is adopted to extract intricate features of genes and infer the potential associations between regulators and target genes. Moreover, feature importance visualization experiments are implemented to seek the key features. Experimental results show that CNNGRN achieved competitive performance on benchmark datasets compared to the state-of-the-art computational methods. Finally, hub genes identified based on CNNGRN have been confirmed to be involved in biological processes through literature.

关键词： Bulk gene expression deep learning gene regulatory network network visualization

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Explainable Drug Repurposing Approach From Biased Random Walks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 1009-1019页

作者： Castiglione, Filippo Nardini, Christine Onofri, Elia Pedicini, Marco Tieri, Paolo Inst Appl Comp Natl Res Council CNR IAC Italy I-00185 Rome RM Italy Roma Tre Univ Dept Math & Phys RM I-00146 Rome Italy

Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.

关键词： Drugs Diseases Databases Standards Data mining Sparse matrices Simultaneous localization and mapping Drug repurposing explainable artificial intelligence network medicine Markov chain biased random walk

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