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Research in computational molecular biology: 19th annual international conference, recomb2015 warsaw, poland, april 12-15, 2015 proceedings 19

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19th Annual International conference on Research in computational Molecular biology, RECOMB 2015

作者： Przytycka, Teresa M. National Center for Biotechnology Information Bethesda MD United States

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"Who are the key players behind a disease state?": Outcomes of a new computational approach on cancer data

"Who are the key players behind a disease state?": Outcomes ...

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International conference on bioinformatics and Systems biology (BSB)

作者： Devasia, Jeethu V. Chandran, Priya Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut 673601 Kerala India

ISBN: (纸本)9781509022618

The problem of identifying disease causing genes and dysregulated pathways has attained a key position in computational biology research, as it helps in understanding major causal genes and their interactions behind a disease state and thereby enables proposing new drug targets. The development of computational approaches for the inference of disease causing genes and associated pathways can improve the accuracy and efficiency and reduce the cost of biomedical analysis. Identification of disease causing genes from the large set of genes produced by high throughput experiments is a time consuming and costly process. Based on the fact that interactions among several genes results in certain phenotypes, the molecular interaction network is a major resource for computational approaches to identify disease causing genes and associated pathways. Executing computations on the huge molecular interaction network is also major challenge. Here, we address the problem of inferring disease causing genes and their pathways using graph theoretical approaches which focus on reducing the execution time by using graph pruning techniques, without compromising on accuracy of results. Experimentation on real biological data shows reduced execution time and increased accuracy than other methods reported in literature on benchmark datasets, on using the proposed technique.

关键词： Molecular interaction Network Target Genes Causal genes Dysregulated pathway Graph pruning

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Using harmony search for solving a typical bioinformatics problem

Using harmony search for solving a typical bioinformatics pr...

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1st International conference on Informatics and computational intelligence, ICI 2011

作者： Navi, Saman Poursiah Asgarian, Ehsan Moeinzadeh, Hossein Chahkandi, Vahid Department of Computer Engineering Islamic Azad University Quchan Branch Quchan Iran Department of Engineering Quchan Institute of Engineering and Technology Quchan Iran Department of Computer Engineering Iran University of Science Tehran Iran Department of Computer Engineering Islamic Azad University Mashad Branch Mashhad Iran

ISBN: (纸本)9780769546186

Recently, there has been great interest in bioinformatics among researches from various disciplines such as computer science, mathematics, statistics and artificial intelligence. bioinformatics mainly deals with solving biological problems at molecular levels. One of the classic problems of bioinformatics which has gain a lot attention lately is Haplotyping, the goal of which is categorizing SNP-fragments into two clusters and deducing a haplotype for each. Since the problem is proved to be NP-hard, several computational and heuristic methods have addressed the problem seeking feasible answers. In this paper, harmony search (HS) is considered as a clustering approach. Extensive computational experiments indicate that the designed HS algorithm achieves a higher accuracy than the genetic algorithm (GA) and particle swarm optimization (PSO) to the MEC model in most cases. © 2011 ieee.

关键词： bioinformatics

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Protein Secondary Structure Prediction Using Support Vector Machines (SVMs)

Protein Secondary Structure Prediction Using Support Vector ...

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ieee International conference on Machine intelligence Research and Advancement (ICMIRA)

作者： Patel, Mayuri Shah, Hitesh GH Patel Coll Engn & Technol Dept Informat Technol Vallabh Vidyanagar 388120 Gujarat India GH Patel Coll Engn & Technol Elect & Commun Engn Dept Vallabh Vidyanagar 388120 Gujarat India

ISBN: (纸本)9780769550138

bioinformatics or computational biology is field of science in which biology, computer science and information technology merges into a single discipline. In modern computation biology, protein secondary structure prediction is a major problem. Secondary structure prediction is depends on its amino acid sequence. Current studies prefer machine learning techniques for classification and regression task. Recently many researchers used various data mining and machine learning tool for protein structure prediction. Our intention is to use model based (i.e., supervised learning) approach for protein secondary structure prediction and our objective is to enhance the prediction of 2D protein structure problem using advance machine learning techniques like, linear and non-linear support vector machine with different kernel functions. The datasets used for this problem are Protein Data Bank (PDB) sets, which is based on structural classification of protein (SCOP), RS126 and CB513.

关键词： bioinformatics CB513 Feature Selection Protein Data Bank (PDB) RS126

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23^rd InternationalWorkshop on Data Mining in bioinformatics (BIOKDD 2024) 24

23<SUP>rd</SUP> InternationalWorkshop on Data Mining in Bioi...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Yan, Da Hamed, Ahmed Abdeen Chen, Jake Y. Zaki, Mohammed J. Indiana Univ Bloomington Dept Comp Sci Bloomington IN 47405 USA SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY USA Univ Alabama Birmingham Birmingham AL USA Rensselaer Polytech Inst Dept Comp Sci Troy NY USA

ISBN: (纸本)9798400704901

The goal of the 22(nd) International Workshop on Data Mining in bioinformatics (BIOKDD 2023) is to encourage KDD researchers to solve the numerous problems and challenges in bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2023 features the theme "Large-Scale Data-Driven Methods for bioinformatics". This theme encourages the use of high-performance computing (HPC) to support the training of large machine learning models for problems in bioinformatics and computational biology. The key goal is to accelerate the convergence between Data Mining and bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare. The goal of the 23(rd) International Workshop on Data Mining in bioinformatics (BIOKDD 2024) is to encourage KDD researchers to solve the numerous problems and challenges in bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2024 features the theme "Advancing bioinformatics with LLMs and GenAI". This theme encourages the use of large language models and generative artificial intelligence to solve problems in bioinformatics and computational biology. The key goal is to accelerate the convergence between Data Mining and bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.

关键词： bioinformatics health informatics AI

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Intrinsically disordered proteins: An update

Intrinsically disordered proteins: An update

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7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Dunker, A. Keith Oldfield, Christopher J. Meng, Jingwei Romero, Pedro Yang, Jack Y. Obradovic, Zoran Uversky, Vladimir N. Center for Computational Biology and Bioinformatics Indiana University Schools of Medicine and Informatics Indianapolis IN 46202 United States Center for Computational Biology and Bioinformatics Indiana University School of Informatics Indianapolis IN 46202 United States Department of Biochemistry and Molecular Biology Indiana University School of Medicine Indianapolis IN 46202 United States Department of Radiation Oncology Harvard Medical School Massachusetts General Hospital Boston MA 02114 United States Center for Information Science and Technology Temple University Philadelphia PA 19122 United States Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 United States Institute for Biological Instrumentation Russian Academy of Sciences 142290 Puschino Russia

ISBN: (纸本)1424415098

Just over 10 years ago, in June, 1997, in the Proceedings of the ieee International conference on Neural Networks, we published our first predictor of intrinsically disordered protein [1]. Since then, we have substantially improved our predictors, and more than 20 other laboratory groups have joined in efforts to improve the prediction of protein disorder. At the algorithmic level, prediction of protein intrinsic disorder is similar to the prediction of secondary structure, but, at the structural level, secondary structure and intrinsic disorder are entirely different. The secondary structure class called random coil or irregular differs from intrinsic disorder due to very different dynamic properties, with the secondary structure class being much less mobile than the region of disorder. At the biological level, unlike the prediction of secondary structure, the prediction of intrinsic disorder has been revolutionary. That is, for many years, experimentalists have provided evidence that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. Experimentalists further are showing that, for some proteins, functions depended on the unstructured rather than structured state. However, these examples have been mostly ignored. To our knowledge, not one disordered protein or disorder-associated function is discussed in any biochemistry textbook, even though such examples began to be discovered more than 50 years ago. Disorder prediction has been important for showing that the few experimentally characterized examples represent a very large cohort that is found all across all three domains of life. We now know that many significant biological functions depend directly on, or are importantly associated with, the unfolded or partially folded state. In this paper, we will briefly review some of the key discoveries that have occurred in the last decade, and, furthermore, will make a few highly speculative projections. ©2007 ieee.

关键词： Forecasting

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Overcoming the curse of dimensionality in a statistical geometry based computational protein mutagenesis

Overcoming the curse of dimensionality in a statistical geom...

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10th ieee International conference on Data Mining Workshops, ICDMW 2010

作者： Masso, Majid Department of Bioinformatics and Computational Biology George Mason University Manassas VA United States

A computational mutagenesis is detailed whereby each single residue substitution in a protein chain of primary sequence length N is represented as a sparse N-dimensional feature vector, whose M

ISBN: (纸本)9780769542577

A computational mutagenesis is detailed whereby each single residue substitution in a protein chain of primary sequence length N is represented as a sparse N-dimensional feature vector, whose M

关键词： computational geometry

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SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 1765-1774页

作者： Sun, Weicheng Ren, Chengjuan Xu, Jinsheng Zhang, Ping Huazhong Agr Univ Coll informat Wuhan 430070 Peoples R China Sichuan Int Studies Univ Coll Language Intelligence Chongqing 400031 Peoples R China

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first constructs a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

关键词： subgraph extractor subgraph perceptron CircRNAs-drug sensitivity association cancer treatment cancer treatment subgraph perceptron cancer treatment graph convolu- tional network

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Performance evaluation of clustering algorithms on microcalcifications as mammography findings

Performance evaluation of clustering algorithms on microcalc...

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ieee 13th International conference on bioinformatics and Bioengineering (BIBE)

作者： Ikonomakis, Emmanouil K. Spyrou, George M. Ligomenides, Panos A. Vrahatis, Michael N. Univ Patras Dept Math Computat Intelligence Lab CILab GR-26110 Patras Greece Acad Athens Biomed Res Fdn Biomed Informat Unit Athens 11527 Greece

ISBN: (纸本)9781479931637

Breast cancer can be prevented with regular mammography screening. Yet, the incorporation of computational intelligence relies on training classifiers on a set of predefined Regions of Interest (ROIs). Data Clustering has been applied to address the problem of ROI detection, yet no extensive research has been carried out on which algorithm to utilize. This contribution focuses on microcalcification clustering as a Data Clustering application, giving insights concerning the performance of three main clustering algorithms.

关键词： cancer image classification mammography medical image processing object detection pattern clustering performance evaluation

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Heuristics for Placing the Spawn Points in Multiplayer First Person Shooters

Heuristics for Placing the Spawn Points in Multiplayer First...

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ieee conference on Games (ieee COG)

作者： Ballabio, Marco Loiacono, Daniele Politecn Milan Dipartimento Elettron Informaz & Bioinformat Milan Italy

ISBN: (纸本)9781728118840

Level design in first person shooters is a critical and complex process with many facets. Among them, the placement of the spawn points, i.e., the positions of the level where players starts the game after being killed, has a huge impact on the game experience. In this work, we propose a novel approach to this problem that combines a set of design rules with a graph-based analysis of the level. As a results, we design a set of heuristics that, based on the topological features of the graphs extracted from the levels, can be used for the placement of spawn point. Finally, we test our approach with a small user study on three different levels, comparing our heuristic placement with an uniform strategy. Although preliminary, our results are promising and suggest that the level design principles are effectively captured by our heuristics.

关键词： Games Weapons Layout Feature extraction Navigation Software bioinformatics

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