An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Mining Temporal Association Rules with Temporal Soft Sets

This work was partially supported by the National Natural Science Foundation of China (grant no. 11301415), the Shaanxi Provincial Key Research and Development Program (grant no. 2021SF-480), and the Natural Science Basic Research Plan in Shaanxi Province of China (grant no. 2018JM1054).Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q-clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.National Natural Science Foundation of China (NSFC) 11301415Shaanxi Provincial Key Research and Development Program 2021SF-480Natural Science Basic Research Plan in Shaanxi Province of China 2018JM105

Seentest lähtuvad argumendid metsade looduskaitses

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Mets on seentele oluline elupaik – ning seened täidavad metsaökosüsteemis olulisi funktsioone. Valdav osa metsi on tänapäeval inimmõju tõttu vaesunud, näiteks on neis mitmekordselt vähenenud kõdupuidu ja põlispuude hulk. Niisuguseid «jäänukstruktuure» asustavad aga mitmed metsaseened; neid seeni minu doktoritöö käsitleski. Põhifookus oli metsas lagundajatena olulistel torikseentel, kuid uurisin ka puukoorel ja paljandunud puidul elavaid lihheniseerunud seeni. Peale selle, et mitmed neist seentest on ohustatud ja kaitse all, kasutatakse neid looduskaitses ka indikaatoritena teiste metsaväärtuste tuvastamisel. Töö põhieesmärgiks oli hinnata erinevate kasutatavate metsakaitse ja –majandusmeetodite olulisust seeneliikide kaitsel. Torikseente uurimisel olid põhimeetodiks viljakehadel põhinevad liigi-inventuurid. Leidsin, et puistu mastaabis on see adekvaatne meetod, sest ehkki puiduproovidest määratud DNA põhjal võivad konkreetses puutüves mütseelina elavad seened viljakehana tuvastamata jääda, on suur tõenäolisus leida sama liigi viljakeha mõnelt teiselt puutüvelt läheduses. Võrreldes torikseente levikumustreid põlismetsades, küpsetes majandusmetsades ja raiesmikel leidsin, et erinevalt Fennoskandiast, kus majandusmetsad on ulatuslikult vaesunud, ei olene Eestis enamik liike otseselt põlismetsadest. Seega lähtuvad liigi elupaigaseosed ja indikaatorväärtus piirkonna metsamaastiku üldseisundist. Üksikute liikide koondumine põlismetsa oli põhiliselt tingitud spetsiifilisest substraadivajadusest (nt suured lamakuused). Selgus ka, et liigi elupaigaseoste mõistmist võivad segada «krüptilised liigid»: sarnase välimuse taha peituvad erineva ökoloogiaga liigid, mida tuleks seente puhul tuvastada molekulaarselt. Rohkete indikaatorliikide asemel tuleks seetõttu lähtuda pigem vähestest hästi uuritud suunisliikidest. Doktoritöö näitas, et enamik kõdupuitu ja põlispuid asustavaid seeni (sh mitmed põlismetsaseoseliseks peetud liigid) saavad elada ka majandusmetsades, kui need on piisavalt vanad, seal leidub erinevaid puuliike ning piisavalt erinevaid jäänukstruktuure, sh nii erinevate puuliikide tüügas- ja lamapuid kui põlispuid. Seetõttu leidub metsamajanduse ning seente elurikkuse edukaks kombineerimiseks mitmeid võimalusi.Forests provide habitat for a huge variety of fungi that, in turn, play key roles in several forest ecosystem processes. The fungal biota is increasingly affected by human-caused transformation of forests, i.a., significant declines in the amount of coarse dead wood and large old trees. My thesis concentrates on fungi inhabiting such structures. My main focus is on polypores that belong to the dominant dead-wood composers in the forests, but I also explore lichenised fungi. Many polypores and lichens are nowadays found mainly in the remaining old-forest patches, and the occurrence of several species is thought to indicate ecological value of a forest. My general aim was to assess the forest conservation and management practices from the perspective of the fungi inhabiting dead wood and old living trees. The main method in polypore studies was fruit-body based species-inventory. In the stand scale, I found this method efficient: while all polypore species inhabiting a tree-trunk (as revealed by their DNA in the wood samples) may not produce fruit-body on the same tree, conspecific fruit-bodies can be usually found on other trunks in close vicinity. Differently from the intensively managed Fennoscandia, the old-forest associations of polypores were relatively weak in Estonia, as revealed by a comparison of old-growth, mature managed and harvested forests. Thus, fungal habitat associations and indicator value depend on regional landscape context. For the few species that were confined to old -growths, specific substrate requirements (notably large spruce trunks) were the reason. I also show that interpreting species habitat associations may me confused by “cryptic species” that have similar morphology, but differ by genetics and ecology. Thus, conservation practices might benefit from fewer, but better supported, fungal indicators. I demonstrate that fungi inhabiting dead wood and old living trees (including putative old-forest specialists) may form species-rich assemblages also in managed forests. This is possible if these forests are diverse in terms of tree species, dead-wood structures, and successional stages, including old stands. My general conclusion is that there are many possibilities for adjusting forest management to better address the fungal diversity that depends on dead wood and old living trees

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions

This research focuses mainly on the binary class imbalance problem in data mining. It investigates the use of combined approaches of data and algorithmic level solutions. Moreover, it examines the use of swarm intelligence and population-based techniques to combat the class imbalance problem at all levels, including at the data, algorithmic, and feature level. It also introduces various solutions to the class imbalance problem, in which swarm intelligence techniques like Stochastic Diffusion Search (SDS) and Dispersive Flies Optimisation (DFO) are used. The algorithms were evaluated using experiments on imbalanced datasets, in which the Support Vector Machine (SVM) was used as a classifier. SDS was used to perform informed undersampling of the majority class to balance the dataset. The results indicate that this algorithm improves the classifier performance and can be used on imbalanced datasets. Moreover, SDS was extended further to perform feature selection on high dimensional datasets. Experimental results show that SDS can be used to perform feature selection and improve the classifier performance on imbalanced datasets. Further experiments evaluated DFO as an algorithmic level solution to optimise the SVM kernel parameters when learning from imbalanced datasets. Based on the promising results of DFO in these experiments, the novel approach was extended further to provide a hybrid algorithm that simultaneously optimises the kernel parameters and performs feature selection

Program and abstracts from the 24th Fungal Genetics Conference

Abstracts of the plenary and poster sessions from the 24th Fungal Genetics Conference, March 20-25, 2007, Pacific Grove, CA

Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that is otherwise obscured. The objective of this dissertation is to develop machine learning based effective tools to predict disordered protein, its properties and dynamics, and interaction paradigm by systematically mining and analyzing large-scale biological data. In this dissertation, we propose a robust framework to predict disordered proteins given only sequence information, using an optimized SVM with RBF kernel. Through appropriate reasoning, we highlight the structure-like behavior of IDPs in disease-associated complexes. Further, we develop a fast and effective predictor of Accessible Surface Area (ASA) of protein residues, a useful structural property that defines protein’s exposure to partners, using regularized regression with 3rd-degree polynomial kernel function and genetic algorithm. As a key outcome of this research, we then introduce a novel method to extract position specific energy (PSEE) of protein residues by modeling the pairwise thermodynamic interactions and hydrophobic effect. PSEE is found to be an effective feature in identifying the enthalpy-gain of the folded state of a protein and otherwise the neutral state of the unstructured proteins. Moreover, we study the peptide-protein transient interactions that involve the induced folding of short peptides through disorder-to-order conformational changes to bind to an appropriate partner. A suite of predictors is developed to identify the residue-patterns of Peptide-Recognition Domains from protein sequence that can recognize and bind to the peptide-motifs and phospho-peptides with post-translational-modifications (PTMs) of amino acid, responsible for critical human diseases, using the stacked generalization ensemble technique. The involved biologically relevant case-studies demonstrate possibilities of discovering new knowledge using the developed tools

Tracing the molecular and evolutionary determinants of novel functions in protein families

This thesis explores the limits of homology-based inference of protein function and evolution, where overall similarity between sequences can be a poor indicator of functional similarity or evolutionary relationships. Each case presented has undergone different patterns of evolutionary change due to differing selective pressures. Surface adaptations and regulatory (e.g., gene expression) divergence are examined as molecular determinants of novel functions whose patterns are easily missed by assessments of overall sequence similarity. Following this, internal repeats and mosaic sequences are investigated as cases in which key evolutionary events involving fragments of protein sequences are masked by overall comparison. Lastly, virulence factors, which cannot be unified based on sequence, are predicted by analysis of elevated host-mimicry patterns in pathogenic versus non-pathogenic bacterial genomes. These patterns have resulted from unique co-evolutionary pressures that apply to bacterial pathogens, but may be lacking in their close relatives. A recurring theme in the proteins/genes/genomes analyzed is an involvement in microbial pathogenesis or pathogen-defense. Due to the ongoing "evolutionary arms race" between hosts and pathogens, virulence and defense proteins have undergone—and will likely continue to generate—evolutionary novelties. Thus, they demonstrate the necessity to look beyond overall sequence comparison, and assess multiple dimensions of functional innovation in proteins