Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

Klasifikasi Data Microarray Menggunakan Discrete Wavelet Transform dan Extreme Learning Machine

AbstrakData microarray digunakan sebagai alternatif untuk diagnosa penyakit kanker karena kesulitan dalam dignosa kanker berdasarkan bentuk morfologis, yaitu perbedaan morfologis yang tipis antar jenis kanker yang berbeda. Penelitian ini bertujuan untuk membangun pengklasifikasi data microarray. Proses klasifikasi diawali dengan reduksi dimensi data microarray menggunakan DWT, dengan cara mendekomposisi sampel hingga level tertentu, kemudian mengambil nilai koefisien aproksimasi pada level tersebut sebagai fitur sampel. Fitur tersebut selanjutnya menjadi masukan untuk klasifikasi. Metode klasifikasi yang digunakan adalah ELM yang diterapkan pada RBFN. Dataset yang digunakan adalah data microarray multikelas, yaitu dataset GCM (16.063 gen, 14 kelas) dan Subtypes-Leukemia (12.600 gen, 7 kelas).Pengujian dilakukan dengan cara membagi data latih dan data uji secara random sepuluh kali dengan proporsi data yang sama. Classifier yang dihasilkan dari penelitian ini untuk dataset GCM belum memiliki performa yang cukup baik, ditunjukkan dengan nilai akurasi sekitar 75% ± 6,25% dan nilai minimum sensitivity yang masih rendah, yaitu 15% ± 19,95% menunjukkan bahwa sensitivity untuk tiap kelas belum merata, terdapat beberapa kelas yang sensitivity-nya masih rendah. Namun, classifier untuk dataset Subtypes-Leukemia yang memiliki jumlah kelas lebih sedikit dari dataset GCM memiliki performa yang cukup baik, ditunjukkan dengan nilai akurasi 87,68% ± 2,88% dan minimum sensitivity 51,90% ± 20,29%.   Kata kunci— microarray, ekspresi gen, DWT, ELM, RBFN AbstractMicroarray data is used as an alternative in cancer diagnosis because of the difficulties cancer diagnosis based on morphologis structures. Different classes of cancer usually have poor distintion of morphologis structures. The aim of this reserach is to bulid microarray data classfier. The classification process is started by reducing dimension of microarray data. The method used to reduce the microarray data dimension is DWT by decomposing the samples until certain decomposition level and then use approximation coefficients at those level as feature to classifier. Classifier used in this reserach is ELM implemeted on RBFN. Dataset used are GCM (16.063 genes, 14 classes) and Subtypes-Leukemia (12.600 genes, 7 classes). Testing process is done by randomly dividing the training and testing data ten times with same proprotion of training and testing data. The perfomance of classifier built in this research is not so good for GCM dataset, shown by accuracy 75% ± 6,25% and mean of minimum sensitivity 15% ± 19,95%. The low minimum sensitivity indicate that there are few classes that have low sensitivity. But the classifier for Subtypes-Leukemia dataset give better result, that is accuracy 87,68% ± 2,88%  and mean of minimum sensitivity 51,90% ± 20,29%.    Keywords— microarray, gene expression, DWT, ELM, RBF

Genetic Algorithms for Feature Selection and Classification of Complex Chromatographic and Spectroscopic Data

A basic methodology for analyzing large multivariate chemical data sets based on feature selection is proposed. Each chromatogram or spectrum is represented as a point in a high dimensional measurement space. A genetic algorithm for feature selection and classification is applied to the data to identify features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. A good principal component plot can only be generated using features whose variance or information is primarily about differences between classes in the data. Hence, feature subsets that maximize the ratio of between-class to within-class variance are selected by the pattern recognition genetic algorithm. Furthermore, the structure of the data set can be explored, for example, new classes can be discovered by simply tuning various parameters of the fitness function of the pattern recognition genetic algorithm. The proposed method has been validated on a wide range of data. A two-step procedure for pattern recognition analysis of spectral data has been developed. First, wavelets are used to denoise and deconvolute spectral bands by decomposing each spectrum into wavelet coefficients, which represent the samples constituent frequencies. Second, the pattern recognition genetic algorithm is used to identify wavelet coefficients characteristic of the class. In several studies involving spectral library searching, this method was employed. In one study, a search pre-filter to detect the presence of carboxylic acids from vapor phase infrared spectra which has previously eluted prominent researchers has been successfully formulated and validated. In another study, this same approach has been used to develop a pattern recognition assisted infrared library searching technique to determine the model, manufacturer, and year of the vehicle from which a clear coat paint smear originated. The pattern recognition genetic algorithm has also been used to develop a potential method to identify molds in indoor environments using volatile organic compounds. A distinct profile indicative of microbial volatile organic compounds was developed from air sampling data that could be readily differentiated from the blank for both high mold count and moderate mold count exposure samples. The utility of the pattern recognition genetic algorithm for discovery of biomarker candidates from genomic and proteomic data sets has also been shown.Chemistry Departmen

Optimized data processing algorithms for biomarker discovery by LC-MS

This thesis reports techniques and optimization of algorithms to analyse label-free LC-MS data sets for clinical proteomics studies with an emphasis on time alignment algorithms and feature selection methods. The presented work is intended to support ongoing medical and biomarker research. The thesis starts with a review of important steps in a data processing pipeline of label-free Liquid Chromatography – Mass Spectrometry (LC-MS) data. The first part of the thesis discusses an optimization strategy for aligning complex LC-MS chromatograms. It explains the combination of time alignment algorithms (Correlation Optimized Warping, Parametric Time Warping and Dynamic Time Warping) with a Component Detection Algorithm to overcome limitations of the original methods that use Total Ion Chromatograms when applied to highly complex data. A novel reference selection method to facilitate the pre-alignment process and an approach to globally compare the quality of time alignment using overlapping peak area are introduced and used in the study. The second part of this thesis highlights an ongoing challenge faced in the field of biomarker discovery where improvements in instrument resolution coupled with low sample numbers has led to a large discrepancy between the number of measurements and the number of measured variables. A comparative study of various commonly used feature selection methods for tackling this problem is presented. These methods are applied to spiked urine data sets with variable sample size and class separation to mimic typical conditions of biomarker research. Finally, the summary and the remaining challenges in the data processing field are summarized at the end of this thesis.

Machine Learning Approaches for Cancer Analysis

In addition, we propose many machine learning models that serve as contributions to solve a biological problem. First, we present Zseq, a linear time method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors, such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score less than the user-defined threshold. Zseq is able to provide a better mapping rate; it reduces the number of ambiguous bases significantly in comparison with other methods. Evaluation of the filtered reads has been conducted by aligning the reads and assembling the transcripts using the reference genome as well as de novo assembly. The assembled transcripts show a better discriminative ability to separate cancer and normal samples in comparison with another state-of-the-art method. Studying the abundance of select mRNA species throughout prostate cancer progression may provide some insight into the molecular mechanisms that advance the disease. In the second contribution of this dissertation, we reveal that the combination of proper clustering, distance function and Index validation for clusters are suitable in identifying outlier transcripts, which show different trending than the majority of the transcripts, the trending of the transcript is the abundance throughout different stages of prostate cancer. We compare this model with standard hierarchical time-series clustering method based on Euclidean distance. Using time-series profile hierarchical clustering methods, we identified stage-specific mRNA species termed outlier transcripts that exhibit unique trending patterns as compared to most other transcripts during disease progression. This method is able to identify those outliers rather than finding patterns among the trending transcripts compared to the hierarchical clustering method based on Euclidean distance. A wet-lab experiment on a biomarker (CAM2G gene) confirmed the result of the computational model. Genes related to these outlier transcripts were found to be strongly associated with cancer, and in particular, prostate cancer. Further investigation of these outlier transcripts in prostate cancer may identify them as potential stage-specific biomarkers that can predict the progression of the disease. Breast cancer, on the other hand, is a widespread type of cancer in females and accounts for a lot of cancer cases and deaths in the world. Identifying the subtype of breast cancer plays a crucial role in selecting the best treatment. In the third contribution, we propose an optimized hierarchical classification model that is used to predict the breast cancer subtype. Suitable filter feature selection methods and new hybrid feature selection methods are utilized to find discriminative genes. Our proposed model achieves 100% accuracy for predicting the breast cancer subtypes using the same or even fewer genes. Studying breast cancer survivability among different patients who received various treatments may help understand the relationship between the survivability and treatment therapy based on gene expression. In the fourth contribution, we have built a classifier system that predicts whether a given breast cancer patient who underwent some form of treatment, which is either hormone therapy, radiotherapy, or surgery will survive beyond five years after the treatment therapy. Our classifier is a tree-based hierarchical approach that partitions breast cancer patients based on survivability classes; each node in the tree is associated with a treatment therapy and finds a predictive subset of genes that can best predict whether a given patient will survive after that particular treatment. We applied our tree-based method to a gene expression dataset that consists of 347 treated breast cancer patients and identified potential biomarker subsets with prediction accuracies ranging from 80.9% to 100%. We have further investigated the roles of many biomarkers through the literature. Studying gene expression through various time intervals of breast cancer survival may provide insights into the recovery of the patients. Discovery of gene indicators can be a crucial step in predicting survivability and handling of breast cancer patients. In the fifth contribution, we propose a hierarchical clustering method to separate dissimilar groups of genes in time-series data as outliers. These isolated outliers, genes that trend differently from other genes, can serve as potential biomarkers of breast cancer survivability. In the last contribution, we introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have significant value in guiding treatment decisions. Our study also supports PTGFR, NREP, scaRNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

MALDI-ToF mass spectrometry biomarker profiling via multivariate data analysis application in the biopharmaceutical bioprocessing industry

PhD ThesisMatrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF MS) is a technique by which protein profiles can be rapidly produced from biological samples. Proteomic profiling and biomarker identification using MALDI-ToF MS have been utilised widely in microbiology for bacteria identification and in clinical proteomics for disease-related biomarker discovery. To date, the benefits of MALDI-ToF MS have not been realised in the area of mammalian cell culture during bioprocessing. This thesis explores the approach of ‘intact-cell’ MALDI-ToF MS (ICM-MS) combined with projection to latent structures – discriminant analysis (PLS-DA), to discriminate between mammalian cell lines during bioprocessing. Specifically, the industrial collaborator, Lonza Biologics is interested in adopting this approach to discriminate between IgG monoclonal antibody producing Chinese hamster ovaries (CHO) cell lines based on their productivities and identify protein biomarkers which are associated with the cell line productivities. After classifying cell lines into two categories (high/low producers; Hs/Ls), it is hypothesised that Hs and Ls CHO cells exhibit different metabolic profiles and hence differences in phenotypic expression patterns will be observed. The protein expression patterns correlate to the productivities of the cell lines, and introduce between-class variability. The chemometric method of PLS-DA can use this variability to classify the cell lines as Hs or Ls. A number of differentially expressed proteins were matched and identified as biomarkers after a SwissProt/TrEMBL protein database search. The identified proteins revealed that proteins involved in biological processes such as protein biosynthesis, protein folding, glycolysis and cytoskeleton architecture were upregulated in Hs. This study demonstrates that ICM-MS combined with PLS-DA and a protein database search can be a rapid and valuable tool for biomarker discovery in the bioprocessing industry. It may help in providing clues to potential cell genetic engineering targets as well as a tool in process development in the bioprocessing industry. With the completion of the sequencing of the CHO genome, this study provides a foundation for rapid biomarker profiling of CHO cell lines in culture during recombinant protein manufacturing.Lonza Biologics

Radiogenomics in non-small-cell lung cancer

Ο μη μικροκυτταρικός καρκίνος του πνεύμονα είναι ο πιο συχνά συναντώμενος υποτύπος καρκίνου του πνεύμονα, ο οποίος αποτελείται από ένα φάσμα υποτύπων. Το NSCLC είναι ένας θανατηφόρος, ετερογενής συμπαγής όγκος με μια εκτεταμένη σειρά μοριακών χαρακτηριστικών. Η πάθηση έχει γίνει ένα αξιοσημείωτο παράδειγμα ιατρικής ακριβείας καθώς το ενδιαφέρον για το θέμα συνεχίζει να επεκτείνεται. Ο απώτερος στόχος της τρέχουσας έρευνας είναι να χρησιμοποιήσει συγκεκριμένα γονίδια ως βιοδείκτες για την πρόγνωση, την έγκαιρη διάγνωση και την εξατομικευμένη θεραπεία, τα οποία διευκολύνονται από τη χρήση εξελισσόμενων τεχνικών αλληλούχισης επόμενης γενιάς που επιτρέπουν την ταυτόχρονη ανίχνευση μεγάλου αριθμού γενετικές ανωμαλίες. Γνωστές μεταλλάξεις ενός αριθμού γονιδίων, όπως τα EGFR, ALK και KRAS, επηρεάζουν ήδη τις αποφάσεις θεραπείας και νέα βασικά γονίδια και μοριακές υπογραφές διερευνώνται για την προγνωστική τους αξία καθώς και για την πιθανή συμβολή τους στην ανοσοθεραπεία και τη θεραπεία της υποτροπής στην αντίσταση στις υπάρχουσες θεραπείες. Οι τύποι δειγμάτων που χρησιμοποιούνται για μελέτες NGS, όπως αναρροφήσεις με λεπτή βελόνα, ιστός ενσωματωμένος σε παραφίνη σταθεροποιημένος με φορμαλίνη και DNA χωρίς κύτταρα, έχουν ο καθένας τα δικά του πλεονεκτήματα και μειονεκτήματα που πρέπει να ληφθούν υπόψηNon-small cell lung cancer is the most often encountered subtype of lung cancer, which consists of a spectrum of subtypes. NSCLC is a lethal, heterogeneous solid tumor with an extensive array of molecular features. The condition has become a notable example of precision medicine as interest in the topic continues to expand. The ultimate goal of the current research is to use specific genes as biomarkers for its prognosis, timely diagnosis, and personalized therapy, all of which are facilitated by the use of evolving next-generation sequencing techniques that permit the simultaneous detection of a large number of genetic abnormalities. Known mutations of a number of genes, such as EGFR, ALK, and KRAS, already influence treatment decisions, and new key genes and molecular signatures are being investigated for their prognostic value as well as their potential contribution to immunotherapy and the treatment of recurrence due to resistance to existing therapies. The sample types utilized for NGS studies, such as fine-needle aspirates, formalin-fixed paraffin-embedded tissue, and cell-free DNA, each have their own advantages and disadvantages that must be taken into accoun