Littérature scientifique sur le sujet « Analisi dati omici »

Foto udara Pulau Angso Duo dapat berperan sebagai bahan dasar untuk memperoleh berbagai informasi spasial yang bisa bermanfaat bagi pengelola pulau dan pemerintah setempat baik dalam upaya perencanaan konservasi maupun pembangunan. Informasi-informasi spasial yang dapat diperoleh misalnya lebar dan panjang garis pantai, luas daratan, tutupan vegetasi, penggunaan lahan, sebaran objek bangunan, dan lain sebagainya. Meskipun demikian namun informasi-informasi spasial tersebut belum bisa diperoleh sebelum dilakukan uji akurasi objek yang bertujuan untuk mengetahui tingkat ketelitian foto udara. Berdasarkan uji Omisi-Komisi yang dilakukan terhadap foto udara Pulau Angso Duo dapat diketahui bahwa tingkat ketelitian foto udara mencapai 95% dan layak untuk digunakan sebagai data geografis dasar dalam menghasilkan informasi- informasi secara spasial, dapat berperan sebagai bahan dalam kajian daya dukung lingkungan pulau untuk kegiatan wisata bahari, serta dapat digunakan untuk analisis-analisis tematik terhadap fenomena dan dinamika pada lingkungan pulau.

Abstrak: Reaksi Pasar Terhadap Perubahan Dividen dengan Indikator Abnormal Return dan Trading Volume Activity. Penelitian ini bertujuan untuk mengetahui reaksi pasar terhadap perubahan dividen, yaitu dividen tetap, dividen naik, dividen turun, dividen inisiasi, dan dividen omisi dengan indikator abnormal return dan trading volume activity pada perusahaan yang terdaftar di Bursa Efek Indonesia pada sektor properti, real estate, dan konstruksi bangunan periode 1998-2015. Penelitian ini menggunakan desain event study, di mana dilakukan pengamatan 5 hari sebelum dan 5 hari sesudah peristiwa. Analisis data yang digunakan dalam penelitian ini adalah Uji Paired Sample t-test. Hasil penelitian menunjukkan bahwa hanya dividen tetap dan dividen inisiasi dengan indikator trading volume activity terjadi reaksi pasar secara signifikan. Hasil penelitian ini juga menunjukkan bahwa untuk melihat reaksi pasar lebih baik menggunakan indikator trading volume activity dari pada abnormal return.Kata kunci: dividen, abnormal return, trading volume activity.Abstract: Market Reaction to Dividend Change with Abnormal Return and Trading Volume Activity as Indicators. The aim of this study is to find the influence of dividend change on market reaction, which are fixed dividend, rise dividend, fall dividend, initiation dividend, and omission dividend with abnormal return and trading volume activity as indicators at the companies listed in Indonesian Stock Exchange in property, real estate, and building construction sectors in 1998-2015. This study employs event study, in which it is observed within 5 days before and 5 days after the event date. Paired Sample t-test is utilized to analyze the data. The results show that fixed dividend and initiation dividend using average trading volume activity have significant effect on market reaction. Furthermore, it also suggests that to comprehend market reaction, trading volume activity is better indicator rather than abnormal return.Keywords: dividend, abnormal return, trading volume activity.

The recent advancements in cancer genomics have put under the spotlight DNA methylation, a genetic modification that regulates the functioning of the genome and whose modifications have an important role in tumorigenesis and tumor-suppression. Because of the high dimensionality and the enormous amount of genomic data that are produced through the last advancements in Next Generation Sequencing, it is very challenging to effectively make use of DNA methylation data in diagnostics applications, e.g., in the identification of healthy vs diseased samples. Additionally, state-of-the-art techniques are not fast enough to rapidly produce reliable results or efficient in managing those massive amounts of data. For this reason, we propose HD-classifier, an in-memory cognitive-based hyperdimensional (HD) supervised machine learning algorithm for the classification of tumor vs non tumor samples through the analysis of their DNA Methylation data. The approach takes inspiration from how the human brain is able to remember and distinguish simple and complex concepts by adopting hypervectors and no single numerical values. Exactly as the brain works, this allows for encoding complex patterns, which makes the whole architecture robust to failures and mistakes also with noisy data. We design and develop an algorithm and a software tool that is able to perform supervised classification with the HD approach. We conduct experiments on three DNA methylation datasets of different types of cancer in order to prove the validity of our algorithm, i.e., Breast Invasive Carcinoma (BRCA), Kidney renal papillary cell carcinoma (KIRP), and Thyroid carcinoma (THCA). We obtain outstanding results in terms of accuracy and computational time with a low amount of computational resources. Furthermore, we validate our approach by comparing it (i) to BIGBIOCL, a software based on Random Forest for classifying big omics datasets in distributed computing environments, (ii) to Support Vector Machine (SVM), and (iii) to Decision Tree state-of-the-art classification methods. Finally, we freely release both the datasets and the software on GitHub.

Identitas atau konsep diri merupakan representasi seseorang. Konsep diri pengarang dapat direkonstruksi pembaca melalui karya-karyanya, salah satunya autobiografi. Penelitian ini bertujuan mendeskripsikan konsep diri perempuan di persimpangan budaya, mencakup gambaran diri, harga diri, dan harapan diri. Sumber data penelitian ini adalah roman autobiografi Stupeur et Tremblements karya Amélie Nothomb. Penelitian ini merupakan penelitian kualitatif deskriptif dengan teknik analisis interpretatif. Pengumpulan data dilakukan dengan teknik membaca, mencatat, mengklasifikasikan, dan mengkoding. Hasil penelitian menunjukkan adanya konsep diri sebagai berikut. Pertama, kegagalan usaha peleburan diri tokoh Aku atau Amélie, sosok perempuan Belgia terdidik, menguasai bahasa Jepang dan diterima bekerja di Perusahaan Yumimoto sebagai penerjemah Jepang-Belgia/Prancis, namun terpaksa harus menerima dirinya diperkerjakan sebagai pembersih toilet, agar diterima dan melebur sebagai seorang Jepang. Dia mencoba menghapus dirinya dan mencoba melebur dalam cara pikir dan budaya Jepang, tempat ia dilahirkan dan tumbuh sampai usia lima tahun. Kedua, self-esteem atau harga diri yang selalu direndahkan oleh atasannya, wanita Jepang bernama Mori Fubuki, Saito dan Omichi. Hal itu berbenturan dengan keyakinan dan penilaian dirinya sebagai perempuan yang tumbuh di Barat. Ketiga, ideal self tokoh Amélie di Jepang yang tidak tercapai. Tokoh ini mengalami self-discrepancies, yaitu harapan dirinya berbeda dengan kenyataan. Pada akhirnya ia dapat mengaktualisasikan diri menjadi penulis setelah kembali ke Belgia. Kata Kunci: identitas, feminisme, barat-timur, autobiografi, konsep diri WOMEN'S SELF-CONCEPT IN CULTURAL JUNCTION IN AMÉLIE NOTHOMB’S STUPEUR ET TREMBLEMENTS AUTOBIOGRAPHY AbstractIdentity is closely related to self-concept. Through an autobiography, authors reconstruct their concepts through their works. This study is aimed at describing women’s self-concepts in a cross-cultural setting which includes their self-images, self-esteem, and self-ideals. The main source of this study is “Stupeur et Tremblements”, an autobiography written by Amélie Nothomb. This study is a descriptive qualitative research using interpretive analysis techniques. Data collection is done by reading, collecting, classifying, and coding. The results show that self-concepts consist of (1) dissolution of selves marked by the figure of ‘I’ as Amélie, a Belgian woman, 22 years, educated, mastering Japanese, accepted to work at Yumimoto as a Japanese-French translator but working as toilet janitor in this company. She tried to fuse into the Japanese way of thinking and culture, the country where she was born and grew until she was five years old; (2) her self-esteem is always demeaned by his direct supervisor, a Japanese woman named Mori Fubuki and also Omichi. It clashes with her beliefs and considerations as a woman who grew up as a Western woman; and (3) Amélie’s ideal self in Japan was disapproved because she faced self-discrepancies and pushed her to return to Belgium and became a successful writer. Keywords: identity, feminism, east-west, autobiography, self-concept

Di dalam naskah lama tersimpan ide, pemikiran, dan pengalaman penulisnya yang hidup di tengah-tengah masyarakat. Naskah Tuntunan Ringkas Manasik Haji (TRMH) karya K.H. Bisri Mustofa (KHBM) mengungkap kondisi sosial masyarakat berkaitan dengan pengalaman spiritual haji penulisnya ke Tanah Suci pada masa transportasi kapal laut sebagai kendaraan pilihannya. Penelitian ini bertujuan mengungkap pengalaman spiritual KHBM dalam naskah TRMH. Penelitian ini menggunakan metode penelitian filologi dan sosiologi sastra. Dari hasil penelitian filologi (kritik teks), dihasilkan kesalahan tulis substitusi sebanyak 16 kata, adisi 2 kata, omisi 3 kata, dan transposisi 2 kata atau kalimat. Naskah TRMH adalah potret pengalaman spiritual KHBM dan juga potret pengalaman spiritual haji masyarakat Indone- sia pada saat itu. Berdasarkan analisis sosiologi sastra diperoleh lima hasil penelitian tentang pengalaman spiritual KHBM, yaitu pengalaman spiritual 1) saat di kapal laut menuju Tanah Suci, 2) saat berziarah ke makam Rasulullah, 3) saat menyaksikan jemaah bertabaruk (mengharap berkah) berlebihan di Tanah Suci, 4) saat menyaksikan air Sumur Aris yang kering, dan 5) saat salat arba’in (salat empat puluh waktu).Abstract:In the old manuscripts, ideas, thoughts, and author’s experience are stored. The manu- script of Tuntunan Ringkasan Manasik Haji (TRMH) by Bisri Mustafa (KHBM) reveals social conditions associated with author’s pilgrimage spiritual experiences to the Holy Land author dur- ing sea transportation as choice. The present research aims at revealing the KHBM spiritual experiences in TRMH manuscript. In addition, this study also uses philological research method and literary sociology. The results of the research indicate that in philological research (textual criticism) there are substitution errors as many as 16 words, 2 words addition, 3 words omission and two words or sentences transposition. TRMH manuscript is a portrait of a KHBM spiritual experience and also people’s pilgrimage spiritual experience that occurred at that time. Based on the analysis of literary sociology it can be summarized that there are five findings on KHBM spiritual experience: his observation on spiritual experience during on voyage to the Holy Land, during a pilgrimage to the tomb of the Prophet Muhammad, during the pilgrims praying to expect a plentiful blessing(tabarruk) in the Holy Land, during the experience to see the Aris dry well , and during prayer forty time praying (Arba’in)

ABSTRAK Pandemi COVID-19 menyebabkan pertumbuhan ekonomi global melambat. Pertumbuhan ekonomi yang dikuru melalui GDP suatu negara dipengaruhi oleh beberapa hal. OIC merupakan organisasi negara-negara muslim didunia dimana GDP mereka juga juga terdampak secara signifikan akibat pandemi COVID-19. Penelitian ini bertujuan untuk mengobservasi pertumbuhan ekonomi negara anggota OKI yang dipengaruhi oleh variabel Industri manufaktur, Penanaman Modal Asing, dan Total konsumsi yang di moderasi oleh tenaga kerja. Penelitian ini menggunakan jenis data panel. Data diambil dengan menggunakan metode purposive sampling. Penelitian ini menggunakan sampel 10 negara aggota OKI dalam rentan tahun 2018-2020. Studi ini menggunakan teknik Random Effect Model (REM) untuk melihat pengaruh hubungan variabel independen terhadap variabel dependen secara simultan maupun parsial. Hasil menunjukkan bahwa beberapa variable independen seperti total konsumsi dan Penanaman Modal Asing mampu memberikan pengaruh signifikan untuk pertumbuhan ekonomi tanpa harus dimoderasi oleh Tenaga Kerja. Sedangkan industri manufaktur ketika dimoderasi oleh tenaga kerja, justru memberikan pengaruh yang relevan kepada pertumbuhan ekonomi di beberapa negara anggota OKI saat masa pandemi Covid-19. Maka, saran kepada pemangku kepentingan diharapkan memberikan perhatian khusus terhadap sektor industri manufaktur yang sangat banyak menyerap tenaga kerja. Untuk penelitian selanjutnya, diharapkan mampu menambah jumlah sampel data jika topik yang diambil sama dengan penelitian ini karena sampel data terbatas hanya pada negara OKI dalam penelitian ini. Kata kunci: Covid-19, Pertumbuhan Ekonomi, Penanaman Modal Asing, Tenaga Kerja, OKI. ABSTRACT The COVID-19 pandemic has caused global economic growth to slow down. Economic growth measured through a country's GDP is influenced by several things. OIC is an organization of Muslim countries in the world where their GDP has also been significantly affected by the COVID-19 pandemic. This study aimed to observe the economic growth of OIC member countries which is influenced by the variables of the manufacturing industry, foreign investment, and total consumption which are moderated by labor. This research used panel data type. Data were taken using purposive sampling method. This study used a sample of 10 OIC member countries in the 2018-2020 vulnerable years. This study uses the Random Effect Model (REM) technique to see the effect of the relationship of the independent variable on the dependent variable simultaneously or partially. The results showed that several independent variables such as total consumption and foreign investment are able to have a significant influence on economic growth without having to be moderated by the labour force. 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Bioinformatics can be used to manage the data storage resulted from in silico molecular biology experiments. Off-network (offline) applications require large computing resources, in which researchers in the bioinformatics field of agriculture and health sectors do not necessarily possess. This review paper addressed examples of affordable and applicable in silico analytical cases in both mentioned sectors. Genome sequence analysis and in silico drug design using (1) a computational method, pharmacokinetic parameter prediction, (2) Computer Aided Design and Drafting (CADD) technology, (3) potential protein action prediction, (4) OMICs application in stem cell biology, and (5) lncRNAs based database computing internet sites is one of examples. In agriculture, bioinformatics-based research has been used in (1) the development of molecular markers; (2) the design of primer for differential gene expression analysis; (3) the development of genetic maps; and (4) gene expression analysis. Further application of bioinformatics also targets the design of applicative products for pest control and the protection of plant varieties in the farm. Through this example, novice researchers in the bioinformatics field of agriculture and health sectors can conduct sophisticated research using standard computer tools, internet networks, and sufficient knowledge about bioinformatics. On the other hand, multidisciplinary collaboration between these scientists can be carried out through social networking. The synergy can be directed to improve computing capabilities and data analysis via procurement of computing resources and use of public information clusters. [Key words: genome sequences, in silico drug design, online, bioinformatics, health, agriculture.] AbstrakBioinformatika dapat digunakan dalam manajemen informasi di bidang penyimpanan data in silico dari eksperimen biologi molekuler. Aplikasi luar jaringan (luring) memerlukan sumber daya komputasi yang besar, yang belum tentu dimiliki oleh para peneliti dalam bidang bioinformatika kesehatan dan pertanian. Kajian ilmiah ini membahas contoh kasus analisis in silico yang terjangkau dan aplikatif dalam bidang kesehatan dan pertanian. Contoh kasus tersebut adalah analisis sekuen genom dan desain obat in silico, menggunakan pendekatan metode komputasional, prediksi parameter farmakokinetik, teknologi Computer Aided Design and Drafting (CADD), prediksi potensial aksi protein, aplikasi OMICs pada biologi sel punca, hingga komputasi basis data lncRNAs berbasis situs internet. Pada bidang pertanian, penelitian berbasis bioinformatika telah digunakan dalam (1) pengembangan penanda molekuler; (2) desain primer untuk analisis ekspresi gen diferensial; (3) pengembangan peta genetik; dan (4) analisis ekspresi gen. Pemanfaatan bio-informatika dalam ilmu terapan dibidang pertanian juga menyasar desain produk aplikatif untuk pengendalian hama dan perlindungan varietas tanaman. Melalui contoh tersebut, peneliti pemula dibidang bioinformatika kesehatan dan pertanian dapat melakukan penelitian canggih hanya dengan alat komputer standar, jaringan internet, dan pengetahuan mencukupi tentang bioinformatika. Disisi lain, sinergi dan kolaborasi antar peneliti multi-displiner dapat dilakukan melalui penggunaan jejaring sosial. Sinergi tersebut dapat diarahkan untuk meningkatkan kemampuan komputasi dan analisis data melalui pengadaan sumber daya komputasi dan penggunaan klaster informatika publik.[Kata kunci: sekuen genom, desain obat in silico, daring, bioinformatika, kesehatan, pertanian]

Il presente lavoro di tesi si pone nell'ambito dell'analisi dati attraverso un metodo (QDanet_PRO), elaborato dal Prof. Remondini in collaborazine coi Dott. Levi e Malagoli, basato sull'analisi discriminate a coppie e sulla Teoria dei Network, che ha come obiettivo la classificazione di dati contenuti in dataset dove il numero di campioni è molto ridotto rispetto al numero di variabili. Attraverso questo studio si vogliono identificare delle signature, ovvero un'insieme ridotto di variabili che siano in grado di classificare correttamente i campioni in base al comportamento delle variabili stesse. L'elaborazione dei diversi dataset avviene attraverso diverse fasi; si comincia con una un'analisi discriminante a coppie per identificare le performance di ogni coppia di variabili per poi passare alla ricerca delle coppie più performanti attraverso un processo che combina la Teoria dei Network con la Cross Validation. Una volta ottenuta la signature si conclude l'elaborazione con una validazione per avere un'analisi quantitativa del successo o meno del metodo.

L'eterogeneità pervade i sistemi biologici e si manifesta in differenze strutturali e funzionali osservate sia tra diversi individui di uno stesso gruppo (es. organismi o patologie), sia fra gli elementi costituenti di un singolo individuo (es. cellule). Lo studio dell’eterogeneità dei sistemi biologici e, in particolare, di quelli multicellulari è fondamentale per la comprensione meccanicistica di fenomeni fisiologici e patologici complessi (es. il cancro), così come per la definizione di strategie prognostiche, diagnostiche e terapeutiche efficaci. Questo lavoro è focalizzato sullo sviluppo e l’applicazione di metodi computazionali e modelli matematici per la caratterizzazione dell’eterogeneità di sistemi multicellulari e delle sottopopolazioni di cellule tumorali che sottendono l’evoluzione di una patologia neoplastica. Analoghe metodologie sono state sviluppate per caratterizzare efficacemente l’evoluzione e l’eterogeneità virale. La ricerca è suddivisa in due porzioni complementari, la prima finalizzata alla definizione di metodi per l’analisi e l’integrazione di dati omici generati da esperimenti di sequenziamento, la seconda alla modellazione e simulazione multiscala di sistemi multicellulari. Per quanto riguarda il primo filone, le tecnologie di next-generation sequencing permettono di generare enormi moli di dati omici, relativi per esempio al genoma o trascrittoma di un determinato individuo, attraverso esperimenti di bulk o single-cell sequencing. Una delle sfide principale in informatica è quella di definire metodi computazionali per estrarre informazione utile da tali dati, tenendo conto degli alti livelli di errori dato-specifico, dovuti principalmente a limiti tecnologici. In particolare, nell’ambito di questo lavoro, ci si è concentrati sullo sviluppo di metodi per l’analisi di dati di espressione genica e di mutazioni genomiche. In dettaglio, è stata effettuata una comparazione esaustiva dei metodi di machine-learning per il denoising e l’imputation di dati di single-cell RNA-sequencing. Inoltre, sono stati sviluppati metodi per il mapping dei profili di espressione su reti metaboliche, attraverso un framework innovativo che ha consentito di stratificare pazienti oncologici in base al loro metabolismo. Una successiva estensione del metodo ha permesso di analizzare la distribuzione dei flussi metabolici all'interno di una popolazione di cellule, via un approccio di flux balance analysis. Per quanto riguarda l’analisi dei profili mutazionali, è stato ideato e implementato il primo metodo per la ricostruzione di modelli filogenomici a partire da dati longitudinali a risoluzione single-cell, che sfrutta un framework che combina una Markov Chain Monte Carlo con una nuova funzione di likelihood pesata. Analogamente, è stato sviluppato un framework che sfrutta i profili delle mutazioni a bassa frequenza per ricostruire filogenie robuste e probabili catene di infenzione, attraverso l’analisi dei dati di sequenziamento di campioni virali. Gli stessi profili mutazionali permettono anche di deconvolvere il segnale nelle firme associati a specifici meccanismi molecolari che generano tali mutazioni, attraverso un approccio basato su non-negative matrix factorization. La ricerca condotta per quello che riguarda la simulazione computazionale ha portato allo sviluppo di un modello multiscala, in cui la simulazione della dinamica di popolazioni cellulari, rappresentata attraverso un Cellular Potts Model, è accoppiata all'ottimizzazione di un modello metabolico associato a ciascuna cellula sintetica. Co modello è possibile rappresentare ipotesi in termini matematici e osservare proprietà emergenti da tali assunti. Infine, un primo tentativo per combinare i due approcci metodologici ha condotto all'integrazione di dati di single-cell RNA-seq all'interno del modello multiscala, consentendo di formulare ipotesi data-driven sulle proprietà emergenti del sistema.
Heterogeneity pervades biological systems and manifests itself in the structural and functional differences observed both among different individuals of the same group (e.g., organisms or disease systems) and among the constituent elements of a single individual (e.g., cells). The study of the heterogeneity of biological systems and, in particular, of multicellular systems is fundamental for the mechanistic understanding of complex physiological and pathological phenomena (e.g., cancer), as well as for the definition of effective prognostic, diagnostic, and therapeutic strategies. This work focuses on developing and applying computational methods and mathematical models for characterising the heterogeneity of multicellular systems and, especially, cancer cell subpopulations underlying the evolution of neoplastic pathology. Similar methodologies have been developed to characterise viral evolution and heterogeneity effectively. The research is divided into two complementary portions, the first aimed at defining methods for the analysis and integration of omics data generated by sequencing experiments, the second at modelling and multiscale simulation of multicellular systems. Regarding the first strand, next-generation sequencing technologies allow us to generate vast amounts of omics data, for example, related to the genome or transcriptome of a given individual, through bulk or single-cell sequencing experiments. One of the main challenges in computer science is to define computational methods to extract useful information from such data, taking into account the high levels of data-specific errors, mainly due to technological limitations. In particular, in the context of this work, we focused on developing methods for the analysis of gene expression and genomic mutation data. In detail, an exhaustive comparison of machine-learning methods for denoising and imputation of single-cell RNA-sequencing data has been performed. Moreover, methods for mapping expression profiles onto metabolic networks have been developed through an innovative framework that has allowed one to stratify cancer patients according to their metabolism. A subsequent extension of the method allowed us to analyse the distribution of metabolic fluxes within a population of cells via a flux balance analysis approach. Regarding the analysis of mutational profiles, the first method for reconstructing phylogenomic models from longitudinal data at single-cell resolution has been designed and implemented, exploiting a framework that combines a Markov Chain Monte Carlo with a novel weighted likelihood function. Similarly, a framework that exploits low-frequency mutation profiles to reconstruct robust phylogenies and likely chains of infection has been developed by analysing sequencing data from viral samples. The same mutational profiles also allow us to deconvolve the signal in the signatures associated with specific molecular mechanisms that generate such mutations through an approach based on non-negative matrix factorisation. The research conducted with regard to the computational simulation has led to the development of a multiscale model, in which the simulation of cell population dynamics, represented through a Cellular Potts Model, is coupled to the optimisation of a metabolic model associated with each synthetic cell. Using this model, it is possible to represent assumptions in mathematical terms and observe properties emerging from these assumptions. Finally, we present a first attempt to combine the two methodological approaches which led to the integration of single-cell RNA-seq data within the multiscale model, allowing data-driven hypotheses to be formulated on the emerging properties of the system.

The rather generic title of this Thesis is due to the fact that several aspects of biological phenomena have been investigated. Most of this work was addressed at the investigation of the limitations of one of the essential tools for analyzing gene expression data: cluster analysis. With several hundred of clustering methods in existence, there is clearly no shortage of clustering algorithms but, at the same time, satisfactory answers to some basic questions are still to come. In particular, we present a novel algorithm for the clustering of static data and a new strategy for the clustering of short-length time-course data. Finally, we analyzed data coming from Cap Analysis Gene Expression, a relatively new technology useful for the genome-wide promoter analysis and still mostly unexplored.
Il titolo piuttosto generico di questa tesi è dovuto al fatto che sono stati indagati diversi aspetti di fenomeni biologici. La maggior parte di questo lavoro è stato rivolto alla ricerca dei limiti di uno degli strumenti essenziali per l'analisi di dati di espressione genica: l'analisi dei gruppi. Esistendo diverse centinaia di metodi di raggruppamento, chiaramente non c'è carenza di algoritmi di analisi dei gruppi, ma, allo stesso tempo, alcuni quesiti fondamentali non hanno ancora ricevuto risposte soddisfacenti. In particolare, presentiamo un nuovo algoritmo di analisi dei gruppi per dati statici ed una nuova strategia per il raggruppamento di dati temporali di breve lunghezza. Infine, abbiamo analizzato dati provenienti da una tecnologia relativamente nuova, chiamata Cap Analysis Gene Expression, utile per l'analisi dei promotori su tutto il genoma e ancora in gran parte inesplorata.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community.
Ph. D.

Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes. This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis. This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term.
Master of Science

We develop statistical tools for analyzing different types of phenotypic data in genome-wide settings. When the phenotype of interest is a binary case-control status, most genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is very rare; otherwise, a loss in the statistical power to detect disease-associated variants is expected. To address this, we propose a joint analysis of the three types of samples; cases, bases and controls. This is done by modeling the bases as a mixture of multinomial logistic functions of cases and controls, according to disease prevalence. In a typical GWAS, where thousands of single-nucleotide polymorphisms (SNPs) are available for testing, score-based test statistics are ideal in this case. Other tests of associations such as Wald’s and likelihood ratio tests are known to be asymptotically equivalent to the score test, however their performance under small sample sizes can vary significantly. In order to allow the test comparison to be performed under the proposed case-base-control (CBC) design, we provide an estimation procedure using the maximum likelihood (ML) method along with the expectation-maximization (EM) algorithm. Simulations show that combining the three samples can increase the power to detect disease-associated variants, though a very large base sample set can compensate for lack of controls. In the second part of the thesis, we consider a joint analysis of both genome-wide SNPs as well as multiple phenotypes, with a focus on the challenges they present in the estimation of SNP heritability. The current standard for performing this task is fit-ting a variance component model, despite its tendency to produce boundary estimates when small sample sizes are used. We propose a Bayesian covariance component model (BCCM) that takes into account genetic correlation among phenotypes and genetic correlation among individuals. The use of Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP heritability estimates over univariate and ML-based methods, thus explaining why recent progress in eQTL identification has been limited. I published this work as an article in the European Journal of Human genetics. In the third part of the thesis, we study the prospects of using the proposed BCCM for phenotype prediction. Results from real data show consistency in accuracy between ML based methods and the proposed Bayesian method, when effect sizes are estimated using their posterior mode. It is also noted that an initial imputation step relatively increases the predictive accuracy.

Aquesta tesi presenta una sèrie de recursos bioinformàtics desenvolupats per a donar suport en l'anàlisi de dades de NGS i altres òmics en el camp d'estudi i diagnòstic d'infeccions fúngiques. Hem dissenyat tècniques de computació per identificar nous biomarcadors i determinar potencial trets de resistència, pronosticant les característiques de les seqüències d'ADN/ARN, i planejant estratègies optimitzades de seqüenciació per als estudis de hoste-patogen transcriptomes (Dual RNA-seq). Hem dissenyat i desenvolupat tambe una solució bioinformàtica composta per un component de costat de servidor (constituït per diferents pipelines per a fer anàlisi VariantSeq, Denovoseq i RNAseq) i un altre component constituït per eines software basades en interfícies gràfiques (GUIs) per permetre a l'usuari accedir, gestionar i executar els pipelines mitjançant interfícies amistoses. També hem desenvolupat i validat un software per a l'anàlisi de seqüències i el disseny dels primers (SeqEditor) orientat a la identificació i detecció d'espècies en el diagnòstic de la PCR. Finalment, hem desenvolupat CandidaMine una base de dades integrant dades omiques de fongs patògens.
The aim of this thesis has been to develop a series of bioinformatic resources for analysis of NGS data, proteomics, or other omics technologies in the field of study and diagnosis of yeast infections. In particular, we have explored and designed distinct computational techniques to identify novel biomarker candidates of resistance traits, to predict DNA/RNA sequences’ features, and to optimize sequencing strategies for host-pathogen transcriptome sequencing studies (Dual RNA-seq). We have designed and developed an efficient bioinformatic solution composed of a server-side component constituted by distinct pipelines for VariantSeq, Denovoseq and RNAseq analyses as well as another component constituted by distinct GUI-based software to let the user to access, manage and run the pipelines with friendly-to-use interfaces. We have also designed and developed SeqEditor a software for sequence analysis and primers design for species identification and detection in PCR diagnosis. We also have developed CandidaMine an integrated data warehouse of fungal omics and for data analysis and knowledge discovery.

Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung geringer Ausgangsmengen, gekennzeichnet sind. Um diese experimentellen Defizite auszugleichen, habe ich eine Methode namens netSmooth entwickelt, welche die scRNA-seq-Daten entrascht und fehlende Werte mittels Netzwerkdiffusion über ein Gennetzwerk imputiert. Das Gennetzwerk reflektiert dabei erwartete Koexpressionsmuster von Genen. Unter Verwendung eines Gennetzwerks, das aus Protein-Protein-Interaktionen aufgebaut ist, zeige ich, dass netSmooth anderen hochmodernen scRNA-Seq-Imputationsmethoden bei der Identifizierung von Blutzelltypen in der Hämatopoese, zur Aufklärung von Zeitreihendaten unter Verwendung eines embryonalen Entwicklungsdatensatzes und für die Identifizierung von Tumoren der Herkunft für scRNA-Seq von Glioblastomen überlegen ist. netSmooth hat einen freien Parameter, die Diffusionsdistanz, welche durch datengesteuerte Metriken optimiert werden kann. So kann netSmooth auch dann eingesetzt werden, wenn der optimale Diffusionsabstand nicht explizit mit Hilfe von externen Referenzdaten optimiert werden kann. Eine integrierte Analyse ist auch relevant wenn multi-omics Daten von mehrerer Omics-Protokolle auf den gleichen biologischen Proben erhoben wurden. Hierbei erklärt jeder einzelne dieser Datensätze nur einen Teil des zellulären Systems, während die gemeinsame Analyse ein vollständigeres Bild ergibt. Ich entwickelte eine Methode namens maui, um eine latente Faktordarstellungen von multiomics Daten zu finden.
The development of high throughput sequencing (HTS) was followed by a swarm of protocols utilizing HTS to measure different molecular aspects such as gene expression (transcriptome), DNA methylation (methylome) and more. This opened opportunities for developments of data analysis algorithms and procedures that consider data produced by different experiments. Considering data from seemingly unrelated experiments is particularly beneficial for Single cell RNA sequencing (scRNA-seq). scRNA-seq produces particularly noisy data, due to loss of nucleic acids when handling the small amounts in single cells, and various technical biases. To address these challenges, I developed a method called netSmooth, which de-noises and imputes scRNA-seq data by applying network diffusion over a gene network which encodes expectations of co-expression patterns. The gene network is constructed from other experimental data. Using a gene network constructed from protein-protein interactions, I show that netSmooth outperforms other state-of-the-art scRNA-seq imputation methods at the identification of blood cell types in hematopoiesis, as well as elucidation of time series data in an embryonic development dataset, and identification of tumor of origin for scRNA-seq of glioblastomas. netSmooth has a free parameter, the diffusion distance, which I show can be selected using data-driven metrics. Thus, netSmooth may be used even in cases when the diffusion distance cannot be optimized explicitly using ground-truth labels. Another task which requires in-tandem analysis of data from different experiments arises when different omics protocols are applied to the same biological samples. Analyzing such multiomics data in an integrated fashion, rather than each data type (RNA-seq, DNA-seq, etc.) on its own, is benefitial, as each omics experiment only elucidates part of an integrated cellular system. The simultaneous analysis may reveal a comprehensive view.

Breast cancer is one of the most common cancer types worldwide, and is a leading cause of cancer related deaths in women. In this book, medical experts review our current understanding of the molecular biology and characteristics of breast cancer. The topics covered in this book provide comprehensive knowledge of mechanisms underlying breast carcinogenesis, and are intended for a wide audience including scientists, teachers, and students. 11 chapters present information about several topics on breast cancer, including the role of cell growth and proliferation pathways, androgen and cytokine signaling, germline mutations in breast cancer susceptibility genes, and molecular factors causing invasive and metastatic breast cancer. In addition, the editors discuss the recent advancements in multi-omics data analysis based on inter-and intra-tumor molecular profiles. The reference highlights how the knowledge and understanding of the biological behavior of breast neoplasms have facilitated ongoing investigations into dietary polyphenolic compounds with antioxidant properties, making them function as cancer chemopreventive agents. Along with this, the current development of treatment strategies such as targeted molecular therapy, and radiation therapy is brought to the fore to update readers.

Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.

The approach to system analysis of bile duct dysfunctions based on analysis of multi-omics data of cholangiocytes is considered. There is suggested that changes in intercellular interactions in tissues of the bile duct cause phenotypic manifestations of the cholangiopathies in the changes in cholangiocyte regulation, which can be evaluated by analysis of changes in the molecular composition of the bile.

The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.