Extreme learning machines for reverse engineering of gene regulatory networks from expression time series

The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene-expression data. Results: Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions.Fil: Rubiolo, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Analisa Perbandingan Data Mining Pada Klasifikasi Penyakit Jantung Menggunakan Algoritma Extreme Learning Machine (Elm) Dan K-Nearest Neighbor (K-NN)

Heart”disease”is”type”of”non communicable disease which results in.a”high’mortality” rate. Heart disease is caused by.several.risk”factors.including”smoking, anunhealth.lifestyle,” high holesterol, ”hypertension,”and”diabetes. Based on’these facts, an”appropriate”algorithm& is needed”to”classify”heart disease as an effort to prevent n’increase’in”the”death”rate from heart”disease..The”algorithm.used is.expected to work accurately.in”the’classification”method. among”them,.there”aren two algorithm used, namely.the& Extreme.Learning”Machine&(ELM) algorithm and.the K-Nearest Neighbour (K-NN)/algorithm.7The1aim5is1to”compare”the”two algorithms, in.order5t determine.which”algorithm has the higher percentage”of accuracy in classifying heart1disease5data.%To3achieve2the1objectives of the study,7several%research methods were”carried”out,”namely data preprocessing with the”data collection stage,&data splitting”and data normalization followed by%the%ELM and K-NN”algorithm” methods at”the.data”processing stage..From”the”steps”that”have”bee” carried”out,.the”final’result”of the”Extreme Learning Machine2(ELM) algorithm obtained a7greater&accuracy& value&of 93.33%,”while”the K-Nearest”Neighbour (K-NN) algorithm1obtained1 an1accuracy value1of 83.52%. This’shows that”in”this study the Extreme Learning Machine (ELM) algorithm;works more”optimally2than7the”K-Nearest4Neighbour3(K-NN) algorithm in the”classification%of heart%disease data

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues