Integrative clustering by non-negative matrix factorization can reveal coherent functional groups from gene profile data

Recent developments in molecular biology and tech- niques for genome-wide data acquisition have resulted in abun- dance of data to profile genes and predict their function. These data sets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of non-negative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous data sets and yields high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering

Integrative clustering by non-negative matrix factorization can reveal coherent functional groups from gene profile data

Recent developments in molecular biology and tech- niques for genome-wide data acquisition have resulted in abun- dance of data to profile genes and predict their function. These data sets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of non-negative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous data sets and yields high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering

Coping with School Failure: How Important are Emotional Competence, Personality and Learning Goal Orientation?

Cilj ovog istraživanja je ispitati kako se pomoću dobi i spola, nekih osobina ličnosti, mjera emocionalne kompetencije, ciljne motivacije, školskog uspjeha i kognitivne procjene stresa može predvidjeti upotreba emocijama i problemu usmjerenih strategija suočavanja adolescenata u situaciji školskog neuspjeha. Postavljene su hipoteze da se emocijama usmjereno suočavanje može bolje prognozirati na temelju varijabla ličnosti i emocionalne kompetentnosti, a problemu usmjereno suočavanje na temelju dobi, školskog uspjeha i motivacije ispitanika. U ispitivanju je sudjelovalo 534 učenika gimnazija (294 djevojaka i 240 mladića) od 14 do 19 godina. Šest grupa prediktora (godine i spol, osobine ličnosti, emocionalna kompetentnost, ciljna orijentacija, školski uspjeh i kognitivna procjena) objasnilo je ukupno 44% varijance emocijama usmjerenog suočavanja, a osobine ličnosti su najbolja grupa prediktora. Istih šest grupa prediktora objasnilo je ukupno 41% varijance problemu usmjerenog suočavanja, a najveći udio imaju varijable spol i dob. Potvrđene su postavljene hipoteze. Osobine ličnosti i emocionalna kompetentnost zajedno objašnjavaju 35% varijance emocijama usmjerenog suočavanja (kod problemu usmjerenog suočavanja 9%). Dob, ciljna orijentacija i školski uspjeh objašnjavaju 21% varijance problemu usmjerenog suočavanja (kod emocijama usmjerenog suočavanja 8%).The present study examined the relationship between coping with school failure, emotional competence, personality, goal orientation, school achievement, and cognitive stress appraisals. Two hypotheses were tested: 1) emotion-focused coping can be best predicted from personality variables and emotional competence; 2) problem-focused coping can be best predicted from age, school achievement and motivation. 534 high school students (294 females and 240 males), aged 14 to 19 years, participated in this study. Results showed that six groups of predictors (age and gender, personality traits, emotional competence, goal orientation, school achievement, and cognitive stress appraisal) explained 44% of emotion-focused coping variance, with personality traits being best predictor group. The same predictor groups explained 41% of problem-focused coping variance, with age and gender being best predictor group. The results have confirmed both hypotheses. Personality variables and emotional competence explained 35% of emotion-focused coping variance (compared to 9% of problem-focused coping variance). Age, school achievement and motivation explained 21% of problem-focused coping variance (compared to 8% of emotion-focused variance)

Coping with School Failure: How Important are Emotional Competence, Personality and Learning Goal Orientation?

Cilj ovog istraživanja je ispitati kako se pomoću dobi i spola, nekih osobina ličnosti, mjera emocionalne kompetencije, ciljne motivacije, školskog uspjeha i kognitivne procjene stresa može predvidjeti upotreba emocijama i problemu usmjerenih strategija suočavanja adolescenata u situaciji školskog neuspjeha. Postavljene su hipoteze da se emocijama usmjereno suočavanje može bolje prognozirati na temelju varijabla ličnosti i emocionalne kompetentnosti, a problemu usmjereno suočavanje na temelju dobi, školskog uspjeha i motivacije ispitanika. U ispitivanju je sudjelovalo 534 učenika gimnazija (294 djevojaka i 240 mladića) od 14 do 19 godina. Šest grupa prediktora (godine i spol, osobine ličnosti, emocionalna kompetentnost, ciljna orijentacija, školski uspjeh i kognitivna procjena) objasnilo je ukupno 44% varijance emocijama usmjerenog suočavanja, a osobine ličnosti su najbolja grupa prediktora. Istih šest grupa prediktora objasnilo je ukupno 41% varijance problemu usmjerenog suočavanja, a najveći udio imaju varijable spol i dob. Potvrđene su postavljene hipoteze. Osobine ličnosti i emocionalna kompetentnost zajedno objašnjavaju 35% varijance emocijama usmjerenog suočavanja (kod problemu usmjerenog suočavanja 9%). Dob, ciljna orijentacija i školski uspjeh objašnjavaju 21% varijance problemu usmjerenog suočavanja (kod emocijama usmjerenog suočavanja 8%).The present study examined the relationship between coping with school failure, emotional competence, personality, goal orientation, school achievement, and cognitive stress appraisals. Two hypotheses were tested: 1) emotion-focused coping can be best predicted from personality variables and emotional competence; 2) problem-focused coping can be best predicted from age, school achievement and motivation. 534 high school students (294 females and 240 males), aged 14 to 19 years, participated in this study. Results showed that six groups of predictors (age and gender, personality traits, emotional competence, goal orientation, school achievement, and cognitive stress appraisal) explained 44% of emotion-focused coping variance, with personality traits being best predictor group. The same predictor groups explained 41% of problem-focused coping variance, with age and gender being best predictor group. The results have confirmed both hypotheses. Personality variables and emotional competence explained 35% of emotion-focused coping variance (compared to 9% of problem-focused coping variance). Age, school achievement and motivation explained 21% of problem-focused coping variance (compared to 8% of emotion-focused variance)

Within-field correlation between satellite-derived vegetation indices and grain yield of wheat

This research aimed to inspect the correlation coefficients, during the crop growth stages, between vegetation indices (VIs) derived from Sentinel-2 imagery and grain winter wheat yield derived from yield monitoring and select the most promising indices for monitoring crop growth and yield estimation. METHOD / DESIGN: The satellite images in 10m resolution were selected based on crop growth stages, from the end of tillering phase (beginning of March 2019) until the full ripening (end of June 2019). For the analysis, the BBCH-scale for cereals was used. Yield observations were performed at harvest on five fields in one season and twelve VIs were calculated across 10 growth stages. To designate their correlation and dependence, a statistical comparison of the VIs and yield was made. The Pearson’s and Spearman’s correlation coefficients were calculated, and their statistical significance was tested using p-value (at p=0.01, p=0.05). RESULTS: According to the crop growth stages, the highest correlation coefficients were detected from the early boot stage (BBCH 41) until the middle of development of the fruiting stage (BBCH 73 – early milk). In that period the correlation coefficients varied from 0.39 to 0.84 depending on the field. Based on the location, the highest correlation coefficient values for all 12 indices were recorded for the parcel named C-6 (April 15), and the lowest values for the parcel named C-10 (June 29). Most of the indices showed statistically significant dependence (at the p<0.01 and p<0.05 significant levels) on the yield in the first five growth stages except the chlorophyll vegetation index (CVI) for the parcel named C-11 (p=0.21, p=0.39). CONCLUSIONS: To conclude, the last growth stage named ripening showed the lowest values both for correlation coefficient and statistical significance which means that VIs also had low values because the reflectance is weak in this growth stage and wheat is about to be harvested. In the first five stages, VIs showed significantly high spectral reflectance values since in this period the leaf is full of chlorophyll pigments. Analyzing the correlation coefficient in different stages of wheat growth, we look at the current state of crops and have the opportunity to take appropriate measures in time to increase yields or save inputs at specific locations

Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data

In the 21st century, the establishment of efficient water resource management is crucial for ensuring world water and food security. Irrigation is a significant artificial process in the hydrological cycle and presents the only way to balance between mentioned issues, where collecting knowledge is essential for developing adaptive and sustainable strategies. Considering that, the precise information about the spatio-temporal distribution of irrigated fields on a national scale is thus the initial key step for agricultural water resource management. With a high spatial, spectral, and temporal resolution, Sentinel-2 provides new possibilities in this field. This research focuses on using multispectral satellite imagery and advanced machine learning models for detecting irrigation and rainfed fields on a plot scale. Dry year images during irrigation season were used for vegetation indices calculation for three crop types: maize, soybean, and sugar beet. These three databases were used separately for training the Random Forest classifier. The results showed high overall accuracy for each three crops where soybean reached the highest 0.91, maize 0.89, while sugar beet reached 0.76. According to the results, the assumption is that the difference in accuracy between crops could be caused by the difference in the geospatial characteristic of the area, amount of data, omission in labeling crop types and rainfed fields. Irrigated agricultural fields present a challenge for classification and mapping considering the heterogeneity of the area, climate impact, and diverse crop types. This study showed that classification could be done using Sentinel-2 images, but further analysis including climate and soil data could improve the classification. This methodology has the potential to produce an annual irrigation map which is very important information for optimizing water use and making sustainable agricultural policy

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classifcation task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classifcation. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fngerprint with scattered light and laser induced fuorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fuorescence spectrum and fuorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish diferent pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the frst results of applied explainable artifcial intelligence (xAI) methodology on the pollen classifcation model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofuorometer measurements

Algoritmi integrativnog klasterovanja podataka primenom nenegativne faktorizacije matrice

Integrative approaches are motivated by the desired improvement of robustness, stability and accuracy. Clustering, the prevailing technique for preliminary and exploratory analysis of experimental data, may benefit from integration across multiple partitions. In this thesis we have proposed integration methods based on non-negative matrix factorization that can fuse clusterings stemming from different data sets, different data preprocessing steps or different sub-samples of objects or features. Proposed methods are evaluated from several points of view on typical machine learning data sets, synthetics data, and above all, on data coming form bioinformatics realm, which rise is fuelled by technological revolutions in molecular biology. For a vast amounts of 'omics' data that are nowadays available sophisticated computational methods are necessary. We evaluated methods on problem from cancer genomics, functional genomics and metagenomics.Предмет истраживања докторске дисертације су алгоритми кластеровања, односно груписања података, и могућности њиховог унапређења интегративним приступом у циљу повећања поузданости, робустности на присуство шума и екстремних вредности у подацима, омогућавања фузије података. У дисертацији су предложене методе засноване на ненегативној факторизацији матрице. Методе су успешно имплементиране и детаљно анализиране на разноврсним подацима са UCI репозиторијума и синтетичким подацима које се типично користе за евалуацију нових алгоритама и поређење са већ постојећим методама. Већи део дисертације посвећен је примени у домену биоинформатике која обилује хетерогеним подацима и бројним изазовним задацима. Евалуација је извршена на подацима из домена функционалне геномике, геномике рака и метагеномике.Predmet istraživanja doktorske disertacije su algoritmi klasterovanja, odnosno grupisanja podataka, i mogućnosti njihovog unapređenja integrativnim pristupom u cilju povećanja pouzdanosti, robustnosti na prisustvo šuma i ekstremnih vrednosti u podacima, omogućavanja fuzije podataka. U disertaciji su predložene metode zasnovane na nenegativnoj faktorizaciji matrice. Metode su uspešno implementirane i detaljno analizirane na raznovrsnim podacima sa UCI repozitorijuma i sintetičkim podacima koje se tipično koriste za evaluaciju novih algoritama i poređenje sa već postojećim metodama. Veći deo disertacije posvećen je primeni u domenu bioinformatike koja obiluje heterogenim podacima i brojnim izazovnim zadacima. Evaluacija je izvršena na podacima iz domena funkcionalne genomike, genomike raka i metagenomike