Predicting self‐declared movie watching behavior using Facebook data and information‐fusion sensitivity analysis

The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self-reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self-declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self-reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information-fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time- and frequency-based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self-reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie-watchers and market their movies more efficiently

Active learning and search on low-rank matrices

Collaborative prediction is a powerful technique, useful in domains from recommender systems to guiding the scien-tific discovery process. Low-rank matrix factorization is one of the most powerful tools for collaborative prediction. This work presents a general approach for active collabora-tive prediction with the Probabilistic Matrix Factorization model. Using variational approximations or Markov chain Monte Carlo sampling to estimate the posterior distribution over models, we can choose query points to maximize our un-derstanding of the model, to best predict unknown elements of the data matrix, or to find as many “positive ” data points as possible. We evaluate our methods on simulated data, and also show their applicability to movie ratings prediction and the discovery of drug-target interactions

Observational and genetic associations between cardiorespiratory fitness and cancer:A UK Biobank and international consortia study

Background: The association of fitness with cancer risk is not clear. Methods: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method. Results: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O 2⋅min −1⋅kg −1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73–0.89), colorectal (0.94, 0.90–0.99), and breast cancer (0.96, 0.92–0.99). In MR analyses, a 0.5 SD increase in genetically predicted O 2⋅min −1⋅kg −1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86–0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. Discussion: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.</p

ОЦЕНКА ЦИТОТОКСИЧНОСТИ ТРИХОТЕЦЕНА FUSARIUM SP. НА ЛИНИЮ РАКА МОЛОЧНОЙ ЖЕЛЕЗЫ IN VITRO

trichothecenes and their derivatives have recently attracted much attention of researchers with respect of their potential role in medicine, including for the treatment of different types of cancer. The purpose of the study was to investigate the cytotoxic effect of Fusarium trichothecene on human breast cancer cells,  human skin fibroblasts and embryonic kidney cells in vitro. Material and methods. Based on the Mtt assay, the cytotoxic effect of trichothecene on cell cultures was determined. Evaluation of morphological changes in cell cultures under the influence of trichothecene was performed by light microscopy. Results. Fusarium trichothecene was found to exhibit a dose-dependent toxic effect on cell lines in the range 1 nM to 1000 nM. the most pronounced cytotoxic effect of trichothecene was observed in human breast cancer cell line (IС50 94.72 ± 4.1 нМ). Lower doses of trichothecene led to a change in the size, shape of human breast cancer cells, human skin fibroblasts and embryonic kidney cells, and loss of contact between them and their isolation. the degradation of cell membranes, formation of unformed cell aggregates and fragments were observed in higher doses of trichothecene. Conclusion. Fusarium trichothecen is a biologically active compound and is less toxic to the normal than to the cancer cell lines, therefore, further studies of this agent are needed.В последнее время трихотеценовые соединения и их производные привлекают внимание исследователей в связи с их потенциальной возможностью применения в медицине, в том числе для лечения различных видов рака. Цель исследования – изучение цитотоксического действия трихотецена Fusarium sp. в отношении линий опухолевых клеток рака молочной железы, нормальных клеток фибробластов кожи и почек эмбриона человека in vitro. Материал и методы. С использованием общепринятого метода МТТ-теста проводилось определение цитотоксического действия трихотецена в отношении исследуемых культур клеток. Оценку изменения в морфологии клеток под воздействием трихотецена проводили методом световой микроскопии. Результаты. Было обнаружено, что трихотецен Fusarium sp. в диапазоне концентрации 1–1000 нM проявлял дозозависимое токсическое действие в отношении исследуемых линий клеток. Наиболее выраженное цитотоксическое действие трихотецена наблюдали при его действии на линию опухолевых клеток молочной железы (IС50 94,72 ± 4,1 нМ). Совместная инкубация трихотецена с линиями клеток рака молочной железы, клеток фибробластов кожи и почек эмбриона человека в более низких дозах приводила к изменению размеров, формы клеток, потере контактов между ними и их обособлению. При более высоких дозах трихотецена наблюдалась деградация мембран, образование неоформленных клеточных агрегатов и фрагментов (апоптозных тел). Заключение. Трихотецен Fusarium sp. обладает биологически активным потенциалом и является менее токсичным по отношению к нормальным клеткам человека по сравнению с опухолевыми, поэтому его целесообразно в дальнейшем исследовать как возможного противоопухолевого агента

Multi-Task Learning for Interpretation of Brain Decoding Models

Improving the interpretability of multivariate models is of primary interest for many neuroimaging studies. In this study, we present an application of multi-task learning (MTL) to enhance the interpretability of linear classifiers once applied to neuroimaging data. To attain our goal, we propose to divide the data into spatial fractions and define the temporal data of each spatial unit as a task in MTL paradigm. Our result on magnetoencephalography (MEG) data reveals preliminary evidence that, (1) dividing the brain recordings into spatial fractions based on spatial units of data and (2) considering each spatial fraction as a task, are two factors that provide more stability and consequently more interpretability for brain decoding models

Naive Bayes ant colony optimization for designing high dimensional experiments

In a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naive Bayes classifier (NBC) that is, the naive Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice

A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Geneenvironment interactions (G x E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G x E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G x E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G x E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant GxBMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer