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1.
PLoS One ; 15(7): e0235545, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32645045

RESUMO

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.

2.
Sci Rep ; 10(1): 9407, 2020 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-32523075

RESUMO

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

3.
Eur Urol Focus ; 2020 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-32418878

RESUMO

BACKGROUND: Multiparametric prostate magnetic resonance imaging (mpMRI) can be considered the gold standard in prostate magnetic resonance imaging (MRI). Biparametric prostate MRI (bpMRI) is faster and could be a feasible alternative to mpMRI. OBJECTIVE: To determine the negative predictive value (NPV) of Improved Prostate Cancer Diagnosis (IMPROD) bpMRI as a whole and in clinical subgroups in primary diagnostics of clinically significant prostate cancer (CSPCa). DESIGN, SETTING, AND PARTICIPANTS: This is a pooled data analysis of four prospective, registered clinical trials investigating prebiopsy IMPROD bpMRI. Men with a clinical suspicion of prostate cancer (PCa) were included. INTERVENTION: Prebiopsy IMPROD bpMRI was performed, and an IMPROD bpMRI Likert scoring system was used. If suspicious lesions (IMPROD bpMRI Likert score 3-5) were visible, targeted biopsies in addition to systematic biopsies were taken. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Performance measures of IMPROD bpMRI in CSPCa diagnostics were evaluated. NPV was also evaluated in clinical subgroups. Gleason grade ≥3 + 4 in any biopsy core taken was defined as CSPCa. RESULTS AND LIMITATIONS: A total of 639 men were included in the analysis. The mean age was 64 yr, mean prostate-specific antigen level was 8.9 ng/ml, and CSPCa prevalence was 48%. NPVs of IMPROD bpMRI Likert scores 3-5 and 4-5 for CSPCa were 0.932 and 0.909, respectively, and the corresponding positive predictive values were 0.589 and 0.720. Only nine of 132 (7%) men with IMPROD bpMRI Likert score 1-2 had CSPCa and none with Gleason score >7. Thus, 132 of 639 (21%) study patients could have avoided biopsies without missing a single Gleason >7 cancer in the study biopsies. In the subgroup analysis, no clear outlier was present. The limitation is uncertainty of the true CSPCa prevalence. CONCLUSIONS: IMPROD bpMRI demonstrated a high NPV to rule out CSPCa. IMPROD bpMRI Likert score 1-2 excludes Gleason >7 PCa in the study biopsies. PATIENT SUMMARY: We investigated the feasibility of prostate magnetic resonance imaging (MRI) with the Improved Prostate Cancer Diagnosis (IMPROD) biparametric MRI (bpMRI) protocol in excluding significant prostate cancer. In this study, highly aggressive prostate cancer was excluded using the publicly available IMPROD bpMRI protocol (http://petiv.utu.fi/multiimprod/).

4.
J Magn Reson Imaging ; 51(4): 1075-1085, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31566845

RESUMO

BACKGROUND: Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic-assisted laparoscopic prostatectomy (RALP). PURPOSE: To evaluate the performance of a routine clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP. STUDY TYPE: Retrospective cohort study. SUBJECTS: Ninety-one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow-up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml. FIELD STRENGTH/SEQUENCE: mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T2 w, diffusion-weighted imaging (DWI), or dynamic contrast-enhanced (DCE) MRI. ASSESSMENT: All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI-RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re-reported by reader B to evaluate interreader variability. STATISTICAL TESTS: Univariate and multivariate analysis using greedy feature selection and tournament leave-pair-out cross-validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group [GGG], extracapsular extension, seminal vesicle invasion, intraoperative surgical margins [PSM], final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI-RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume [MRI-lesion-percentage], mpMRI ECE, mpMRI seminal vesicle invasion [SVI]) variables. The evaluation metric was the area under the curve (AUC). RESULTS: Forty-eight (53%) patients developed BCR. The best-performing individual features with TLPOCV AUC of 0.73 (95% confidence interval [CI] 0.64-0.82) were RALP GGG, MRI-lesion-percentage followed by biopsy GGG (0.72, 0.62-0.82), and Decipher score (0.71, 0.60-0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively DATA CONCLUSION: Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1075-1085.

5.
J Magn Reson Imaging ; 51(5): 1540-1553, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-31588660

RESUMO

BACKGROUND: Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI. PURPOSE: To evaluate the diagnostic accuracy of a unique biparametric MRI protocol (IMPROD bpMRI) combined with clinical and molecular markers in men with cSPCa. STUDY TYPE: Prospective single-institutional clinical trial (NCT01864135). SUBJECTS: Eighty men with cSPCa. FIELD STRENGTH/SEQUENCE: 3T, surface array coils. Two T2 -weighted and three diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm2 ; 2) b-values 0,1500 s/mm2 ; 3) b-values 0, 2000 s/mm2 . ASSESSMENT: IMPROD bpMRI examinations were qualitatively (IMPROD bpMRI Likert score) and quantitatively (DWI-based Gleason grade score) prospectively reported. Men with IMPROD bpMRI Likert 3-5 had two targeted biopsies followed by 12-core systematic biopsies (SB); those with IMPROD bpMRI Likert 1-2 had only SB. Additionally, 2-core from normal-appearing prostate areas were obtained for the mRNA expression of ACSM1, AMACR, CACNA1D, DLX1, PCA3, PLA2G7, RHOU, SPINK1, SPON2, TMPRSS2-ERG, and TDRD1 measured by quantitative reverse-transcription polymerase chain reaction. STATISTICAL TESTS: Univariate and multivariate analysis using regularized least-squares, feature selection and tournament leave-pair-out cross-validation (TLPOCV), as well as 10 random splits of the data in training-testing sets, were used to evaluate the mRNA, clinical and IMPROD bpMRI parameters in detecting clinically significant prostate cancer (SPCa) defined as Gleason score ≥ 3 + 4. The evaluation metric was the area under the curve (AUC). RESULTS: IMPROD bpMRI Likert demonstrated the highest TLPOCV AUC of 0.92. The tested clinical variables had AUC 0.56-0.73, while the mRNA and additional IMPROD bpMRI parameters had AUC 0.50-0.67 and 0.65-0.89 respectively. The combination of clinical and mRNA biomarkers produced TLPOCV AUC of 0.87, the highest TLPOCV performance without including IMPROD bpMRI Likert. DATA CONCLUSION: The qualitative IMPROD bpMRI Likert score demonstrated the highest accuracy for SPCa detection compared with the tested clinical variables and mRNA biomarkers. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.

6.
J Magn Reson Imaging ; 51(5): 1556-1567, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-31750988

RESUMO

BACKGROUND: Multiparametric MRI of the prostate has been shown to improve the risk stratification of men with an elevated prostate-specific antigen (PSA). However, long acquisition time, high cost, and inter-center/reader variability of a routine prostate multiparametric MRI limit its wider adoption. PURPOSE: To develop and validate nomograms based on unique rapid biparametric MRI (bpMRI) qualitative and quantitative derived variables for prediction of clinically significant cancer (SPCa). STUDY TYPE: Retrospective analyses of single (IMPROD, NCT01864135) and multiinstitution trials (MULTI-IMPROD, NCT02241122). POPULATION: 161 and 338 prospectively enrolled men who completed the IMPROD and MULTI-IMPROD trials, respectively. FIELD STRENGTH/SEQUENCE: IMPROD bpMRI: 3T/1.5T, T2 -weighted imaging, three separate diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm2 ; 2) b values 0, 1500 s/mm2 ; 3) values 0, 2000 s/mm2 . ASSESSMENT: The primary endpoint of the combined trial analysis was the diagnostic accuracy of the combination of IMPROD bpMRI and clinical variables for detection of SPCa. STATISTICAL TESTS: Logistic regression models were developed using IMPROD trial data and validated using MULTI-IMPROD trial data. The model's performance was expressed as the area under the curve (AUC) values for the detection of SPCa, defined as ISUP Gleason Grade Group ≥2. RESULTS: A model incorporating clinical variables had an AUC (95% confidence interval) of 0.83 (0.77-0.89) and 0.80 (0.75-0.85) in the development and validation cohorts, respectively. The corresponding values for a model using IMPROD bpMRI findings were 0.93 (0.89-0.97), and 0.88 (0.84-0.92), respectively. Further addition of the quantitative DWI-based score did not improve AUC values (P < 0.05). DATA CONCLUSION: A prediction model using qualitative IMPROD bpMRI findings demonstrated high accuracy for predicting SPCa in men with an elevated PSA. Online risk calculator: http://petiv.utu.fi/multiimprod/ Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1556-1567.

7.
Int J Med Inform ; 133: 104014, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31783311

RESUMO

INTRODUCTION: Predictive survival modeling offers systematic tools for clinical decision-making and individualized tailoring of treatment strategies to improve patient outcomes while reducing overall healthcare costs. In 2015, a number of machine learning and statistical models were benchmarked in the DREAM 9.5 Prostate Cancer Challenge, based on open clinical trial data for metastatic castration resistant prostate cancer (mCRPC). However, applying these models into clinical practice poses a practical challenge due to the inclusion of a large number of model variables, some of which are not routinely monitored or are expensive to measure. OBJECTIVES: To develop cost-specified variable selection algorithms for constructing cost-effective prognostic models of overall survival that still preserve sufficient model performance for clinical decision making. METHODS: Penalized Cox regression models were used for the survival prediction. For the variable selection, we implemented two algorithms: (i) LASSO regularization approach; and (ii) a greedy cost-specified variable selection algorithm. The models were compared in three cohorts of mCRPC patients from randomized clinical trials (RCT), as well as in a real-world cohort (RWC) of advanced prostate cancer patients treated at the Turku University Hospital. Hospital laboratory expenses were utilized as a reference for computing the costs of introducing new variables into the models. RESULTS: Compared to measuring the full set of clinical variables, economic costs could be reduced by half without a significant loss of model performance. The greedy algorithm outperformed the LASSO-based variable selection with the lowest tested budgets. The overall top performance was higher with the LASSO algorithm. CONCLUSION: The cost-specified variable selection offers significant budget optimization capability for the real-world survival prediction without compromising the predictive power of the model.


Assuntos
Neoplasias da Próstata/economia , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Tomada de Decisão Clínica , Análise Custo-Benefício , Hospitais , Humanos , Masculino , Prognóstico , Neoplasias da Próstata/diagnóstico , Sistema de Registros
8.
Eur Urol Oncol ; 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31501082

RESUMO

BACKGROUND: Biparametric magnetic resonance imaging (bpMRI) combined with prostate-specific antigen density (PSAd) may be an effective strategy for selecting men for prostate biopsy. It has been shown that performing biopsy only for men with bpMRI Likert scores of 4-5 or PSAd ≥0.15 ng/ml/cm3 is the most efficient strategy. OBJECTIVE: To externally validate previously published biopsy strategies using two prospective bpMRI trial cohorts. DESIGN, SETTING, AND PARTICIPANTS: After IMPROD bpMRI, 499 men had systematic transrectal prostate biopsies and men with IMPROD bpMRI Likert scores of 3-5 had an additional two to four targeted biopsies. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Various IMPROD bpMRI Likert score and PSAd thresholds were assessed using detection rates for significant prostate cancer (sPCa; Gleason score ≥3 + 4), predictive values, and proportion of biopsies avoided. Net benefits and decision curve analyses (DCA) were compared with the aim of finding an optimal strategy for sPCa detection. Combined biopsies were used for reference. RESULTS AND LIMITATIONS: The negative predictive value (NPV) for sPCa in IMPROD bpMRI Likert 3-5 and 4-5 score groups was 93% and 92%, respectively, while the corresponding positive predictive value (PPV) was 57% and 72%, respectively. In DCA, the optimal combination was IMPROD bpMRI Likert score 4-5 or Likert 3 with PSAd ≥0.20 ng/ml/cm3, which had NPV of 93% and PPV of 67%. Using this combination, 35% of the study patients would have avoided biopsies and 13 sPCas (6%, 13/229, of all sPCas diagnosed) would have been missed. CONCLUSIONS: IMPROD bpMRI demonstrated a good NPV for sPCa. PSAd improved the NPV mainly among men with equivocal suspicion on IMPROD bpMRI. However, the additional value of PSAd was marginal: the NPV and PPV for IMPROD bpMRI Likert 4-5 score group were 92% and 72%, respectively, while the corresponding values for the best combination strategy were 93% and 67%. PATIENT SUMMARY: We investigated a rapid prostate magnetic resonance imaging protocol (IMPROD bpMRI) combined with prostate-specific antigen (PSA) density for detection of significant prostate cancer. Our results show that IMPROD bpMRI is a good diagnostic tool, but the additional value provided by PSA density is marginal.

9.
PLoS One ; 14(7): e0217702, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31283771

RESUMO

PURPOSE: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2). METHODS: T2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS. RESULTS: In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82-0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments. CONCLUSION: Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.


Assuntos
Interpretação de Imagem Assistida por Computador , Aprendizado de Máquina , Imageamento por Ressonância Magnética Multiparamétrica , Neoplasias da Próstata/diagnóstico por imagem , Idoso , Humanos , Masculino , Pessoa de Meia-Idade
10.
J Magn Reson Imaging ; 50(5): 1641-1650, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-30903647

RESUMO

BACKGROUND: Prostate MRI is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). However, development and validation of methods for focal therapy planning are still lagging. PURPOSE: To evaluate the diagnostic accuracy on lesion, region-of-interest (ROI), and voxel level of IMPROD biparametric prostate MRI (bpMRI) for PCa detection in men with a clinical suspicion of PCa who subsequently underwent radical prostatectomy. STUDY TYPE: Prospective single-institution clinical trial (NCT01864135). POPULATION: Sixty-four men who underwent radical prostatectomy after IMPROD bpMRI performed in prebiopsy settings. FIELD STRENGTH/SEQUENCE: IMPROD bpMRI consisted of T2 -weighted imaging (T2 w) and three separate diffusion-weighted imaging acquisitions with an average acquisition time of 15 minutes. ASSESSMENT: The diagnostic accuracy of prospectively reported manual cancer delineations and regions increased with 3D dilation were evaluated on the voxel level (volume of 1.17 mm3 , 1 mm3 , 125 mm3 ) as well as the 36 ROI level. Only PCa lesions with a diameter ≥ 5 mm or any Gleason Grade 4 were analyzed. All data and protocols are freely available at: http://petiv.utu.fi/improd STATISTICAL TESTS: Sensitivity, specificity, accuracy. RESULTS: In total, 99 PCa lesions were identified. Forty (40%, 40/99) had a Gleason score (GS) of >3 + 4. Twenty-eight PCa lesions (28%, 28/99) were missed by IMPROD bpMRI, three (7.5%, 3/40) with GS >3 + 4. 3D dilation of manual cancer delineations in all directions by ~10-12 mm (corresponding to the Hausdorff distance) was needed to achieve sensitivity approaching 100% on a voxel level. DATA CONCLUSION: IMPROD bpMRI had a high sensitivity on lesion level for PCa with GS >3 + 4. Increasing 3D lesion delineations by ~10-12 mm (corresponding to the Hausdorff distance) was needed to achieve high sensitivity on the voxel level. Such information may help in planning ablation therapies. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1641-1650.

11.
Stat Methods Med Res ; 28(10-11): 2975-2991, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30126322

RESUMO

Receiver operating characteristic analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under receiver operating characteristic curve with standard cross-validation methods suffers from a large bias. The leave-pair-out cross-validation has been shown to correct this bias. However, while leave-pair-out produces an almost unbiased estimate of area under receiver operating characteristic curve, it does not provide a ranking of the data needed for plotting and analyzing the receiver operating characteristic curve. In this study, we propose a new method called tournament leave-pair-out cross-validation. This method extends leave-pair-out by creating a tournament from pair comparisons to produce a ranking for the data. Tournament leave-pair-out preserves the advantage of leave-pair-out for estimating area under receiver operating characteristic curve, while it also allows performing receiver operating characteristic analyses. We have shown using both synthetic and real-world data that tournament leave-pair-out is as reliable as leave-pair-out for area under receiver operating characteristic curve estimation and confirmed the bias in leave-one-out cross-validation on low-dimensional data. As a case study on receiver operating characteristic analysis, we also evaluate how reliably sensitivity and specificity can be estimated from tournament leave-pair-out receiver operating characteristic curves.

12.
Conf Proc IEEE Eng Med Biol Soc ; 2019: 3482-3485, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31946628

RESUMO

Patient self-reporting of pain is not always possible, in those cases automated objective pain assessment could lead to reliable pain assessment. In this context, physiological measurements have been studied and one of the promising signals is skin conductance (SC). In this study, 1Hz SC signal acquisition is performed while gradually increasing heat and electrical pain stimuli are induced. Three labeled study periods are defined based on pain stimuli presence, self-reported pain threshold and pain tolerance. Different classification and regression models are compared, together with selected SC features. The model performances are evaluated using c-index. Results show good predictability, especially for the slow tonic component decomposed from the SC signal.


Assuntos
Resposta Galvânica da Pele , Medição da Dor/métodos , Limiar da Dor , Dor , Humanos , Pele
13.
J Am Heart Assoc ; 7(20): e010378, 2018 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-30371266

RESUMO

Background The molecular mechanisms mediating postnatal loss of cardiac regeneration in mammals are not fully understood. We aimed to provide an integrated resource of mRNA , protein, and metabolite changes in the neonatal heart for identification of metabolism-related mechanisms associated with cardiac regeneration. Methods and Results Mouse ventricular tissue samples taken on postnatal day 1 (P01), P04, P09, and P23 were analyzed with RNA sequencing and global proteomics and metabolomics. Gene ontology analysis, KEGG pathway analysis, and fuzzy c-means clustering were used to identify up- or downregulated biological processes and metabolic pathways on all 3 levels, and Ingenuity pathway analysis (Qiagen) was used to identify upstream regulators. Differential expression was observed for 8547 mRNA s and for 1199 of 2285 quantified proteins. Furthermore, 151 metabolites with significant changes were identified. Differentially regulated metabolic pathways include branched chain amino acid degradation (upregulated at P23), fatty acid metabolism (upregulated at P04 and P09; downregulated at P23) as well as the HMGCS ( HMG -CoA [hydroxymethylglutaryl-coenzyme A] synthase)-mediated mevalonate pathway and ketogenesis (transiently activated). Pharmacological inhibition of HMGCS in primary neonatal cardiomyocytes reduced the percentage of BrdU-positive cardiomyocytes, providing evidence that the mevalonate and ketogenesis routes may participate in regulating the cardiomyocyte cell cycle. Conclusions This study is the first systems-level resource combining data from genomewide transcriptomics with global quantitative proteomics and untargeted metabolomics analyses in the mouse heart throughout the early postnatal period. These integrated data of molecular changes associated with the loss of cardiac regeneration may open up new possibilities for the development of regenerative therapies.


Assuntos
Coração/crescimento & desenvolvimento , Camundongos/crescimento & desenvolvimento , Aminoácidos de Cadeia Ramificada/metabolismo , Animais , Animais Recém-Nascidos/crescimento & desenvolvimento , Ácidos Graxos/metabolismo , Expressão Gênica/fisiologia , Coração/embriologia , Ventrículos do Coração , Corpos Cetônicos/biossíntese , Metabolômica , Ácido Mevalônico/metabolismo , Proteômica , RNA Mensageiro/genética , RNA Mensageiro/fisiologia , Transcriptoma/fisiologia
14.
Brief Bioinform ; 2018 Oct 16.
Artigo em Inglês | MEDLINE | ID: mdl-30329015

RESUMO

Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models. The machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package: https://github.com/aatapa/RLScore.

15.
Neural Comput ; 30(8): 2245-2283, 2018 08.
Artigo em Inglês | MEDLINE | ID: mdl-29894652

RESUMO

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

16.
Bioinformatics ; 34(13): i509-i518, 2018 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-29949975

RESUMO

Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Antineoplásicos/farmacologia , Biologia Computacional/métodos , Descoberta de Drogas/métodos , Neoplasias/tratamento farmacológico , Máquina de Vetores de Suporte , Antineoplásicos/uso terapêutico , Linhagem Celular Tumoral , Humanos , Neoplasias/enzimologia , Neoplasias/metabolismo , Proteínas Quinases/efeitos dos fármacos , Proteínas Quinases/metabolismo , Transdução de Sinais , Software , Resultado do Tratamento
17.
Comput Biol Med ; 98: 1-7, 2018 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-29758452

RESUMO

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population.


Assuntos
Algoritmos , Síndrome de Down/diagnóstico , Aprendizado de Máquina , Diagnóstico Pré-Natal/métodos , Adulto , Síndrome de Down/epidemiologia , Feminino , Humanos , Modelos Estatísticos , Redes Neurais de Computação , Gravidez , Curva ROC , Medição de Risco , Máquina de Vetores de Suporte
18.
Anal Chem ; 90(7): 4832-4839, 2018 04 03.
Artigo em Inglês | MEDLINE | ID: mdl-29513001

RESUMO

Raman spectroscopy is widely used for quantitative pharmaceutical analysis, but a common obstacle to its use is sample fluorescence masking the Raman signal. Time-gating provides an instrument-based method for rejecting fluorescence through temporal resolution of the spectral signal and allows Raman spectra of fluorescent materials to be obtained. An additional practical advantage is that analysis is possible in ambient lighting. This study assesses the efficacy of time-gated Raman spectroscopy for the quantitative measurement of fluorescent pharmaceuticals. Time-gated Raman spectroscopy with a 128 × (2) × 4 CMOS SPAD detector was applied for quantitative analysis of ternary mixtures of solid-state forms of the model drug, piroxicam (PRX). Partial least-squares (PLS) regression allowed quantification, with Raman-active time domain selection (based on visual inspection) improving performance. Model performance was further improved by using kernel-based regularized least-squares (RLS) regression with greedy feature selection in which the data use in both the Raman shift and time dimensions was statistically optimized. Overall, time-gated Raman spectroscopy, especially with optimized data analysis in both the spectral and time dimensions, shows potential for sensitive and relatively routine quantitative analysis of photoluminescent pharmaceuticals during drug development and manufacturing.


Assuntos
Corantes Fluorescentes/análise , Preparações Farmacêuticas/análise , Análise dos Mínimos Quadrados , Análise Espectral Raman , Fatores de Tempo
19.
IEEE Trans Neural Netw Learn Syst ; 29(8): 3374-3387, 2018 08.
Artigo em Inglês | MEDLINE | ID: mdl-28783645

RESUMO

Kronecker product kernel provides the standard approach in the kernel methods' literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The methods allow generalization to such new edges, whose start and end vertices do not appear in the training data, a setting known as zero-shot or zero-data learning. Such a setting occurs in numerous applications, including drug-target interaction prediction, collaborative filtering, and information retrieval. Efficient training algorithms based on the so-called vec trick that makes use of the special structure of the Kronecker product are known for the case where the training data are a complete bipartite graph. In this paper, we generalize these results to noncomplete training graphs. This allows us to derive a general framework for training Kronecker product kernel methods, as specific examples we implement Kronecker ridge regression and support vector machine algorithms. Experimental results demonstrate that the proposed approach leads to accurate models, while allowing order of magnitude improvements in training and prediction time.

20.
Cell Syst ; 5(5): 485-497.e3, 2017 11 22.
Artigo em Inglês | MEDLINE | ID: mdl-28988802

RESUMO

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.


Assuntos
Expressão Gênica/genética , Genes Essenciais/genética , Algoritmos , Linhagem Celular Tumoral , Genômica/métodos , Humanos , RNA Interferente Pequeno/genética
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