Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.

Drug-target interactions (DTIs) prediction algorithms are used at various stages of the drug discovery process. In this context, specific problems such as deorphanization of a new therapeutic target or target identification of a drug candidate arising from phenotypic screens require large-scale predictions across the protein and molecule spaces. DTI prediction heavily relies on supervised learning algorithms that use known DTIs to learn associations between molecule and protein features, allowing for the prediction of new interactions based on learned patterns.

Nutritional contributions and processability of pasta made from climate-smart, sustainable crops: A critical review.

Total or partial replacement of traditional durum wheat semolina (DWS) by alternative flours, such as legumes or wholegrain cereals in pasta improves their nutritional quality and can make them interesting vector for fortification. Climate-smart gluten-free (C-GF) flours, such as legumes (bambara groundnut, chickpea, cowpea, faba bean, and pigeon pea), some cereals (amaranth, teff, millet, and sorghum), and tubers (cassava and orange fleshed sweet potato), are of high interest to face ecological transition and develop sustainable food systems. In this review, an overview and a critical analysis of their nutritional potential for pasta production and processing conditions are undertaken.

Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance.

Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping.

Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

BACKGROUND: Risk stratification and therapeutic decision-making for myelodysplastic syndromes (MDS) are based on the International Prognostic Scoring System–Revised (IPSS-R), which considers hematologic parameters and cytogenetic abnormalities. Somatic gene mutations are not yet used in the risk stratification of patients with MDS. METHODS: To develop a clinical-molecular prognostic model (IPSS-Molecular [IPSS-M]), pretreatment diagnostic or peridiagnostic samples from 2957 patients with MDS were profiled for mutations in 152 genes.

Subject-specific segregation of functional territories based on deep phenotyping.

Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data-collection efforts have started to target a systematic spatial representation of multiple mental functions.

The functional database of the ARCHI project: Potential and perspectives.

More than two decades of functional magnetic resonance imaging (fMRI) of the human brain have succeeded to identify, with a growing level of precision, the neural basis of multiple cognitive skills within various domains (perception, sensorimotor processes, language, emotion and social cognition …). Progress has been made in the comprehension of the functional organization of localized brain areas. However, the long time required for fMRI acquisition limits the number of experimental conditions performed in a single individual.

Shared genetic aetiology between cognitive performance and brain activations in language and math tasks.

Cognitive performance is highly heritable. However, little is known about common genetic influences on cognitive ability and brain activation when engaged in a cognitive task. The Human Connectome Project (HCP) offers a unique opportunity to study this shared genetic etiology with an extended pedigree of 785 individuals.

Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping.

Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain.

The Brainomics/Localizer database.

The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects.

Linking morphological and functional variability in hand movement and silent reading.

It is generally accepted in neuroscience that anatomy and function go hand in hand. Accordingly, a local morphological variability could lead to a corresponding functional variability. In this study, we tested this hypothesis by linking the variability of the cortical folding pattern of 252 right-handed subjects to the localization or the pattern of functional activations induced by hand motion or silent reading.

Principal component regression predicts functional responses across individuals.

Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability.

Anatomical connections of the visual word form area.

The visual word form area (VWFA), a region systematically involved in the identification of written words, occupies a reproducible location in the left occipitotemporal sulcus in expert readers of all cultures. Such a reproducible localization is paradoxical, given that reading is a recent invention that could not have influenced the genetic evolution of the cortex. Here, we test the hypothesis that the VWFA recycles a region of the ventral visual cortex that shows a high degree of anatomical connectivity to perisylvian language areas, thus providing an efficient circuit for both grapheme-phoneme conversion and lexical access.

Genetic and Environmental Influences on the Visual Word Form and Fusiform Face Areas.

Two areas of the occipitotemporal cortex show a remarkable hemispheric lateralization: written words activate the visual word form area (VWFA) in the left fusiform gyrus and faces activate a symmetrical site in the right hemisphere, the fusiform face area (FFA). While the lateralization of the VWFA fits with the leftward asymmetry of the speech processing network, origin of the rightward asymmetry for faces is still unclear. Using fMRI data from 64 subjects (including 16 monozygotic (MZ) and 13 dizygotic (DZ) twin pairs), we investigated how activations evoked by written words, faces, and spoken language are co-lateralized in the temporal lobe, and whether this organization reflects genetic factors or individual reading expertise.

Spatially invariant coding of numerical information in functionally defined subregions of human parietal cortex.

Macaque electrophysiology has revealed neurons responsive to number in lateral (LIP) and ventral (VIP) intraparietal areas. Recently, fMRI pattern recognition revealed information discriminative of individual numbers in human parietal cortex but without precisely localizing the relevant sites or testing for subregions with different response profiles. Here, we defined the human functional equivalents of LIP (feLIP) and VIP (feVIP) using neurophysiologically motivated localizers.

Genetic and environmental contributions to brain activation during calculation.

Twin studies have long suggested a genetic influence on inter-individual variations in mathematical abilities, and candidate genes have been identified by genome-wide association studies. However, the localization of the brain regions under genetic influence during number manipulation is still unexplored. Here we investigated fMRI data from a group of 19 MZ (monozygotic) and 13 DZ (dizygotic) adult twin pairs, scanned during a mental calculation task.

Improving accuracy and power with transfer learning using a meta-analytic database.

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects.

Cohort-level brain mapping: learning cognitive atoms to single out specialized regions.

Functional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population.

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares.

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions.

Genetic variants of FOXP2 and KIAA0319/TTRAP/THEM2 locus are associated with altered brain activation in distinct language-related regions.

Recent advances have been made in the genetics of two human communication skills: speaking and reading. Mutations of the FOXP2 gene cause a severe form of language impairment and orofacial dyspraxia, while single-nucleotide polymorphisms (SNPs) located within a KIAA0319/TTRAP/THEM2 gene cluster and affecting the KIAA0319 gene expression are associated with reading disability. Neuroimaging studies of clinical populations point to partially distinct cerebral bases for language and reading impairments.

Cortical representations of symbols, objects, and faces are pruned back during early childhood.

Regions of human ventral extrastriate visual cortex develop specializations for natural categories (e.g., faces) and cultural artifacts (e.

A group model for stable multi-subject ICA on fMRI datasets.

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons.

A disconnection account of Gerstmann syndrome: functional neuroanatomy evidence.

Objective: To examine the functional neuroanatomy that could account for pure Gerstmann syndrome, which is the selective association of acalculia, finger agnosia, left-right disorientation, and agraphia.

Methods: We used structural and functional neuroimaging at high spatial resolution in healthy subjects to seek a shared cortical substrate of the Grundstörung posited by Gerstmann, ie, a common functional denominator accounting for this clinical tetrad. We construed a functional activation paradigm that mirrors each of the four clinical deficits in Gerstmann syndrome and determined cortical activation patterns.

The enigma of Gerstmann's syndrome revisited: a telling tale of the vicissitudes of neuropsychology.

Eighty years ago, the Austrian neurologist Josef Gerstmann observed in a few patients a concomitant impairment in discriminating their own fingers, writing by hand, distinguishing left from right and performing calculations. He claimed that this tetrad of symptoms constituted a syndromal entity, assigned it to a lesion of the dominant parietal lobe and suggested that it was due to damage of a common functional denominator. Ever since, these claims have been debated and an astute synopsis and sceptical discussion was presented 40 years ago by MacDonald Critchley in this journal.

Beyond hemispheric dominance: brain regions underlying the joint lateralization of language and arithmetic to the left hemisphere.

Language and arithmetic are both lateralized to the left hemisphere in the majority of right-handed adults. Yet, does this similar lateralization reflect a single overall constraint of brain organization, such an overall "dominance" of the left hemisphere for all linguistic and symbolic operations? Is it related to the lateralization of specific cerebral subregions? Or is it merely coincidental? To shed light on this issue, we performed a "colateralization analysis" over 209 healthy subjects: We investigated whether normal variations in the degree of left hemispheric asymmetry in areas involved in sentence listening and reading are mirrored in the asymmetry of areas involved in mental arithmetic. Within the language network, a region-of-interest analysis disclosed partially dissociated patterns of lateralization, inconsistent with an overall "dominance" model.